Patent Application
20240144676
This disclosure relates generally to Visual Question Answering (VQA), and, more particularly, to methods, systems, articles of manufacture and apparatus for providing responses to queries regarding store observation images.
Auditors visit retail locations to perform store observations and/or data collection used to identify strengths and weaknesses of store layouts, product placements, etc. These auditors often have recurring visual questions related to product information, store shelf layout, etc. that require answers on-site. Visual Question Answering (VQA) combines Computer Vision (CV), Natural Language Processing (NLP), and/or Knowledge Representation & Reasoning (KR&R) techniques to provide natural language responses to questions asked by users regarding images.
The figures are not to scale. Instead, the thickness of the layers or regions may be enlarged in the drawings. In general, the same reference numbers will be used throughout the drawing(s) and accompanying written description to refer to the same or like parts.
Descriptors “first,” “second,” “third,” etc. are used herein when identifying multiple elements or components which may be referred to separately. Unless otherwise specified or understood based on their context of use, such descriptors are not intended to impute any meaning of priority, physical order or arrangement in a list, or ordering in time but are merely used as labels for referring to multiple elements or components separately for ease of understanding the disclosed examples. In some examples, the descriptor “first” may be used to refer to an element in the detailed description, while the same element may be referred to in a claim with a different descriptor such as “second” or “third.” In such instances, it should be understood that such descriptors are used merely for ease of referencing multiple elements or components.
Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and/or other artificial machine-driven logic, enables machines (e.g., computers, logic circuits, etc.) to use a model to process input data to generate an output based on patterns and/or associations previously learned by the model via a training process. For instance, the model may be trained with data to recognize patterns and/or associations and follow such patterns and/or associations when processing input data such that other input(s) result in output(s) consistent with the recognized patterns and/or associations.
Many different types of machine learning models and/or machine learning architectures exist. In some examples disclosed herein, a Convolutional Neural Network (CNN) model is used. Using a CNN model enables weight sharing (e.g., reducing the number of weights that must be learned by the model), which reduces model training time and computation cost. In general, machine learning models/architectures that are suitable to use in the example approaches disclosed herein will be Neural Networks (NN), Deep Neural Networks (DNN), and/or Recurrent Neural Networks (RNN). However, other types of machine learning models could additionally or alternatively be used such as Support Vector Machines (SVM), Long Term Short Memory (LSTM), Gated Recurrent Units (GRU), etc.
In general, implementing an ML/AI system involves two phases, a learning/training phase and an inference phase. In the learning/training phase, a training algorithm is used to train a model to operate in accordance with patterns and/or associations based on, for example, training data. In general, the model includes internal parameters that guide how input data is transformed into output data, such as through a series of nodes and connections within the model to transform input data into output data. Additionally, hyperparameters are used as part of the training process to control how the learning is performed (e.g., a learning rate, a number of layers to be used in the machine learning model, etc.). Hyperparameters are defined to be training parameters that are determined prior to initiating the training process.
Different types of training may be performed based on the type of ML/AI model and/or the expected output. For example, supervised training uses inputs and corresponding expected (e.g., labeled) outputs to select parameters (e.g., by iterating over combinations of select parameters) for the ML/AI model that reduce model error. As used herein, labeling refers to an expected output of the machine learning model (e.g., a classification, an expected output value, etc.) Alternatively, unsupervised training (e.g., used in deep learning, a subset of machine learning, etc.) involves inferring patterns from inputs to select parameters for the ML/AI model (e.g., without the benefit of expected (e.g., labeled) outputs).
In examples disclosed herein, ML/AI models are trained using stochastic gradient descent. However, any other training algorithm may additionally or alternatively be used. In examples disclosed herein, training is performed until an acceptable amount of error has been reached. In examples disclosed herein, training may be performed at an electronic system (e.g., on one or more ML model(s)). Training is performed using hyperparameters that control how the learning is performed (e.g., a learning rate, a number of layers to be used in the machine learning model, etc.). In examples disclosed herein, hyperparameters that control a dictionary of values (e.g., for word embeddings) are used. Such hyperparameters are selected by, for example, manually and/or using statistical (random) sampling. In some examples, re-training may be performed. Such re-training may be performed in response to an accuracy metric not satisfying a threshold value.
Training is performed using training data. In examples disclosed herein, the training data may originate from a datastore (e.g., an example datastore 292 explained further in conjunction with
Once training is complete, the model is deployed for use as an executable construct that processes an input and provides an output based on the network of nodes and connections defined in the model. The model is stored at a datastore. The model may then be executed by example model execution circuitry 280 (explained further in conjunction with
Once trained, the deployed model may be operated in an inference phase to process data. In the inference phase, data to be analyzed (e.g., live data) is input to the model, and the model executes to create an output. This inference phase can be thought of as the AI “thinking” to generate the output based on what it learned from the training (e.g., by executing the model to apply the learned patterns and/or associations to the live data). In some examples, input data undergoes pre-processing before being used as an input to the machine learning model. Moreover, in some examples, the output data may undergo post-processing after it is generated by the AI model to transform the output into a useful result (e.g., a display of data, an instruction to be executed by a machine, etc.).
In some examples, output of the deployed model may be captured and provided as feedback. By analyzing the feedback, an accuracy of the deployed model can be determined. If the feedback indicates that the accuracy of the deployed model is less than a threshold or other criterion, training of an updated model can be triggered using the feedback and an updated training data set, hyperparameters, etc., to generate an updated, deployed model.
Large numbers of field and/or store auditors frequently (e.g., daily) visit retail locations to collect data (e.g., product data, sales data, etc.) and/or perform analysis on product placement, store layout, etc. to improve store performance (e.g., by improving sales performance of products of interest in a given store). These store auditors often have recurrent doubts regarding specific products observed on store shelves, with a frequency of these doubts exacerbated by cultural gaps and/or regional differences in specific products and/or brands. For example, a field auditor and/or store auditor may visit a store in a region different from one they are used to. Subsequently, as a result of regional differences, languages barriers, etc., the field auditor may, for example, not recognize a particular product or set of similar products on a store shelf, thus halting and/or otherwise delaying the field auditor's ability to continue collecting data relating to that particular shelf, surrounding shelves, etc. Therefore, efficient provision of answers (e.g., determination of answers) to field auditors' questions regarding particular observations on store shelves (e.g., “what type of products are in this image?”, “are there bananas on this shelf?”, “are there any wine bottles present?”, “what does this product do?”, etc.) promote reduction in waste of resources and/or time. A higher accuracy of answers is also desired because a field auditor's understanding of store shelves and/or products directly influences their data collection and/or analysis processes. Therefore, a mistaken belief in a particular store observation has undesirable implications in accuracy of overall store analysis, product category analysis, etc. Stated differently, a reliance on current techniques that consider human discretion cause wasted time and/or erroneous distribution instructions (e.g., excess product is delivered to a particular store because of auditor undercount errors, insufficient product is delivered to a particular store because of auditor overcount errors, etc.).
Current approaches to providing answers to questions regarding store observations involve human operators (e.g., working at a helpdesk, call center, etc.) who review an input image and/or question and output an answer to the query. These approaches introduce a high latency between asking of a question and receipt of an answer to the question, since a human must individually review each question and provide an answer. In a fast-paced audit and/or data collection situation, this massively decreases an efficiency of the field auditors and, thus, wastes resources and/or time. Additionally, involvement of human discretion in answering these store observation questions further involves a high measure of inaccuracy in the provided answers, due to mistakes caused by guesswork, blurry images, worker fatigue, language barriers, regional differences, etc.
