Abstract: An information processing apparatus registers a face image of a person, performs authentication to authenticate a person using an input face image and the registered face image, calculates a matching rate between the input face image and the registered face image, determines whether a possibility exists that the person in the input face image is identical to the person in the registered face image based on a result of the authentication and the matching rate, and outputs information corresponding to a result of the determination.

Abstract: An information processing apparatus comprises a first computation unit configured to obtain first features of an image of a tracking target, a second computation unit configured to obtain second features of an image of a search region, a third computation unit configured to obtain an inference tensor representing likelihoods that the tracking target is present at respective positions of the image of the search region, using the first features and the second features, and a fourth computation unit configured to obtain an inference map representing a position of the tracking target in the image of the search region, using the inference tensor.

Abstract: An information processing device includes an acquirer and a feature extractor. The acquirer is configured to acquire a feature sequence from each image in a plurality of images containing a common object. The feature extractor is configured to extract representative features of the object in the plurality of images from the feature sequence acquired by the acquirer. The acquirer is configured to acquire the feature sequence based on intra-image information within each image in the plurality of images and inter-image information across the plurality of images.

Abstract: An information processing apparatus includes one or more memories storing instructions, and one or more processors that, upon execution of the stored instructions, are configured to extract feature information from an input image, in a case where it is determined that the extracted feature information is inappropriate for object recognition, accumulate the feature information, and based on feature information extracted from an image as a processing target and the accumulated feature information, determine whether the image as the processing target is an inappropriate image for object recognition.

Abstract: An image processing apparatus includes one or more memories storing instructions, and one or more processors that, upon execution of the stored instructions, are configured to perform predetermined conversion processing on a first image including a subject to generate a second image, detect feature points of the subject from the first and second images, and determine a reference coordinate system for a detector for detecting feature points based on a relation between a result of detecting feature points in the first image and a result of detecting feature points in the second image.

Abstract: There is provided with a data processing apparatus for detecting an object from an image using a hierarchical neural network. The data processing apparatus has parallel first and second neural networks. An obtaining unit obtains a table which defines different first and second portions. An operation unit performs calculation of the feature data of a third portion based on feature data of the first portion identified using the table and on a weighting parameter between first and second layers of the first neural network, and calculation of feature data of a fourth portion based on feature data of the second portion identified using the table and on a weighting parameter between the first and second layers of the second neural network.

Abstract: A connected layer feature is generated by connecting outputs of a plurality of layers of a hierarchical neural network obtained by processing an input image using the hierarchical neural network. An attribute score map representing an attribute of each region of the input image is generated for each attribute using the connected layer feature. A recognition result for a recognition target is generated and output by integrating the generated attribute score maps for respective attributes.

Abstract: There is provided with a data processing apparatus for detecting an object from an image using a hierarchical neural network. The data processing apparatus has parallel first and second neural networks. An obtaining unit obtains a table which defines different first and second portions. An operation unit performs calculation of the feature data of a third portion based on feature data of the first portion identified using the table and on a weighting parameter between first and second layers of the first neural network, and calculation of feature data of a fourth portion based on feature data of the second portion identified using the table and on a weighting parameter between the first and second layers of the second neural network.

Abstract: A connected layer feature is generated by connecting outputs of a plurality of layers of a hierarchical neural network obtained by processing an input image using the hierarchical neural network. An attribute score map representing an attribute of each region of the input image is generated for each attribute using the connected layer feature. A recognition result for a recognition target is generated and output by integrating the generated attribute score maps for respective attributes.

Abstract: There is provided with an image processing apparatus. An extraction unit extracts a first feature from first data of a first modal type, the first data including information of a first object that is registered, and extract a second feature from second data of a second modal type that is different from the first modal type, the second data including information of a second object for matching. A determination unit determines whether or not the first object and the second object are identical, based on the first feature and the second feature. The extraction unit is trained to extract the first feature and the second feature to be similar when the first object and the second object are identical.

Abstract: An image processing apparatus includes a first acquisition unit configured to acquire a first feature amount from a first image based on a first trained model configured to extract a feature from an image, a second acquisition unit configured to acquire a second feature amount from a second image based on a second trained model determined based on a state of the second image and configured to extract a feature from an image, and a verification unit configured to determine, based on the first feature amount and the second feature amount, whether an object in the first image and an object in the second image are the same. The second trained model is a model having learned the second feature amount in a same feature space as that for the first trained model.

