Abstract: The present disclosure relates to a genetic variation identification method, genetic variation identification apparatuses, and a storage medium. The method includes: obtaining at least one gene sequencing read corresponding to a genetic variation candidate site; obtaining base arrangement features of the genetic variation candidate site; determining non-base arrangement features of the genetic variation candidate site based on non-base arrangement information of the at least one gene sequencing read within a preset site interval, where the non-base arrangement features remain unchanged after a base arrangement order changes; and identifying genetic variation of the genetic variation candidate site based on the base arrangement features and the non-base arrangement features of the genetic variation candidate site.

Abstract: A method and an apparatus for facial age identification, an electronic device, and a computer readable medium include: obtaining estimated facial age of a person in an image to be identified; selecting N image samples from an image sample set of known age according to the estimated facial age and age of two or more preset age gaps with the estimated facial age, the N being not less than 2; obtaining a comparison result of ages between the image to be identified and the selected N image samples; and obtaining probability information for determining a person's facial age attribute information according to statistical information formed by the comparison result.

Abstract: The present disclosure relates to an action recognition method, an electronic device, and storage medium. The action recognition method includes: detecting a target part on a face in a detection image; capturing a target image corresponding to the target part from the detection image according to the detection result for the target part; and recognizing, according to the target image, whether the object having the face executes a set action. Embodiments of the present disclosure are applicable to faces of different sizes in different detection images, and are also applicable to faces of different types. The embodiments of the present disclosure have a wide application range. Not only the target images may include sufficient information for analysis, but also the problems of low system processing efficiency caused by oversized captured target images and excessive useless information are reduced.

Abstract: The present disclosure discloses a lesion detection method and apparatus, device, and a storage medium. The method includes: obtaining a first image comprising a plurality of sample slices, the first image being a three-dimensional image; performing feature extraction on the first image, to generate a first feature graph comprising a feature and a position of a lesion; the first feature graph comprising a three-dimensional feature; performing dimension reduction processing on the feature comprised in the first feature graph, to generate a second feature graph; the second feature graph comprising a two-dimensional feature; and detecting the second feature graph, to obtain a position of each lesion in the second feature graph and a confidence score corresponding to the position. Using the present disclosure may accurately detect lesion conditions of multiple parts in a patient body, and implement cancer preliminary assessment of a full body range of the patient.

Abstract: A vehicle lamp detection method includes: obtaining an image block including an image of a vehicle; and performing vehicle lamp detection on the image block by means of a deep neural network, to obtain a vehicle lamp detection result.

Abstract: Methods, apparatuses and electronic devices for identifying an object category include: determining M key point neighborhood regions from corresponding object candidate boxes according to position information of M key points in a plurality of object candidate boxes of an image to be detected, where M is less than or equal to the total number of key points of N preset object categories, and M and N are positive integers; and determining category information of at least one object in the image to be detected using a convolutional neural network model used for identifying an object category in the image according to the M key point neighborhood regions.

Abstract: A method and apparatus for detecting a living body, an electronic device and a storage medium include: performing target object detection on a first image captured by a first image sensor in a binocular camera apparatus to obtain a first target region, and performing the target object detection on a second image captured by a second image sensor in the binocular camera apparatus to obtain a second target region; obtaining key point depth information of a target object according to the first target region and the second target region; and determining, based on the key point depth information of the target object, whether the target object is a living body.

Abstract: The present disclosure relates to a gene mutation identification method and apparatus, and a storage medium. The method includes: obtaining at least one gene sequencing read segment corresponding to a gene mutation candidate site; determining a sequence feature and a non-sequence feature of the gene mutation candidate site according to attribute information of the at least one gene sequencing read segment, where the sequence feature is a feature related to the position of the site; and identifying gene mutation of the gene mutation candidate site based on the sequence feature and the non-sequence feature. According to embodiments of the present disclosure, the sequence feature and the non-sequence feature of the gene can be combined, thereby more comprehensively analyzing the features of a gene mutation site and improving accuracy of gene mutation identification.

Abstract: Image processing methods, apparatuses, and electronic devices include: extracting features of an image to be processed to obtain a first feature map of the image; generating an attention map of the image based on the first feature map; fusing the attention map and the first feature map to obtain a fusion map; and extracting the features of the image again based on the fusion map. The implementation mode introduces an attention mechanism into image processing, and effectively improves the efficiency of acquiring information from an image.

Abstract: A method for structured text detection includes: receiving, by a convolutional neural network, a to-be-detected image and at least one character region template, the to-be-detected image includes structured text, the at least one character region template includes locations of N first character regions with N being an integer equal to or greater than 1, and the location of each first character region is obtained based on locations of second character regions corresponding to the each first character region in M sample images that are of the same type as the to-be-detected image, where M is an integer equal to or greater than 1; and obtaining, by the convolutional neural network, an actual location of the structured text in the to-be-detected image according to the at least one character region template. A system for structured detection and a non-transitory computer-readable medium are also provided.