Additional approaches to providing answers to questions regarding store observations use Visual Question Answering (VQA) techniques that involve a limited dataset and a resulting inability to accurately generalize answers over a wide range of products and/or retail locations. These approaches similarly introduce a high measure of inaccuracy, as a limited dataset results in inaccurate answers to questions regarding store observations and/or observation images. Furthermore, the extensive software retraining and/or testing employed and/or required by these approaches (e.g., due to a high loss metric not satisfying a threshold value) often produces unsatisfactory, delayed, and/or unclear recommendations (e.g., particularly during the inference phase of the ML/AI model). Furthermore, the frequent repetition of testing and/or training of an ML/AI model with a limited dataset required to ensure optimum model results (e.g., results that are consistent with ground-truth data testing) is resource-intensive, computationally-expensive, and/or challenging, particularly in instances where test datasets and/or training datasets are large in volume and/or are frequently-evolving. That is, the software testing required to ensure model results fall within an acceptable range of accuracy and/or loss may cause validation cycles to become prolonged. Additionally, the current approaches described herein may only be applicable in a limited number of situations due to a foreseeable and/or observed risk of an incorrect cutoff decision being made, particularly when the triage performed by a model employing a limited dataset produces an incorrect confidence score. In short, the current approaches frequently over-complicate the process of deploying ML/AI models by complicating the training phase and/or the post-training testing/inference phase.
Example methods for efficiently providing accurate answers to questions regarding store observations focus on a broadening of a dictionary and/or dataset involved in training of ML/AI model(s), as well as generation of question-answer pairs to promote wider generalizability. Such examples reduce the amount of misinformation spread through inaccurate answers provided using human discretion and/or ML/AI models trained using a limited dataset, and additionally reduces computational expense and/or resources. That is, in example methods disclosed herein, an accurate and/or widely generalizable dataset is synthetically generated (e.g., through dictionary updating, generation of question-answer pairs, etc.), using machine-learning (ML) and/or artificial intelligence (AI) techniques to train a more adaptable ML/AI model or a set of ML/AI models.
The electronic system 102 of the illustrated example of
In some examples, the electronic system 102 is a system on a chip (SoC) representative of one or more integrated circuits (ICs) (e.g., compact ICs) that incorporate components of a computer or other electronic system in a compact format. For example, the electronic system 102 may be implemented with a combination of one or more programmable processors, hardware logic, and/or hardware peripherals and/or interfaces. Additionally or alternatively, the example electronic system 102 of
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The general purpose processing circuitry 112 of the example of
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The electronic system 102 includes the power source 118 to deliver power to hardware of the electronic system 102. In some examples, the power source 118 may implement a power delivery network. For example, the power source 118 may implement an alternating current-to-direct current (AC/DC) power supply. In some examples, the power source 118 may be coupled to a power grid infrastructure such as an AC main (e.g., a 110 volt (V) AC grid main, a 220V AC grid main, etc.). Additionally, or alternatively, the power source 118 may be implemented by a battery. For example, the power source 118 may be a limited energy device, such as a lithium-ion battery or any other chargeable battery or power source. In some such examples, the power source 118 may be chargeable using a power adapter or converter (e.g., an AC/DC power converter), a wall outlet (e.g., a 110 V AC wall outlet, a 220 V AC wall outlet, etc.), a portable energy storage device (e.g., a portable power bank, a portable power cell, etc.), etc.
The electronic system 102 of the illustrated example of
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In some examples, one or more of the external electronic systems 130 execute one(s) of the ML model(s) 124 to process a computing workload (e.g., an AI/ML workload). For example, the mobile device 134 can be implemented as a cell or mobile phone having one or more processors (e.g., a CPU, a GPU, a VPU, an AI or NN specific processor, etc.) on a single SoC to process an AI/ML workload using one(s) of the ML model(s) 124. In some examples, the desktop computer 132, the laptop computer 136, the tablet computer, and/or the server 140 may be implemented as electronic (e.g., computing) device(s) having one or more processors (e.g., a CPU, a GPU, a VPU, an AI/NN specific processor, etc.) on one or more SoCs to process AI/ML workload(s) using one(s) of the ML model(s) 124. In some examples, the server 140 may implement one or more servers (e.g., physical servers, virtualized servers, etc., and/or a combination thereof) that may implement a data facility, a cloud service (e.g., a public or private cloud provider, a cloud-based repository, etc.), etc., to process AI/ML workload(s) using one(s) of the ML model(s) 124.
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In some examples, one or more of the first accelerator compiler 104A, the second accelerator compiler 104B, the third accelerator compiler 104C, and/or portion(s) thereof, may be virtualized, such as by being implemented with one or more containers, one or more virtual resources (e.g., virtualizations of compute, memory, networking, storage, etc., physical hardware resources), one or more virtual machines, etc. In some examples, one or more of the first accelerator compiler 104A, the second accelerator compiler 104B, the third accelerator compiler 104C, and/or portion(s) thereof, may be implemented by different resource(s) of the electronic system 102. Alternatively, the electronic system 102 may not include one or more of the first accelerator compiler 104A, the second accelerator compiler 104B, and/or the third accelerator compiler 104C.
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Many different types of machine-learning models and/or machine-learning architectures exist. In some examples, the accelerator compiler 104A-C generates the machine-learning model(s) 124 as neural network model(s). The accelerator compiler 104A-C may invoke the interface circuitry 114 to transmit the machine-learning model(s) 124 to one(s) of the external electronic systems 130. Using a neural network model enables the acceleration circuitry 108, 110 to execute an AI/ML workload. In general, machine-learning models/architectures that are suitable to use in the example approaches disclosed herein include recurrent neural networks. However, other types of machine learning models could additionally or alternatively be used such as supervised learning ANN models, clustering models, classification models, etc., and/or a combination thereof. Example supervised learning ANN models may include two-layer (2-layer) radial basis neural networks (RBN), learning vector quantization (LVQ) classification neural networks, etc. Example clustering models may include k-means clustering, hierarchical clustering, mean shift clustering, density-based clustering, etc. Example classification models may include logistic regression, support-vector machine (SVM) or network, Naive Bayes, etc. In some examples, the accelerator compiler 104A-C may compile and/or otherwise generate one(s) of the machine-learning model(s) 124 as lightweight machine-learning models.
In general, implementing an ML/AI system involves two phases, a learning/training phase and an inference phase. In the learning/training phase, a training algorithm is used to train the machine-learning model(s) 124 to operate in accordance with patterns and/or associations based on, for example, training data. In general, the machine-learning model(s) 124 include(s) internal parameters (e.g., the configuration data 122) that guide how input data is transformed into output data, such as through a series of nodes and connections within the machine-learning model(s) 124 to transform input data into output data. Additionally, hyperparameters (e.g., the configuration data 122) are used as part of the training process to control how the learning is performed (e.g., a learning rate, a number of layers to be used in the machine learning model, etc.). Hyperparameters are defined to be training parameters that are determined prior to initiating the training process.
