Abstract: The embodiments of the present disclosure provide methods and apparatuses for generating point cloud completion network and methods, apparatuses and systems for processing point cloud data. First point cloud data is acquired from a first point cloud completion network based on one or more latent space vectors that are acquired through sampling in latent space, and a second point cloud completion network is generated by adjusting the first point cloud completion network based on a points-distribution feature of the first point cloud data. Since the points-distribution feature of the point cloud data is taken into consideration during generating the second point cloud completion network, the trained second point cloud completion network is capable of correcting the points-distribution feature of the point cloud data, and thus outputting the point cloud data with a relatively even points-distribution feature.

Abstract: The embodiments of the present disclosure provide a method, an apparatus and a system for completing point cloud data and a method, an apparatus and a system for processing point cloud data. The method includes: acquiring first point cloud data; acquiring second point cloud data by completing the first point cloud data with a point cloud completion network, wherein, the point cloud completion network is trained based on complete point cloud data and configured to generate third point cloud data according to target latent space vector, and a difference between fourth point cloud data acquired from the third point cloud data by performing preset degradation and real point cloud data collected from a physical space is within a preset difference range.

Abstract: The embodiments of the present disclosure provide a method, an apparatus and a system for generating a point cloud completion network. The method includes: acquiring first point cloud data by inputting a latent space vector into a pre-trained first point cloud completion network; acquiring real point cloud data collected for a real object in a physical space; for a real point in the real point cloud data, selecting a preset number of points adjacent to the real point from the first point cloud data as neighbor points of the real point; generating second point cloud data based on the neighbor points in the first point cloud data of a plurality of real points; acquiring a second point cloud completion network by adjusting the first point cloud completion network based on a difference between the second point cloud data and the real point cloud data.

Abstract: Provided are a matching method and apparatus, an electronic device, and a computer-readable storage medium. The method includes: obtaining a to-be-matched image; extracting at least one to-be-matched feature point from the to-be-matched image; for one of at least one reference image, performing matching between the at least one to-be-matched feature point and at least one reference feature point extracted from the reference image to obtain a distance set corresponding to the reference image, the distance set corresponding to the reference image being a distance set composed of distances between the at least one to-be-matched feature point and the at least one reference feature point, the one reference image including an image of a standard sample, and different reference images including different standard samples; and determining, based on the distance set corresponding to each reference image, whether the to-be-matched image matches each reference image.

Abstract: The present disclosure relates to an image processing method and apparatus, an electronic device, and a storage medium. The method includes: acquiring at least two target images; determining an attention map of at least one target in each of the at least two target images according to a result of detecting target of each target image, where the attention map indicates the position of one target in a target image; and determining, based on each target image and the attention map of the at least one target in the each target image, a result of association that indicates whether a correspondence exists between at least some of targets in different target images.

Abstract: The present disclosure provides methods, apparatuses, devices and storage media for predicting correlation between objects involved in an image. According to a method, a first object and a second object involved in an acquired image are detected, where the first object and the second object represent different body parts. First weight information of the first object with respect to a target region and second weight information of the second object with respect to the target region are determined. The target region corresponds to a surrounding box for a combination of the first object and the second object. A weighted-processing is performed on the target region respectively based on the first weight information and the second weight information to obtain a first weighted feature and a second weighted feature of the target region. A correlation between the first object and the second object within the target region is predicted based on the first weighted feature and the second weighted feature.

Abstract: Image identification methods and apparatuses, image generation methods and apparatuses, and neural network training methods and apparatuses are provided. In one aspect, an image identification method includes: obtaining a first image including a physical stack formed by stacking one or more first physical objects, and obtaining, by inputting the first image to a first neural network, category information of each of the one or more first physical objects output by the first neural network. The first neural network is pre-trained with a second image generated based on a virtual stack that is generated by stacking a three-dimensional model of one or more second physical objects.

Abstract: Methods, devices, electronic apparatuses and storage media of processing images, training neural networks, and recognizing human body actions are provided. In one aspect, a method of image processing includes: acquiring a human body bounding box and a target key point corresponding to a target body part in an image and acquiring first correlation information between the human body bounding box and the target key point; generating a target bounding box for the target body part according to the target key point and the human body bounding box; and determining, according to the first correlation information and pre-labeled second correlation information indicating correlation between a first body part and the human body bounding box, third correlation information to indicate a correlation between the target bounding box and a first bounding box for the first target body part.

Abstract: Methods, systems, and apparatus for detecting correlated objects involved in images are provided. In one aspect, a method includes: detecting a face object, a preset body part object, and a hand object involved in an image, performing a respective correlation prediction on every two of the face object, the preset body part object, and the hand object to obtain first, second, and third correlation prediction results, segmenting the image to determine at least one body object involved in the image to determine a first body object to which the face object belongs and a second body object to which the preset body part object belongs, adjusting the first correlation prediction result based on the first body object and the second body object, and determining correlated objects involved in the image according to the adjusted first correlation prediction result and the second and third correlation prediction results.

