Abstract: The application relates to a method and system for tracking a target object in a video. The method includes: extracting, from the video, a 3-dimension (3D) feature block containing the target object; decomposing the extracted 3D feature block into a 2-dimension (2D) spatial feature map containing spatial information of the target object and a 2D spatial-temporal feature map containing spatial-temporal information of the target object; estimating, in the 2D spatial feature map, a location of the target object; determining, in the 2D spatial-temporal feature map, a speed and an acceleration of the target object; calibrating the estimated location of the target object according to the determined speed and acceleration; and tracking the target object in the video according to the calibrated location.

Abstract: A method for predicting walking behaviors includes: encoding walking behavior information of at least one target object in a target scene within a historical time period M to obtain a first offset matrix for representing the walking behavior information of the at least one target object within the historical time period M; inputting the first offset matrix into a neural network, and outputting by the neural network a second offset matrix for representing walking behavior information of the at least one target object within a future time period M?; and decoding the second offset matrix to obtain the walking behavior prediction information of the at least one target object within the future time period M?.

Abstract: Embodiments of the present application provide a method and an apparatus for processing a video image. The method includes: obtaining a video image to be processed and a business object to be displayed, wherein the video image comprises a background area and a foreground area comprising a target object non-overlapping with the background area; determining the background area of the video image; performing an action detection on the target object in the foreground area to obtain action detection data; determining a display position of the business object in the video image according to the action detection data; and drawing, according to the display position, the business object in the background area of the video image by means of computer graphics.

Abstract: Disclosed is a method for generating a semantic image labeling model, comprising: forming a first CNN and a second CNN, respectively; randomly initializing the first CNN; inputting a raw image and predetermined label ground truth annotations to the first CNN to iteratively update weights thereof so that a category label probability for the image, which is output from the first CNN, approaches the predetermined label ground truth annotations; randomly initializing the second CNN; inputting the category label probability to the second CNN to correct the input category label probability so as to determine classification errors of the category label probabilities; updating the second CNN by back-propagating the classification errors; concatenating the updated first and second CNNs; classifying each pixel in the raw image into one of general object categories; and back-propagating classification errors through the concatenated CNN to update weights thereof until the classification errors less than a predetermined

Abstract: Embodiments of the present disclosure disclose communication methods and systems, electronic devices, and computer clusters. The method includes: separately creating a corresponding thread for at least one of a plurality of target devices, where the created thread corresponding to the target device includes a communication thread and a message processing thread, and the message processing thread includes a message sending thread and/or a message receiving thread; and communicating with a corresponding target device on the basis of the corresponding created thread.

Abstract: A method for implementing image enhancement includes: performing filtering processing on a to-be-processed image to obtain an image subjected to the filtering processing; determining similarity degrees between pixel points in the to-be-processed image and a target region of a target object in the to-be-processed image; and fusing the similarity degrees, the to-be-processed image and the image subjected to the filtering processing, so that the higher a similarity degree between a pixel point and the target object in the to-be-processed image, the stronger a filtering effect of the pixel point, and the lower a similarity degree between the pixel point and the target object in the to-be-processed image, the weaker a filtering effect of the pixel point.

Abstract: An image question answering method includes: extracting a question feature representing a semantic meaning of a question, a global feature of an image, and a detection frame feature of a detection frame encircling an object in the image; obtaining a first weight of each of at least one area of the image and a second weight of each of at least one detection frame of the image according to question feature, global feature, and detection frame feature; performing weighting processing on global feature by using first weight to obtain an area attention feature of image; performing weighting processing on detection frame feature by using second weight to obtain a detection frame attention feature of image; and predicting an answer to question according to question feature, area attention feature, and detection frame attention feature.

Abstract: A face image processing method and apparatus, an electronic device, and a storage medium are provided. The method includes: obtaining face key points and a face deflection angle in a face image; determining a submalar triangle center in the face image according to the face key points and the face deflection angle; determining a submalar triangle region in the face image according to the face key points and the submalar triangle center; and performing color filling on the submalar triangle region. By means of the present disclosure, a submalar triangle region can be accurately positioned, and submalar triangle filling is performed based on the accurately positioned submalar triangle region, thereby obtaining a more natural filling effect.

Abstract: A face unlocking method includes: performing face detection on one or more images; performing face feature extraction on an image in which a face is detected; performing authentication on extracted face features based on stored face features, wherein the stored face features at least comprise face features of face images of at least two different angles corresponding to a same identity (ID); and performing an unlocking operation at least in response to the extracted face features passing the authentication.

Abstract: A method and an apparatus for operating a service object, and an electronic device include: obtaining first person's behavior data; generating a first service object control instruction corresponding to the first person's behavior data; and sending the first service object control instruction to a second terminal, so that the second terminal displays a service object based on the first service object control instruction; receiving a first service object control instruction sent by a first terminal; generating a second service object control instruction corresponding to second person's behavior data, the second person's behavior data being obtained by the second terminal; and displaying a service object based on the first service object control instruction and the second service object control instruction; thereby enriching the interaction modes between terminals, improving interaction flexibility, and satisfying interaction requirements for a first terminal user and/or a second terminal user.