Different types of training may be performed based on the type of ML/AI model and/or the expected output. For example, the accelerator compiler 104A-C may invoke supervised training to use inputs and corresponding expected (e.g., labeled) outputs to select parameters (e.g., by iterating over combinations of select parameters) for the machine-learning model(s) 124 that reduce model error. As used herein, “labeling” refers to an expected output of the machine learning model (e.g., a classification, an expected output value, etc.). Alternatively, the accelerator compiler 104A-C may invoke unsupervised training (e.g., used in deep learning, a subset of machine learning, etc.) that involves inferring patterns from inputs to select parameters for the machine-learning model(s) 124 (e.g., without the benefit of expected (e.g., labeled) outputs).
In some examples, the accelerator compiler 104A-C trains the machine-learning model(s) 124 using unsupervised clustering of operating observables. However, the accelerator compiler 104A-C may additionally or alternatively use any other training algorithm such as stochastic gradient descent, Simulated Annealing, Particle Swarm Optimization, Evolution Algorithms, Genetic Algorithms, Nonlinear Conjugate Gradient, etc.
In some examples, the accelerator compiler 104A-C may train the machine-learning model(s) 124 until the level of error is no longer reducing. In some examples, the accelerator compiler 104A-C may train the machine-learning model(s) 124 locally on the electronic system 102 and/or remotely at an external electronic system (e.g., one(s) of the external electronic systems 130) communicatively coupled to the electronic system 102. In some examples, the accelerator compiler 104A-C trains the machine-learning model(s) 124 using hyperparameters that control how the learning is performed (e.g., a learning rate, a number of layers to be used in the machine learning model, etc.). In some examples, the accelerator compiler 104A-C may use hyperparameters that control model performance and training speed such as the learning rate and regularization parameter(s). The accelerator compiler 104A-C may select such hyperparameters by, for example, trial and error to reach an optimal model performance. In some examples, the accelerator compiler 104A-C utilizes Bayesian hyperparameter optimization to determine an optimal and/or otherwise improved or more efficient network architecture to avoid model overfitting and improve the overall applicability of the machine-learning model(s) 124. Alternatively, the accelerator compiler 104A-C may use any other type of optimization. In some examples, the accelerator compiler 104A-C may perform re-training. The accelerator compiler 104A-C may execute such re-training in response to override(s) by a user of the electronic system 102, a receipt of new training data, etc.
In some examples, the accelerator compiler 104A-C facilitates the training of the machine-learning model(s) 124 using training data. In some examples, the accelerator compiler 104A-C utilizes training data that originates from locally generated data. In some examples, the accelerator compiler 104A-C utilizes training data that originates from externally generated data. In some examples where supervised training is used, the accelerator compiler 104A-C may label the training data. Labeling is applied to the training data by a user manually or by an automated data pre-processing system. In some examples, the accelerator compiler 104A-C may pre-process the training data using, for example, an interface (e.g., the interface circuitry 114). In some examples, the accelerator compiler 104A-C sub-divides the training data into a first portion of data for training the machine-learning model(s) 124, and a second portion of data for validating the machine-learning model(s) 124.
Once training is complete, the accelerator compiler 104A-C may deploy the machine-learning model(s) 124 for use as an executable construct that processes an input and provides an output based on the network of nodes and connections defined in the machine-learning model(s) 124. The accelerator compiler 104A-C may store the machine-learning model(s) 124 in the datastore 120. In some examples, the accelerator compiler 104A-C may invoke the interface circuitry 114 to transmit the machine-learning model(s) 124 to one(s) of the external electronic systems 130. In some such examples, in response to transmitting the machine-learning model(s) 124 to the one(s) of the external electronic systems 130, the one(s) of the external electronic systems 130 may execute the machine-learning model(s) 124 to execute AI/ML workloads with at least one of improved efficiency or performance.
Once trained, the deployed one(s) of the machine-learning model(s) 124 may be operated in an inference phase to process data. In the inference phase, data to be analyzed (e.g., live data) is input to the machine-learning model(s) 124, and the machine-learning model(s) 124 execute(s) to create an output. This inference phase can be thought of as the AI “thinking” to generate the output based on what it learned from the training (e.g., by executing the machine-learning model(s) 124 to apply the learned patterns and/or associations to the live data). In some examples, input data undergoes pre-processing before being used as an input to the machine-learning model(s) 124. Moreover, in some examples, the output data may undergo post-processing after it is generated by the machine-learning model(s) 124 to transform the output into a useful result (e.g., a display of data, a detection and/or identification of an object, an instruction to be executed by a machine, etc.).
In some examples, output of the deployed one(s) of the machine-learning model(s) 124 may be captured and provided as feedback. By analyzing the feedback, an accuracy of the deployed one(s) of the machine-learning model(s) 124 can be determined. If the feedback indicates that the accuracy of the deployed model is less than a threshold or other criterion, training of an updated model can be triggered using the feedback and an updated training data set, hyperparameters, etc., to generate an updated, deployed model.
In some examples, the accelerator compiler 104A-C configures one(s) of the acceleration circuitry 108, 110 to execute a convolution operation, such as 2-D convolution operation. For example, the acceleration circuitry 108, 110 may implement a CNN. In some examples, CNNs ingest and/or otherwise process images as tensors, which are matrices of numbers with additional dimensions. For example, a CNN can obtain an input image represented by 3-D tensors, where a first and a second dimension correspond to a width and a height of a matrix and a third dimension corresponds to a depth of the matrix. For example, the width and the height of the matrix can correspond to a width and a height of an input image and the depth of the matrix can correspond to a color depth (e.g., a color layer) or a color encoding of the image (e.g., a Red-Green-Blue (RGB) encoding).
A typical CNN may also receive an input and transform the input through a series of hidden layers. For example, a CNN may have a plurality of convolution layers, pooling layers, and/or fully-connected layers. In some such examples, a CNN may have a plurality of layer triplets including a convolution layer, a pooling layer, and a fully-connected layer. In some examples, a CNN may have a plurality of convolution and pooling layer pairs that output to one or more fully-connected layers. In some examples, a CNN may include 20 layers, 30 layers, etc.
In some examples, the acceleration circuitry 108, 110 may execute a convolution layer to apply a convolution function or operation to map images of an input (previous) layer to the next layer in a CNN. In some examples, the convolution may be three-dimensional (3-D) because each input layer can have multiple input features (e.g., input channels) associated with an input image. The acceleration circuitry 108, 110 may execute the convolution layer to perform convolution by forming a regional filter window in each individual input channel and generating output data or activations by calculating a product of (1) a filter weight associated with the regional filter window and (2) the input data covered by the regional filter window. For example, the acceleration circuitry 108, 110 may determine an output feature of an input image by using the convolution filter to scan a plurality of input channels including a plurality of the regional filter windows.
In some examples, the acceleration circuitry 108, 110 may execute a pooling layer to extract information from a set of activations in each output channel. The pooling layer may perform a maximum pooling operation corresponding to a maximum pooling layer or an average pooling operation corresponding to an average pooling layer. In some examples, the maximum pooling operation may include selecting a maximum value of activations within a pooling window. In some examples, the average pooling operation may include calculating an average value of the activations within the pooling window.
In some examples, the acceleration circuitry 108, 110 may execute a fully-connected layer to obtain the data calculated by the convolution layer(s) and/or the pooling layer(s) and/or classify the data into one or more classes. In some examples, the fully-connected layer may determine whether the classified data corresponds to a particular image feature of the input image. For example, the acceleration circuitry 108, 110 may execute the fully-connected layer to determine whether the classified data corresponds to a simple image feature (e.g., a horizontal line) or a more complex image feature like an animal (e.g., a cat).
