Abstract: An image processing apparatus includes a feature extractor, a map estimator, a first image estimator, a second image estimator, and an outputter. The feature extractor extracts an intermediate feature from an input image. The map estimator estimates an area map from the intermediate feature. The first image estimator estimates a first image from the intermediate feature. The second image estimator estimates a second image from the intermediate feature. The outputter outputs an output image obtained by, based on the area map, merging the first image and the second image. The second image estimator is trained to obtain desired image quality at a particular area based on the area map in the second image.

Abstract: A control apparatus controls one or more image capturing units. The apparatus comprises: an obtaining unit configured to, based on an image of a plurality of objects captured by the image capturing units, obtain positions of the plurality of objects; and a generation unit configured to, based on at least the image, the positions of the plurality of objects and the orientation of the image capturing units, generate a control command for changing the orientation of the image capturing units.

Abstract: An image recognition apparatus that recognizes a target with respect to image data by detecting a plurality of targets with respect to image data and outputting a plurality of detection objects that is based on the detected plurality of targets, extracting respective feature quantities from the output plurality of detection objects, outputting, with respect to each of the detection objects, a filtered feature, which is a feature quantity obtained by filtering the feature quantity extracted from each of the detection objects, based on a first mask for current time for each detection object predicted at previous time, and predicting the first mask for next time for each of the detection objects.

Abstract: A learning apparatus trains an estimator that executes a recognition task. The learning apparatus comprises: an obtaining unit configured to obtain a plurality of training data items including input data and supervisory data corresponding to the input data; a calculating unit configured to calculate statistic information relating to a predetermined perspective in the plurality of training data items; a determining unit configured to determine a degree of importance of each training data item included in the plurality of training data items based on the statistic information; and a control unit configured to control training of the estimator based on the degree of importance. The determining unit determines the degree of importance of each training data item such that unevenness of the plurality of training data items with respect to the predetermined perspective is reduced.

Abstract: There is provided with an information processing apparatus including a machine learning model configured to perform a recognition process on a recognition target in a captured image, based on pixel information of the captured image, and information about the captured image in addition to the pixel information. An inputting unit inputs the pixel information to a first portion of the machine learning model. A processing unit performs the recognition process by inputting correction information obtained by correcting an output of the first portion of the machine learning model by using the information about the captured image, to a second portion of the machine learning model, which follows the first portion.

Abstract: An information processing apparatus is operable to train a machine learning model that has a hierarchical structure configured by a plurality of hierarchical layers and that is used for recognizing a recognition target in inputted data. An obtaining unit obtains input data and data indicating a ground truth of an output from the machine learning model regarding the input data. A learning unit trains the machine learning model based on an error between the data indicating the ground truth of the output from the machine learning model regarding a specific domain of the input data and at least one output in an intermediate layer of the machine learning model with respect to the input data.

Abstract: A control apparatus controls one or more image capturing units. The apparatus comprises: an obtaining unit configured to, based on an image of a plurality of objects captured by the image capturing units, obtain positions of the plurality of objects; and a generation unit configured to, based on at least the image, the positions of the plurality of objects and the orientation of the image capturing units, generate a control command for changing the orientation of the image capturing units.

Abstract: An apparatus includes an acquisition unit that acquires a first image based on a first parameter, and a second image based on a second parameter, a segmentation unit that segments each of the first and second images into a plurality of segments, an acquisition unit that acquires feature quantities from each of the plurality of segments formed by segmenting the first and second images, respectively, a calculation unit that calculates a reliability of each of the plurality of segments of the first image based on the feature quantities acquired from the first image, a classification unit that classifies the plurality of segments of the first image into a first field having a relatively high reliability and a second field having a relatively low reliability, and a determination unit that determines categories for the first and second fields based on the feature quantities acquired from the first and second images.

Abstract: A Deep Neural Network (DNN) having a plurality of recognition tasks with different scales makes it possible to perform recognition processing in a network where identification layers are branched from one intermediate layer. An image recognition apparatus for recognizing a target includes a first acquisition unit configured to acquire from an input image a first intermediate feature amount for performing first identification, a first identification unit configured to perform the first identification based on the first intermediate feature amount, a second acquisition unit configured to acquire from the first intermediate feature amount a second intermediate feature amount for performing second identification having a larger scale than the first identification, and a second identification unit configured to perform the second identification based on the second intermediate feature amount.

Abstract: A learning apparatus includes an acquisition unit, a creation unit, and a first learning unit. The acquisition unit acquires a plurality of leaning data sets including a plurality of images imaged by a plurality of imaging devices, and sensor information of the imaging devices when the plurality of respective images is imaged. The creation unit creates, from the plurality of the plurality of learning data sets, a plurality of subsets, wherein each of the plurality of subsets has a different combination of the plurality of images and the sensor information. The first learning unit learns a plurality of first classifiers respectively corresponding to the plurality of subsets based on the plurality of respective subsets.

Abstract: On the basis of subsidiary information associated with image data, an image for the image data is segmented into multiple subregions, and feature values are extracted for each of the subregions obtained through the segmentation. The category for each of the subregions is determined on the basis of the extracted feature values.

Abstract: An apparatus includes an acquisition unit that acquires a first image based on a first parameter, and a second image based on a second parameter, a segmentation unit that segments each of the first and second images into a plurality of segments, an acquisition unit that acquires feature quantities from each of the plurality of segments formed by segmenting the first and second images, respectively, a calculation unit that calculates a reliability of each of the plurality of segments of the first image based on the feature quantities acquired from the first image, a classification unit that classifies the plurality of segments of the first image into a first field having a relatively high reliability and a second field having a relatively low reliability, and a determination unit that determines categories for the first and second fields based on the feature quantities acquired from the first and second images.

Abstract: A Deep Neural Network (DNN) having a plurality of recognition tasks with different scales makes it possible to perform recognition processing in a network where identification layers are branched from one intermediate layer. An image recognition apparatus for recognizing a target includes a first acquisition unit configured to acquire from an input image a first intermediate feature amount for performing first identification, a first identification unit configured to perform the first identification based on the first intermediate feature amount, a second acquisition unit configured to acquire from the first intermediate feature amount a second intermediate feature amount for performing second identification having a larger scale than the first identification, and a second identification unit configured to perform the second identification based on the second intermediate feature amount.

Abstract: A learning apparatus includes an acquisition unit, a creation unit, and a first learning unit. The acquisition unit acquires a plurality of leaning data sets including a plurality of images imaged by a plurality of imaging devices, and sensor information of the imaging devices when the plurality of respective images is imaged. The creation unit creates, from the plurality of the plurality of learning data sets, a plurality of subsets, wherein each of the plurality of subsets has a different combination of the plurality of images and the sensor information. The first learning unit learns a plurality of first classifiers respectively corresponding to the plurality of subsets based on the plurality of respective subsets.

Abstract: On the basis of subsidiary information associated with image data, an image for the image data is segmented into multiple subregions, and feature values are extracted for each of the subregions obtained through the segmentation. The category for each of the subregions is determined on the basis of the extracted feature values.