Abstract: Vehicle-mounted camera pose estimation methods, apparatuses, and systems, and electronic devices involve performing lane line detection of a road on which a vehicle drives on the basis of a video stream of the road acquired by a vehicle-mounted camera; obtaining horizon information of the road on which the vehicle drives according to a lane line detection result; and obtaining pose information of the vehicle-mounted camera according to the horizon information.

Abstract: A target object recognition method includes: performing target object detection on an object of an image to be detected to obtain target object prediction information of the object, where the target object prediction information is confidence information that the detected object is the target object; performing key point detection on the object of the image to be detected to obtain key point prediction information of the object, where the key point prediction information is confidence information that a key point of the detected object is a key point of the target object; fusing the target object prediction information with the key point prediction information to obtain comprehensive prediction information of the object; and recognizing the target object according to the comprehensive prediction information.

Abstract: A method for object detection includes: obtaining a plurality of to-be-determined targets in a to-be-detected image; determining confidences of the plurality of to-be-determined targets separately belonging to at least one category, determining categories of the plurality of to-be-determined targets according to the confidences, and determining position offset values corresponding to the respective categories of the plurality of to-be-determined targets; using the position offset values corresponding to the respective categories of the plurality of to-be-determined targets as position offset values of the plurality of to-be-determined targets; and determining position information and a category of at least one to-be-determined target in the to-be-detected image according to the categories of the plurality of to-be-determined targets, the position offset values of the plurality of to-be-determined targets, and the confidences of the plurality of to-be-determined targets belonging to the categories thereof.

Abstract: A gaze point determination method and apparatus, an electronic device, and a computer storage medium are provided. The method includes: obtaining two-dimensional coordinates of eye feature points of at least one eye of a face in an image, the eye feature points including an eyeball center area feature point; obtaining, in the preset three-dimensional coordinate system, three-dimensional coordinate of a corresponding eyeball center area feature point in a three-dimensional face model corresponding to the face in the image based on the obtained two-dimensional coordinate of the eyeball center area feature point; and obtaining a determination result for a position of a gaze point of the eye of the face in the image according to two-dimensional coordinates of feature points other than the eyeball center area feature point in the eye feature points and the three-dimensional coordinate of the eyeball center area feature point in the preset three-dimensional coordinate system.

Abstract: A pedestrian re-identification method includes: obtaining a target video containing a target pedestrian and at least one candidate video; encoding each target video segment in the target video and each candidate video segment in the at least one candidate segment separately; determining a score of similarity between the each target video segment and the each candidate video segment according to encoding results, the score of similarity being used for representing a degree of similarity between pedestrian features in the target video segment and the candidate video segment; and performing pedestrian re-identification on the at least one candidate video according to the score of similarity.

Abstract: A semantic segmentation model training method includes: performing, by a semantic segmentation model, image semantic segmentation on at least one unlabeled image to obtain a preliminary semantic segmentation result as the category of the unlabeled image; obtaining, by a convolutional neural network based on the category of the at least one unlabeled image and the category of at least one labeled image, sub-images respectively corresponding to the at least two images and features corresponding to the sub-images, where the at least two images comprise the at least one unlabeled image and the at least one labeled image, and the at least two sub-images carry the categories of the corresponding images; and training the semantic segmentation model on the basis of the categories of the at least two sub-images and feature distances between the at least two sub-images.

Abstract: An instance segmentation method includes: performing feature extraction on an image via a neural network to output features at at least two different hierarchies; extracting region features corresponding to at least one instance candidate region in the image from the features at the at least two different hierarchies, and fusing region features corresponding to a same instance candidate region, to obtain a first fusion feature of each instance candidate region; and performing instance segmentation based on each first fusion feature, to obtain at least one of an instance segmentation result of the corresponding instance candidate region or an instance segmentation result of the image.

Abstract: Provided are methods and apparatuses for positioning face feature points. The method includes: carrying out edge detection on a face image to obtain a face feature line image; and fusing the face image and the face feature line image to obtain position information of face feature points.

Abstract: An image processing method includes: inputting a to-be-processed image into a neural network; and forming discrete feature data of the to-be-processed image via the neural network, where the neural network is trained based on guidance information, and during the training process, the neural network is taken as a student neural network; the guidance information includes: a difference between discrete feature data formed by a teacher neural network for an image sample and discrete feature data formed by the student neural network for the image sample.

Abstract: Provided are character image processing methods and apparatuses, devices, storage medium, and computer programs. The character image processing method mainly comprises: obtaining at least one image block containing a character in a character image to be processed; obtaining image block form transformation information of the image block on the basis of a neural network, the image block form transformation information being used for changing a character orientation in the image block to a predetermined orientation, and the neural network being obtained by means of training using an image block sample having form transformation label information; performing form transformation processing on the character image to be processed according to the image block form transformation information; and performing character recognition on the character image to be processed which is subjected to the form transformation.