In some examples, the accelerator compiler 104A-C may configure one(s) of the acceleration circuitry 108, 110 to execute non-2-D convolution operations as 2-D convolution operations. For example, the accelerator compiler 104A-C may configure the one(s) of the acceleration circuitry 108, 110 to implement a depthwise convolution operation, an elementwise addition operation, a grouped convolution operation, a dilated convolution operation, a custom operation (e.g., a custom convolution, a custom acceleration operation, etc.), etc., as a 2-D convolution operation. In some such examples, the accelerator compiler 104A-C may instruct the one(s) of the acceleration circuitry 108, 110 to internally generate data rather than receive the data from the accelerator compiler 104A-C, the configuration data 122, etc. For example, the accelerator compiler 104A-C may instruct the first acceleration resource to generate at least one of activation sparsity data, weight sparsity data, or weight data based on the acceleration operation to be executed by the first acceleration circuitry 108. In some such examples, the accelerator compiler 104A-C may instruct the one(s) of the acceleration circuitry 108, 110 to execute the one(s) of the ML model(s) 124 based on the data generated by the one(s) of the acceleration circuitry 108, 110, which may be based on a convolution operation to be executed by the one(s) of the acceleration circuitry 108, 110.
In the illustrated example of
In operation, the example interface circuitry 210 obtains (e.g., retrieves, receives, acquires) any number of template questions and/or associated images (e.g., regarding store observations) to execute a machine-learning (ML) operation (e.g., a Visual Question Answering (VQA) operation). In examples disclosed herein, template questions represent a number of questions that are relevant to and/or otherwise associated with a retail environment of interest. In some examples, a first set of template questions are in the English language, associated with a grocery store environment, and in the United Kingdom (UK). As such, when an auditor is actively performing auditing tasks in one or more retail establishments in the UK, corresponding relevant questions will serve as informational triggers for data gathering (e.g., “Are there crisps on the shelf?”, “Are product prices shown in Pounds Sterling?”, etc.). In some examples, a second set of template questions are in the Spanish language, associated with a retail manufacturing environment in Spain. As such, when an auditor is performing auditing tasks in one or more retail manufacturing environments in Spain, the first set of template questions would not apply, as the language and/or relevant set of questions have changed. Therefore, by allowing a specific range of template questions (e.g., along with a particular associated dictionary), regional differences, language variation, industry changes, etc. are accounted for. Additionally, in examples disclosed herein, a field auditor's question typically accompanies a store observation image (e.g., that they may have captured on their personal computing device, etc.). For example, a field auditor may present an image of a store shelf and as “Are there bananas here?”. In examples disclosed herein, the interface circuitry 210 may obtain the template questions from the example datastore 120, the example external computing systems 130 of
In some examples, the interface circuitry 210 includes means for obtaining any input (e.g., the store dataset 294) to execute a machine-learning (ML) operation (e.g., a Visual Question Answering (VQA) operation). For example, the means for obtaining any input (e.g., the store dataset 294) to execute a machine-learning (ML) operation (e.g., a Visual Question Answering (VQA) operation) may be implemented by interface circuitry 210. In some examples, the interface circuitry 210 may be instantiated by processor circuitry such as the example processor circuitry 1412 of
The example store dictionary generator circuitry 220 updates and/or generates a dictionary relevant to store observations utilized by a machine-learning (ML) and/or artificial intelligence (AI) model to more accurately perform word embedding and/or parse an input question from a field auditor. In examples disclosed herein, a vocabulary of an ML/AI model defines a set of words that the model is able to recognize (e.g., as used in Natural Language Processing (NLP) models, Visual Question Answering (VQA) models, etc.). When a dictionary used by an NLP and/or VQA model is updated to include a wider group of words relevant to a specific type of industry in which the model is to be deployed, an accuracy measure of the trained model experiences a large increase as opposed to a model with a more limited and/or irrelevant dictionary. For example, if a dictionary associated with a sports industry included words such as “apple”, “banana”, “pear”, etc., the associated ML/AI that employs that dictionary would produce inaccurate and/or irrelevant results in response to questions asked about sporting teams, athletes, etc. In examples disclosed herein, the store dictionary generator circuitry 220 may obtain a new vocabulary from a datastore (e.g., datastore 120 of
In some examples, the example store dictionary generator circuitry 220 includes means for updating and/or generating an ML/AI model dictionary with a vocabulary specific to and/or including store observations. For example, the means for updating and/or generating an ML/AI model dictionary with a vocabulary specific to and/or including store observations may be implemented by store dictionary generator circuitry 220. In some examples, the store dictionary generator circuitry 220 may be instantiated by processor circuitry such as the example processor circuitry 1412 of
The example question processor circuitry 230 obtains a set of question templates (e.g., question templates 295) representing a set of question formats from which question-answer pairs are generated by the example question-answer pair generator circuitry 250. In examples disclosed herein, an example question template may be “how many { } are there?”. Using this example question template, example questions such as “how many bananas are there”, “how many wine bottles are there”, “how many apples are there”, etc. may be generated (e.g., by the question-answer pair generator circuitry 250). In examples disclosed herein, any number of the question templates (e.g., question templates 295) may be obtained from an example datastore (e.g., datastore 292, datastore 120 of
In some examples, the example question processor circuitry 230 includes means for updating and/or generating an ML/AI model dictionary using a vocabulary specific to and/or including store observations. For example, the means for updating and/or generating an ML/AI model dictionary using a vocabulary specific to and/or including store observations may be implemented by question processor circuitry 230. In some examples, the question processor circuitry 230 may be instantiated by processor circuitry such as the example processor circuitry 1412 of
The example store image processor circuitry 240 obtains a set of store observation images and further characterizes the store observation images as being whole images or cropped images. In examples disclosed herein, the store image processor circuitry 240 may obtain the store observation images from the store dataset 294 that was obtained by the interface circuitry 210. In some examples, the example store image processor circuitry 240 is instantiated by processor circuitry executing question processor circuitry 230 instructions and/or configured to perform operations such as those represented by the flowcharts of
In some examples, the example store image processor circuitry 240 includes means for obtaining and characterizing a set of store observation images as whole and/or cropped images. For example, the means for obtaining and characterizing a set of store observation images and whole and/or cropped images may be implemented by store image processor circuitry 240. In some examples, the store image processor circuitry 240 may be instantiated by processor circuitry such as the example processor circuitry 1412 of
The example question-answer pair generator circuitry 250 generates (e.g., by way of the example ML model(s) 293), a set of question-answer pairs, based on an associated loss score. In examples disclosed herein, for each store observation image obtained by the example interface circuitry 210 in the store dataset 294, after characterization as a whole or cropped image by the example store image processor circuitry 240, the question-answer pair generator circuitry 250 selects a subset of the question templates 295 (e.g., generated by the question processor circuitry 230) based on the determined characterization of the given store observation image. For example, if a particular store observation image is classified as a whole image by the store image processor circuitry 240, a set of questions pertaining to a wholistic shelf view may be included (e.g., “how many shelves are there?”, “how many different categories are in this image?”, “what is the number of horizontal shelves?”, etc.), instead of more particular product-specific questions (e.g., “what brand is this product?”, “what size is this product?”, etc.) that would be included if the particular image were characterized as a cropped image.