Abstract: A method for detecting a living body includes: acquiring depth information of a target object sensed by a first sensor and a target image sensed by a second sensor; performing key point detection on the target image to obtain key point information of the target object; and obtaining a living body detection result of the target object based on the depth information of the target object and the key point information of the target object.

Abstract: A method for image processing, an electronic device, and a storage medium are provided. The method includes the following. For each processing method in a preset set of processing methods, a first feature parameter and a second feature parameter are determined according to image data to-be-processed, where the preset set includes at least two processing methods selected from whitening methods and/or normalization methods, and the image data to-be-processed includes at least one image data. A first weighted average of the first feature parameters is determined according to a weight coefficient of each first feature parameter, and a second weighted average of the second feature parameters is determined according to a weight coefficient of each second feature parameter. The image data to-be-processed is whitened according to the first weighted average and the second weighted average.

Abstract: Time domain action detecting methods and systems, electronic devices, and computer storage medium are provided. The method includes: obtaining a time domain interval in a video with an action instance and at least one adjacent segment in the time domain interval; separately extracting action features of at least two video segments in candidate segments, where the candidate segments comprises video segment corresponding to the time domain interval and adjacent segments thereof; pooling the action features of the at least two video segments in the candidate segments, to obtain a global feature of the video segment corresponding to the time domain interval; and determining, based on the global feature, an action integrity score of the video segment corresponding to the time domain interval. The embodiments of the present disclosure benefit accurately determining whether a time domain interval comprises an integral action instance, and improve the accuracy rate of action integrity identification.

Abstract: The present disclosure relates to a video repair method and apparatus, an electronic device, and a storage medium.

Abstract: A method and an apparatus for recognizing and training a video, an electronic device and a storage medium include: extracting features of a first key frame in a video; performing fusion on the features of the first key frame and fusion features of a second key frame in the video to obtain fusion features of the first key frame, where a detection sequence of the second key frame in the video precedes that of the first key frame; and performing detection on the first key frame according to the fusion features of the first key frame to obtain an object detection result of the first key frame. Through iterative multi-frame feature fusion, information contained in shared features of these key frames in the video can be enhanced, thereby improving frame recognition accuracy and video recognition efficiency.

Abstract: The present disclosure relates to a network optimization method and apparatus, an image processing method and apparatus, and a storage medium. The network optimization method includes: obtaining an image sample group; obtaining a first feature and a second feature of an image in the image sample group, and obtaining a first classification result by using the first feature of the image; performing feature exchange processing on an image pair in the image sample group to obtain a new image pair; obtaining a first loss value of the first classification result, a second loss value of the new image pair, and a third loss value of first features and second features of the new image pair in a preset manner; and adjusting parameters of a neural network at least according to the first loss value, the second loss value, and the third loss value until a preset requirement is met.

Abstract: A method and a system for pose estimation are provided. The method includes: extracting a plurality of sets of part-feature maps from an image, each set of the extracted part-feature maps encoding the messages for a particular body part and forming a node of a part-feature network; passing a message of each set of the extracted part-feature maps through the part-feature network to update the extracted part-feature maps, resulting in each set of the extracted part-feature maps incorporating the message of upstream nodes; estimating, based on the updated part-feature maps, the body part within the image.

Abstract: A method and an apparatus for detecting an object keypoint, an electronic device, a computer readable storage medium, and a computer program include: obtaining a respective feature map of at least one local regional proposal box of an image to be detected, the at least one local regional proposal box corresponding to at least one target object; and separately performing target object keypoint detection on a corresponding local regional proposal box of the image to be detected according to the feature map of the at least one local regional proposal box.

Abstract: A key frame scheduling method and apparatus include: performing feature extraction on a current frame through a first network layer of a neural network to obtain low-layer features of the current frame acquiring a scheduling probability of the current frame according to low-level features of a previous key frame adjacent to the current frame and the low-level features of the current frame; determining whether the current frame is scheduled as a key frame according to the scheduling probability value of the current frame; and when determining that the current frame is scheduled as a key frame, performing feature extraction on low-level features of a current key frame via a second network layer of the neural network to obtain high-level features of the current key frame, where the network depth of the first network layer is less than the network depth of the second network layer.

Abstract: Embodiments of the present application disclose driving state monitoring methods and apparatuses, driver monitoring systems, and vehicles. The driving state monitoring method includes: performing driver state detection on a driver image; and performing at least one of: outputting a driving state monitoring result of a driver or performing intelligent driving control based on a result of the driver state detection. The embodiments of the present application can implement real-time monitoring of the driving state of a driver, so as to take corresponding measures in time when the driving state of the driver is poor, to ensure safe driving and avoid road traffic accidents.