Abstract: An information processing apparatus comprises a first computation unit configured to obtain first features of an image of a tracking target, a second computation unit configured to obtain second features of an image of a search region, a third computation unit configured to obtain an inference tensor representing likelihoods that the tracking target is present at respective positions of the image of the search region, using the first features and the second features, and a fourth computation unit configured to obtain an inference map representing a position of the tracking target in the image of the search region, using the inference tensor.

Abstract: An apparatus includes a first acquisition unit that acquires a plurality of likelihood maps by setting a plurality of different weight parameters in a trained model that outputs, with an image feature extracted from the input image as an input, a likelihood map including, in association with a position in the input image, a likelihood indicating a possibility that the object is present, and a detection unit that detects, based on the acquired plurality of the likelihood maps, the position of the object included in the input image, wherein the trained model is a model that has learned the weight parameters based on loss values at least acquired using a first loss function for reducing a likelihood around a position of interest in the likelihood map, and a second loss function for increasing a likelihood acquired at the position of the object in the input image.

Abstract: There is provided with an information processing apparatus. An outputting unit, for each of a plurality of reference angles, outputs an evaluation value indicating whether a detection target in an image is inclined at the reference angle with respect to a standard orientation of the detection target. A first estimating unit estimates an inclination angle of the detection target in the image with respect to the standard orientation based on the evaluation values that have been respectively output for the plurality of reference angles. A detecting unit detects the detection target through processing in which an adjustment has been made using the estimated inclination angle.

Abstract: The present disclosure makes it possible to learn a neural network architecture for achieving a sufficient inference accuracy while preventing an increase in the amount of processing. An information processing apparatus configured to learn an architecture for optimizing a structure of a neural network generates a plurality of candidates for an edge of the neural network, inputs learning data to the neural network with weight coefficients set to these candidates for the edge, and obtains an inference result. The information processing apparatus calculates a loss of the neural network based on a specified candidate number which is the number of candidates to be selected from the plurality of candidates and on the inference result, and then updates the weight coefficients for the plurality of candidates based on the loss. The information processing apparatus then selects candidates from the plurality of candidates based on the updated weight coefficients.

Abstract: According to the present disclosure, a weight parameter of a neural network is divided into a plurality of portions having a certain size and approximation is individually performed on the portions using a weighted sum of the codebook vectors.

Abstract: A connected layer feature is generated by connecting outputs of a plurality of layers of a hierarchical neural network obtained by processing an input image using the hierarchical neural network. An attribute score map representing an attribute of each region of the input image is generated for each attribute using the connected layer feature. A recognition result for a recognition target is generated and output by integrating the generated attribute score maps for respective attributes.

Abstract: A connected layer feature is generated by connecting outputs of a plurality of layers of a hierarchical neural network obtained by processing an input image using the hierarchical neural network. An attribute score map representing an attribute of each region of the input image is generated for each attribute using the connected layer feature. A recognition result for a recognition target is generated and output by integrating the generated attribute score maps for respective attributes.

Abstract: An apparatus includes a first acquisition unit that acquires a plurality of likelihood maps by setting a plurality of different weight parameters in a trained model that outputs, with an image feature extracted from the input image as an input, a likelihood map including, in association with a position in the input image, a likelihood indicating a possibility that the object is present, and a detection unit that detects, based on the acquired plurality of the likelihood maps, the position of the object included in the input image, wherein the trained model is a model that has learned the weight parameters based on loss values at least acquired using a first loss function for reducing a likelihood around a position of interest in the likelihood map, and a second loss function for increasing a likelihood acquired at the position of the object in the input image.

Abstract: An information processing apparatus includes a learning unit configured to learn a plurality of multi-layer neural networks configured to carry out a plurality of tasks, a generation unit configured to generate a shared layer candidate at a predetermined layer between or among the plurality of multi-layer neural networks, a first relearning unit configured to relearn the plurality of multi-layer neural networks in a structure using the shared layer candidate, and a determination unit configured to determine whether to share the shared layer candidate at the predetermined layer with respect to each of the plurality of tasks based on an evaluation of the relearning.