In some examples, the question templates 295 obtained by the interface circuitry 210 (e.g., as part of the store dataset 294) may be flagged and/or otherwise marked for association with whole and/or cropped images. That is, for example, a first subset of question templates directed towards cropped images of the store observation images would be identifiable against a second subset of question templates directed towards whole images of the store observations images, for selection by the question-answer pair generator circuitry 250. Furthermore, in examples disclosed herein, metadata associated with the question templates (e.g., obtained as part of the store dataset 294) may mark and/or otherwise flag for use a particular subset of question templates applicable for a particular retail location, field auditor, region, retail industry, etc. That is, for example, should a field auditor visit a particular retail chain store in Spain, an associated set of question templates relevant to that particular industry, region, language, etc. would be flagged for selection.
For each store observation image, the question-answer pair generator circuitry 250 selects the desired subset of question templates based on the characterization of the given store observation image made by the store image processor circuitry 240 and inputs the subset of question templates to machine-learning (ML)/artificial intelligence (AI) model(s) (e.g., the ML model(s) 293). The question-answer pair generator circuitry 250 causes the ML model(s) 293 to iterate through a number of permutations of question-answer pairs, and selects those with a minimum associated loss score for use with each image, based on a ground truth answer associated with each question. For example, in operation, the ML model(s) 293 may take a whole image of a set of store shelves and a question of “how many shelves are there?” to generate an answer of “4” (e.g., based on a loss score associated with a ground truth answer to that question, as provided in the example store dataset 294). The question-answer pair generator circuitry 250 then stores (e.g., in the datastore 292) “how many shelves are there?” and “4” as a question-answer pair associated with that particular store observation image for use in deployment of the ML model(s) 293. In some examples, the example question-answer pair generator circuitry 250 is instantiated by processor circuitry executing question-answer pair generator circuitry 250 instructions and/or configured to perform operations such as those represented by the flowcharts of
In some examples, the example question-answer pair generator circuitry 250 includes means for generating question-answer pairs using the ML model(s) 293 and associated ground truth values for each characterized store observation image. For example, the means for generating question-answer pairs using the ML model(s) 293 and associated ground truth values for each characterized store observation image may be implemented by question-answer pair generator circuitry 250. In some examples, the question-answer pair generator circuitry 250 may be instantiated by processor circuitry such as the example processor circuitry 1412 of
The example model trainer circuitry 260 trains the ML model(s) 293 using the question-answer pairs generated by the question-answer pair generator circuitry 250 to ensure any combination of words adequately resembling a question included in the question-answer pairs generated by the question-answer pair generator circuitry 250 (e.g., as measured by a loss score associated with training of the ML model(s) 293) is recognized as a natural language representation of that particular question. For example, a question such as “how many shelves are there?” that is part of a question-answer pair generated by the question-answer pair generator circuitry 250 may have the same answer as and/or closely resemble “what are the number of shelves?”, “what number of shelves are there?”, etc. The model trainer circuitry 260, in examples disclosed herein, broadens the recognizability of each question-answer pair to account for variations in natural language, dialects, regions, etc. In some examples, the example model trainer circuitry 260 is instantiated by processor circuitry executing model trainer circuitry 260 instructions and/or configured to perform operations such as those represented by the flowcharts of
In some examples, the example model trainer circuitry 260 includes means for training the ML model(s) 293 to account for natural language variations in the question-answer pairs generated by the question-answer pair generator circuitry 250. For example, the means for training the ML model(s) 293 to account for natural language variations in the question-answer pairs generated by the question-answer pair generator circuitry 250 may be implemented by model trainer circuitry 260. In some examples, the model trainer circuitry 260 may be instantiated by processor circuitry such as the example processor circuitry 1412 of
The example query analyzer circuitry 270 obtains a query (e.g., from a user, a field auditor, etc.) regarding store observation images and parses the question and store observation image to the example model execution circuitry 280 to perform word embedding, image classification, etc. using the ML model(s) 293. In examples disclosed herein, the query may be obtained via a network (e.g., the network 128 of
In some examples, the example query analyzer circuitry 270 includes means for obtaining and processing a query regarding a store observation image, to pass the question and associated store observation image to the model execution circuitry 280 for deployment. For example, the means for obtaining and processing a query regarding a store observation image, to pass the question and associated store observation image to the model execution circuitry 280 for deployment may be implemented by query analyzer circuitry 270. In some examples, the query analyzer circuitry 270 may be instantiated by processor circuitry such as the example processor circuitry 1412 of
The example model execution circuitry 280 inputs the question and associated store observation image distinguished by the query analyzer circuitry 270 to the ML model(s) 293 to generate a query response. In examples disclosed herein, the ML model(s) 293 performs word embedding using the question (e.g., based on the updated dictionary generated by the store dictionary generator circuitry 200) and/or performs image classification on the store observation image further provided as input to the model. In examples disclosed herein, any method and/or technique for word embedding and/or image classification may be utilized by the ML/AL model (e.g., ML model(s) 293). Once the ML/AI model processes the input question and store observation image, the model outputs an answer to that question. For example, an input of a whole store observation image including four horizontal shelves and an associated question of “how many shelves are there?” would yield an answer of “4”. In some examples, the example model execution circuitry 280 is instantiated by processor circuitry executing model execution circuitry 280 instructions and/or configured to perform operations such as those represented by the flowcharts of
In some examples, the model execution circuitry 280 includes means for utilizing an ML/AI model to perform word embedding on a question and/or to perform image classification on an associated store observation image to output an answer to the query. For example, the means for utilizing an ML/AI model to perform word embedding on a question and/or to perform image classification on an associated store observation image to output an answer to the query may be implemented by model execution circuitry 280. In some examples, the model execution circuitry 280 may be instantiated by processor circuitry such as the example processor circuitry 1412 of
The example query response generator circuitry 290 outputs the answer generated by the model execution circuitry 280 (e.g., to a user). In examples disclosed herein, the answer may be displayed via a graphical user interface (e.g., the user interface 126 of
In some examples, the query response generator circuitry 290 includes means for outputting the answer to the query generated by the model execution circuitry 280 (e.g., to a user). For example, the means for outputting the answer to the query generated by the model execution circuitry 280 (e.g., to a user) may be implemented by query response generator circuitry 290. In some examples, the query response generator circuitry 290 may be instantiated by processor circuitry such as the example processor circuitry 1412 of
While an example manner of implementing the accelerator compiler 104 is illustrated in
The example input store observation image 302 is passed as input to the ML/AI model utilized by the ML/AI model framework 300 and classified by the image classification layer 304. In examples disclosed herein, the example input store observation image 302 may be any type of image (e.g., of a store shelf, a particular product, etc.) taken by a field auditor on a personal computing device, a personal image capturing device, etc. In some examples, the ML/AI model may account for blurry and/or otherwise incompatible images (e.g., images where attempted classification fails) by selecting a pre-captured image of the particular store shelf in that particular retail location for use (e.g., through similarity comparison). In addition, in some examples, the field auditor may select from a preexisting set of store observation images (e.g., particular to the retail location in which they are actively auditing) to select their relevant image associated with their question. In examples disclosed herein, any image classification technique may be utilized by the image classification layer 304, which may further include sub-layers such as a pooling layer, a convolution layer combined with a non-linearity pooling layer, and a fully-connected multilayer perceptron (MLP) (e.g., a collection of interleaved fully-connected layers and non-linearity layers). The image classification layer 304, in examples disclosed herein, may identify and/or classify a type of object in the input store observation image 302 (e.g., a shelf, a banana, etc.) for selection later as (part of) the query response 318, based on the input word-embedded question 308. The example image fully-connected layer 306 represents a layer of the neural network (e.g., CNN) where every input neuron is connected to an output neuron (e.g., representing a classified store observation image).
The example input word-embedded question 308 represents an embedded and/or encoded input question associated with the input store observation image 302, such as “how many shelves are in this image?”. In examples disclosed herein, any word embedding and/or encoding technique may be used to represent the input word-embedded question 308, such as one-hot encoding, etc. The example question fully-connected layer 310 represents a layer of the neural network (e.g., CNN) where every input neuron is connected to an output neuron (e.g., representing an embedded input question).
The image fully-connected layer 306 and the question fully-connected layer 310 are then, in examples disclosed herein, combined using point-wise multiplication (e.g., multiplication of each element in the image fully-connected layer 306 by each element in the question fully-connected layer 310) into a point-wise multiplication layer 312. The point-wise multiplication layer 312, in examples disclosed herein, reconciles each classified element of the input store observation image 302 with the input word-embedded question 308. The point-wise multiplication layer 312 is then converted to the example aggregate fully-connected layer 314. In examples disclosed herein, the aggregate fully-connected layer 314 represents a fully-connected layer of classified objects and word embeddings. The aggregate fully-connected layer 314 is then converted into a softmax layer 316 (e.g., by the model execution circuitry 280 of
The example word embedding architecture 400 of
Flowcharts representative of example machine readable instructions, which may be executed to configure processor circuitry to implement the accelerator compiler 104 of
The machine readable instructions described herein may be stored in one or more of a compressed format, an encrypted format, a fragmented format, a compiled format, an executable format, a packaged format, etc. Machine readable instructions as described herein may be stored as data or a data structure (e.g., as portions of instructions, code, representations of code, etc.) that may be utilized to create, manufacture, and/or produce machine executable instructions. For example, the machine readable instructions may be fragmented and stored on one or more storage devices and/or computing devices (e.g., servers) located at the same or different locations of a network or collection of networks (e.g., in the cloud, in edge devices, etc.). The machine readable instructions may require one or more of installation, modification, adaptation, updating, combining, supplementing, configuring, decryption, decompression, unpacking, distribution, reassignment, compilation, etc., in order to make them directly readable, interpretable, and/or executable by a computing device and/or other machine. For example, the machine readable instructions may be stored in multiple parts, which are individually compressed, encrypted, and/or stored on separate computing devices, wherein the parts when decrypted, decompressed, and/or combined form a set of machine executable instructions that implement one or more operations that may together form a program such as that described herein.
In another example, the machine readable instructions may be stored in a state in which they may be read by processor circuitry, but require addition of a library (e.g., a dynamic link library (DLL)), a software development kit (SDK), an application programming interface (API), etc., in order to execute the machine readable instructions on a particular computing device or other device. In another example, the machine readable instructions may need to be configured (e.g., settings stored, data input, network addresses recorded, etc.) before the machine readable instructions and/or the corresponding program(s) can be executed in whole or in part. Thus, machine readable media, as used herein, may include machine readable instructions and/or program(s) regardless of the particular format or state of the machine readable instructions and/or program(s) when stored or otherwise at rest or in transit.
The machine readable instructions described herein can be represented by any past, present, or future instruction language, scripting language, programming language, etc. For example, the machine readable instructions may be represented using any of the following languages: C, C++, Java, C #, Perl, Python, JavaScript, HyperText Markup Language (HTML), Structured Query Language (SQL), Swift, etc.
As mentioned above, the example operations of
“Including” and “comprising” (and all forms and tenses thereof) are used herein to be open ended terms. Thus, whenever a claim employs any form of “include” or “comprise” (e.g., comprises, includes, comprising, including, having, etc.) as a preamble or within a claim recitation of any kind, it is to be understood that additional elements, terms, etc., may be present without falling outside the scope of the corresponding claim or recitation. As used herein, when the phrase “at least” is used as the transition term in, for example, a preamble of a claim, it is open-ended in the same manner as the term “comprising” and “including” are open ended. The term “and/or” when used, for example, in a form such as A, B, and/or C refers to any combination or subset of A, B, C such as (1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) B with C, or (7) A with B and with C. As used herein in the context of describing structures, components, items, objects and/or things, the phrase “at least one of A and B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B. Similarly, as used herein in the context of describing structures, components, items, objects and/or things, the phrase “at least one of A or B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B. As used herein in the context of describing the performance or execution of processes, instructions, actions, activities and/or steps, the phrase “at least one of A and B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B. Similarly, as used herein in the context of describing the performance or execution of processes, instructions, actions, activities and/or steps, the phrase “at least one of A or B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B.
As used herein, singular references (e.g., “a”, “an”, “first”, “second”, etc.) do not exclude a plurality. The term “a” or “an” object, as used herein, refers to one or more of that object. The terms “a” (or “an”), “one or more”, and “at least one” are used interchangeably herein. Furthermore, although individually listed, a plurality of means, elements or method actions may be implemented by, e.g., the same entity or object. Additionally, although individual features may be included in different examples or claims, these may possibly be combined, and the inclusion in different examples or claims does not imply that a combination of features is not feasible and/or advantageous.
At block 1005, the store dictionary generator circuitry 220 of
At block 1010, the model trainer circuitry 260 of
At block 1015, the query response generator circuitry 290 of
At block 1102, the store dictionary generator circuitry 220 of
In some examples, the store dictionary generator circuitry 220 obtains metadata from any number of candidate dictionaries and compares the metadata against context data, in which the context data may include information corresponding to a current location of a store, a current language corresponding to the store, a currency used at the current location, a type of store (e.g., a pharmacy, a grocery store, a convenience store, a beverage store, etc.). The store dictionary generator circuitry 220 compares the metadata for a candidate dictionary of interest against the context data to determine a similarity metric and/or threshold. For instance, the dictionary generator circuitry 220 compares a number of matches between the metadata and the context data in an effort to determine the similarity metric, such as whether the dictionary language is the same/different, whether the dictionary store type is same/different, whether the dictionary currency type is same/different, etc.
In examples disclosed herein, a vocabulary structure of an ML/AI model defines a set of words that the model is able to recognize (e.g., as used in Natural Language Processing (NLP) models, Visual Question Answering (VQA) models, etc.). When a dictionary used by an NLP and/or VQA model is updated to include a wider group of words relevant to a specific type of industry in which the model is to be deployed, an accuracy measure of the trained model experiences a large increase (e.g., improvement) as opposed to a model with a more limited and/or irrelevant dictionary. For example, if a dictionary associated with a sports industry included words such as “apple”, “banana”, “pear”, etc., the associated ML/AI that employs that dictionary would produce inaccurate and/or irrelevant results in response to questions asked about sporting teams, athletes, etc. In examples disclosed herein, the store dictionary generator circuitry 220 may obtain a new vocabulary from a datastore (e.g., datastore 120 of
At block 1104, the store dictionary generator circuitry 220 of
At block 1106, the interface circuitry 210 obtains a set of store observation images for a particular store, set of stores, retail locations, retail industry, etc. In examples disclosed herein, the interface circuitry 210 may obtain the set of store observation images from the example datastore 120, the example external computing systems 130 of
At block 1108, the store image processor circuitry 240 of
At block 1110, the interface circuitry 210 of
At block 1112, the question-answer pair generator circuitry 250 of
At block 1114, the question-answer pair generator circuitry 250 of
At block 1202, the model trainer circuitry 260 of
At block 1204, the model trainer circuitry 260 of
At block 1206, the model trainer circuitry 260 of
At block 1208, the model trainer circuitry 260 of
At block 1210, a broader set of questions are generated by the model trainer circuitry 260 of
At block 1212, the model trainer circuitry 260 of
At block 1214, the model trainer circuitry 260 of
At block 1216, the model trainer circuitry 260 of
At block 1302, the query analyzer circuitry 270 of
At block 1304, the query analyzer circuitry 270 of
At block 1306, the model execution circuitry 280 inputs the store observation input image and the store observation input question to the trained M/AI model(s) for determination of an answer to the field auditor's query.
At block 1308, the model execution circuitry 280 obtains an answer to the question as output of the ML/AI model(s).
At block 1310, the query response generator circuitry 290 reports the answer to the question obtained by the model execution circuitry 280 at block 1308. In examples disclosed herein, this answer may be reported by the query response generator circuitry 290 back to the field auditor (e.g., via the interface circuitry 210) and/or may be included as part of a larger report, etc.
The processor platform 1400 of the illustrated example includes processor circuitry 1412. The processor circuitry 1412 of the illustrated example is hardware. For example, the processor circuitry 1412 can be implemented by one or more integrated circuits, logic circuits, FPGAs, microprocessors, CPUs, GPUs, DSPs, and/or microcontrollers from any desired family or manufacturer. The processor circuitry 1412 may be implemented by one or more semiconductor based (e.g., silicon based) devices. In this example, the processor circuitry 1412 implements the example interface circuitry 210, the example store dictionary generator circuitry 220, the example question processor circuitry 230, the example store image processor circuitry 240, the example question-answer pair generator circuitry 250, the example model trainer circuitry 260, the example query analyzer circuitry 270, the example model execution circuitry 280, and/or the example query response generator circuitry 290.
The processor circuitry 1412 of the illustrated example includes a local memory 1413 (e.g., a cache, registers, etc.). The processor circuitry 1412 of the illustrated example is in communication with a main memory including a volatile memory 1414 and a non-volatile memory 1416 by a bus 1418. The volatile memory 1414 may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory (RDRAM®), and/or any other type of RAM device. The non-volatile memory 1416 may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory 1414, 1416 of the illustrated example is controlled by a memory controller 1417.
The processor platform 1400 of the illustrated example also includes interface circuitry 1420. The interface circuitry 1420 may be implemented by hardware in accordance with any type of interface standard, such as an Ethernet interface, a universal serial bus (USB) interface, a Bluetooth® interface, a near field communication (NFC) interface, a Peripheral Component Interconnect (PCI) interface, and/or a Peripheral Component Interconnect Express (PCIe) interface.
In the illustrated example, one or more input devices 1422 are connected to the interface circuitry 1420. The input device(s) 1422 permit(s) a user to enter data and/or commands into the processor circuitry 1412. The input device(s) 1422 can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, an isopoint device, and/or a voice recognition system.
One or more output devices 1424 are also connected to the interface circuitry 1420 of the illustrated example. The output device(s) 1424 can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display (LCD), a cathode ray tube (CRT) display, an in-place switching (IPS) display, a touchscreen, etc.), a tactile output device, a printer, and/or speaker. The interface circuitry 1420 of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip, and/or graphics processor circuitry such as a GPU.
The interface circuitry 1420 of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem, a residential gateway, a wireless access point, and/or a network interface to facilitate exchange of data with external machines (e.g., computing devices of any kind) by a network 1426. The communication can be by, for example, an Ethernet connection, a digital subscriber line (DSL) connection, a telephone line connection, a coaxial cable system, a satellite system, a line-of-site wireless system, a cellular telephone system, an optical connection, etc.
The processor platform 1400 of the illustrated example also includes one or more mass storage devices 1428 to store software and/or data. Examples of such mass storage devices 1428 include magnetic storage devices, optical storage devices, floppy disk drives, HDDs, CDs, Blu-ray disk drives, redundant array of independent disks (RAID) systems, solid state storage devices such as flash memory devices and/or SSDs, and DVD drives.
The machine readable instructions 1432, which may be implemented by the machine readable instructions of
The cores 1502 may communicate by a first example bus 1504. In some examples, the first bus 1504 may be implemented by a communication bus to effectuate communication associated with one(s) of the cores 1502. For example, the first bus 1504 may be implemented by at least one of an Inter-Integrated Circuit (I2C) bus, a Serial Peripheral Interface (SPI) bus, a PCI bus, or a PCIe bus. Additionally or alternatively, the first bus 1504 may be implemented by any other type of computing or electrical bus. The cores 1502 may obtain data, instructions, and/or signals from one or more external devices by example interface circuitry 1506. The cores 1502 may output data, instructions, and/or signals to the one or more external devices by the interface circuitry 1506. Although the cores 1502 of this example include example local memory 1520 (e.g., Level 1 (L1) cache that may be split into an L1 data cache and an L1 instruction cache), the microprocessor 1500 also includes example shared memory 1510 that may be shared by the cores (e.g., Level 2 (L2 cache)) for high-speed access to data and/or instructions. Data and/or instructions may be transferred (e.g., shared) by writing to and/or reading from the shared memory 1510. The local memory 1520 of each of the cores 1502 and the shared memory 1510 may be part of a hierarchy of storage devices including multiple levels of cache memory and the main memory (e.g., the main memory 1414, 1416 of
Each core 1502 may be referred to as a CPU, DSP, GPU, etc., or any other type of hardware circuitry. Each core 1502 includes control unit circuitry 1514, arithmetic and logic (AL) circuitry (sometimes referred to as an ALU) 1516, a plurality of registers 1518, the local memory 1520, and a second example bus 1522. Other structures may be present. For example, each core 1502 may include vector unit circuitry, single instruction multiple data (SIMD) unit circuitry, load/store unit (LSU) circuitry, branch/jump unit circuitry, floating-point unit (FPU) circuitry, etc. The control unit circuitry 1514 includes semiconductor-based circuits structured to control (e.g., coordinate) data movement within the corresponding core 1502. The AL circuitry 1516 includes semiconductor-based circuits structured to perform one or more mathematic and/or logic operations on the data within the corresponding core 1502. The AL circuitry 1516 of some examples performs integer based operations. In other examples, the AL circuitry 1516 also performs floating point operations. In yet other examples, the AL circuitry 1516 may include first AL circuitry that performs integer based operations and second AL circuitry that performs floating point operations. In some examples, the AL circuitry 1516 may be referred to as an Arithmetic Logic Unit (ALU). The registers 1518 are semiconductor-based structures to store data and/or instructions such as results of one or more of the operations performed by the AL circuitry 1516 of the corresponding core 1502. For example, the registers 1518 may include vector register(s), SIMD register(s), general purpose register(s), flag register(s), segment register(s), machine specific register(s), instruction pointer register(s), control register(s), debug register(s), memory management register(s), machine check register(s), etc. The registers 1518 may be arranged in a bank as shown in
Each core 1502 and/or, more generally, the microprocessor 1500 may include additional and/or alternate structures to those shown and described above. For example, one or more clock circuits, one or more power supplies, one or more power gates, one or more cache home agents (CHAs), one or more converged/common mesh stops (CMSs), one or more shifters (e.g., barrel shifter(s)) and/or other circuitry may be present. The microprocessor 1500 is a semiconductor device fabricated to include many transistors interconnected to implement the structures described above in one or more integrated circuits (ICs) contained in one or more packages. The processor circuitry may include and/or cooperate with one or more accelerators. In some examples, accelerators are implemented by logic circuitry to perform certain tasks more quickly and/or efficiently than can be done by a general purpose processor. Examples of accelerators include ASICs and FPGAs such as those discussed herein. A GPU or other programmable device can also be an accelerator. Accelerators may be on-board the processor circuitry, in the same chip package as the processor circuitry and/or in one or more separate packages from the processor circuitry.
More specifically, in contrast to the microprocessor 1500 of
In the example of
The configurable interconnections 1610 of the illustrated example are conductive pathways, traces, vias, or the like that may include electrically controllable switches (e.g., transistors) whose state can be changed by programming (e.g., using an HDL instruction language) to activate or deactivate one or more connections between one or more of the logic gate circuitry 1608 to program desired logic circuits.
The storage circuitry 1612 of the illustrated example is structured to store result(s) of the one or more of the operations performed by corresponding logic gates. The storage circuitry 1612 may be implemented by registers or the like. In the illustrated example, the storage circuitry 1612 is distributed amongst the logic gate circuitry 1608 to facilitate access and increase execution speed.
The example FPGA circuitry 1600 of
Although
In some examples, the processor circuitry 1412 of
A block diagram illustrating an example software distribution platform 1705 to distribute software such as the example machine readable instructions 1432 of
Example methods, apparatus, systems, and articles of manufacture for providing responses to queries regarding store observation images are disclosed. Further examples and combinations thereof include the following:
Example methods, apparatus, systems, and articles of manufacture to provide responses to queries corresponding to store observation images are disclosed herein. Further examples and combinations thereof include the following:
Example 1 includes a non-transitory computer readable medium comprising instructions that, when executed, cause a machine to at least obtain first metadata associated with a set of store dictionaries, select ones of the set of store dictionaries for use based on the associated first metadata, obtain second metadata associated with a set of question templates, select ones of the set of question templates for use based on the associated second metadata, generate question-answer pairs using the selected ones of the set of store dictionaries and the selected ones of the set of question templates, train a machine-learning model using the question-answer pairs, and generate query responses using the trained machine-learning model.
Example 2 includes the non-transitory computer readable medium as defined in example 1, further including a set of categorized store observation images.
Example 3 includes the non-transitory computer readable medium as defined in example 2, wherein the categorized store observation images are categorized based on an image type.
Example 4 includes the non-transitory computer readable medium as defined in example 3, wherein the image type is one or more of a whole image or a cropped image.
Example 5 includes the non-transitory computer readable medium as defined in example 2, wherein the instructions, when executed, cause the machine to mark the selected ones of the set of question templates for association with ones of the categorized store observation images.
Example 6 includes the non-transitory computer readable medium as defined in example 2, wherein the instructions, when executed, cause the machine to generate the question-answer pairs by determining natural language variations of the selected ones of the set of question templates.
Example 7 includes the non-transitory computer readable medium as defined in example 6, wherein the determination of the natural language variations of the set of question templates included in the store observation dataset is performed by the machine-learning model.
Example 8 includes the non-transitory computer readable medium as defined in example 1, wherein the machine-learning model is trained using an updated dictionary obtained from a store observation dataset.
Example 9 includes an apparatus to generate query responses comprising at least one memory, machine readable instructions, and processor circuitry to at least one of instantiate or execute the machine readable instructions to obtain first metadata associated with a set of store dictionaries, select ones of the set of store dictionaries for use based on the associated first metadata, obtain second metadata associated with a set of question templates, select ones of the set of question templates for use based on the associated second metadata, generate question-answer pairs using the selected ones of the set of store dictionaries and the selected ones of the set of question templates, train a machine-learning model using the question-answer pairs, and generate query responses using the trained machine-learning model.
Example 10 includes the apparatus as defined in example 9, wherein the processor circuitry is to retrieve a set of store observation images.
Example 11 includes the apparatus as defined in example 10, wherein the processor circuitry is to arrange the store observation images based on an image type.
Example 12 includes the apparatus as defined in example 11, wherein the processor circuitry is to detect the image type as at least one of a whole image or a cropped image.
Example 13 includes the apparatus as defined in example 12, wherein the processor circuitry is to identify the whole image as two or more retail shelves, and identify the cropped image as a single retail shelf.
Example 14 includes the apparatus as defined in example 10, wherein the processor circuitry is to mark selected ones of the set of question templates for association with ones of the store observation images.
Example 15 includes the apparatus as defined in example 10, wherein the processor circuitry is to generate the question-answer pairs by determining natural language variations of the selected ones of the set of question templates.
Example 16 includes a method to generate query responses comprising obtaining, by executing an instruction with processor circuitry, first metadata associated with a set of store dictionaries, selecting, by executing an instruction with the processor circuitry, ones of the set of store dictionaries for use based on the associated first metadata, obtaining, by executing an instruction with the processor circuitry, second metadata associated with a set of question templates, selecting, by executing an instruction with the processor circuitry, ones of the set of question templates for use based on the associated second metadata, generating, by executing an instruction with the processor circuitry, question-answer pairs using the selected ones of the set of store dictionaries and the selected ones of the set of question templates, training, by executing an instruction with the processor circuitry, a machine-learning model using the question-answer pairs, and generating, by executing an instruction with the processor circuitry, query responses using the trained machine-learning model.
Example 17 includes the method as defined in example 16, further including retrieving a set of store observation images.
Example 18 includes the method as defined in example 17, further including sorting the store observation images based on an image type.
Example 19 includes the method as defined in example 18, further including determining whether the image type corresponds to a whole image or a partial image.
Example 20 includes the method as defined in example 19, further including determining the whole image corresponds to two or more shelves of a store shelf structure and determining the partial image corresponds to a single shelf of a store shelf structure.
Example 21 includes the method as defined in example 17, further including marking selected ones of the set of question templates for association with ones of the store observation images.
From the foregoing, it will be appreciated that example methods, apparatus and articles of manufacture have been disclosed that extend the applications of Visual Question Answering (VQA) in retail applications and/or industries. Example methods and apparatus disclosed herein efficiently provide accurate answers to questions regarding store observations by utilizing a broadened of a dictionary and/or dataset involved in training of ML/AI model(s), as well as through generation of question-answer pairs in order to promote wider generalizability. Such a method reduces the amount of misinformation spread through inaccurate answers provided using human discretion and/or ML/AI models trained using a limited dataset and additionally reduces computational expense and/or resources. That is, in the example methods disclosed herein, an accurate and/or widely generalizable dataset is synthetically generated (e.g., through dictionary updating, generation of question-answer pairs, etc.), using machine-learning (ML) and/or artificial intelligence (AI) techniques, in order to train a more adaptable ML/AI model or a set of ML/AI models. Disclosed systems, methods, apparatus, and articles of manufacture are accordingly directed to one or more improvement(s) in the operation of a machine such as a computer or other electronic and/or mechanical device.
Although certain example methods, apparatus and articles of manufacture have been disclosed herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all methods, apparatus and articles of manufacture fairly falling within the scope of the claims of this patent.
The following claims are hereby incorporated into this Detailed Description by this reference. Although certain example systems, methods, apparatus, and articles of manufacture have been disclosed herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all systems, methods, apparatus, and articles of manufacture fairly falling within the scope of the claims of this patent.