Abstract: An electronic device is provided. The electronic device includes a knob, a user interface (UI) module, and a human interface device (HID) module. The knob is configured to generate a motion signal. The UI module is configured to generate a setting signal. The HID module includes a first HID definition and a second HID definition stored therein and communicates with the UI module. The HID module interprets the motion signal based on the first HID definition or the second HID definition according to the setting signal to generate input information transferred to the operating system. A control method and a computer program product of the electronic device are also provided.

Abstract: Embodiments of the present disclosure disclose an image processing method and apparatus, and an electronic device. The method includes: obtaining a key frame image that includes a target object, and obtaining a to-be-processed frame image that includes the target object; extracting a feature point in the key frame image and a feature point in the to-be-processed frame image, respectively; determining a matching relationship between each feature point in the key frame image and each feature point in the to-be-processed frame image; determining a transformation relationship based on the feature point in the key frame image and a feature point in the to-be-processed frame image that matches the feature point in the key frame image; and processing the to-be-processed frame image based on the transformation relationship to obtain a target frame image.

Abstract: The present disclosure relates to an image retrieval method and apparatus. The method includes: obtaining an image group including a query image and at least one candidate image; calculating an intermediate similarity between any two images in the image group; performing multi-scale feature fusion based on the intermediate similarity between the any two images to obtain a final similarity between the query image and each candidate image; and determining a target image corresponding to the query image from each candidate image based on the final similarity. The present disclosure can improve precision and robustness of image retrieval.

Abstract: The embodiments of the application disclose a method for detecting a game prop in a game region, a device, and a storage medium. The method includes that: an image frame sequence collected from a game region at a game prop operating stage is acquired, the image frame sequence including a first preset frame number of game images and the first preset frame number being more than or equal to 2; target detection is performed on each frame of game image in the image frame sequence to obtain a first set of recognition results belonging to the same game prop, each recognition result at least including a confidence of the game prop; and reliability of the first set of recognition results of the game prop is determined based on all confidences in the first set of recognition results of the game prop and a confidence threshold.

Abstract: Provided are an image processing method, apparatus and device, and a storage medium. The method includes following operations: from multiple images of a preset scene, total information of picture content in each of the multiple images is acquired; among the multiple images, at least two first images in which a change in picture content meets a preset condition are determined based on the total information of the picture content in the multiple images; incremental information of an object that changes is determined in the at least two first images; and the incremental information is fed to an upper-layer service to enable the upper-layer service to conduct a service related to the object based on the incremental information.

Abstract: An image processing method and apparatus, an electronic device and a storage medium are provided. In the method, scene images corresponding to a game area at different viewpoints during a gaming stage are acquired to obtain a plurality of groups of scene images, and each group of scene images in the plurality of groups of scene images includes at least one frame of scene image corresponding to the game area at the same viewpoint; object analysis is performed on the plurality of groups of scene images to obtain object analysis data corresponding to the game area; and the object analysis data is written into an object general-purpose data structure, and the object general-purpose data structure is provided with positions for supporting storage of various types of data related to the object analysis data.

Abstract: An object recognition method includes: at least two consecutive frames of first images each including an image of at least one target object are acquired; a mapping relationship between each of the first images and a preset standard image associated with the target object is determined; object recognition is performed on the target object in each of the first images based on the mapping relationship to obtain a recognition result of the target object in each of the first images; and a target recognition result of the target object is determined based on the recognition results of the target objects in the at least two consecutive frames of first images.

Abstract: A method and device for identifying a state, electronic device and computer-readable storage medium are provided. The method includes: acquiring a to-be-detected image for a specific scene and determining a region of interest in the to-be-detected image; the region of interest being a region obtained by subtracting an occlusion range of the storage container in a closed state from an occlusion range in an open state; determining a positional relation between the region of interest and specific object regions where the specific objects are positioned in the to-be-detected image; determining at least one value based on values of pixels in the region of interest, when the positional relation represents that there is no specific object region partially overlapping the region of interest; and determining whether the storage container is in an open state or closed state based on the at least one value and a preset value range.

Abstract: The present disclosure relates to a method and apparatus for recognizing a sequence in an image, an electronic device, and a storage medium. The method includes: performing feature extraction on a to-be-processed image to obtain a first feature map of the to-be-processed image, where the to-be-processed image includes a sequence formed by stacking at least one object along a stacking direction; determining a region feature of each segmented region in the to-be-processed image based on the first feature map, where all segmented regions are obtained by dividing the to-be-processed image into k regions along the stacking direction, k is a set number of objects stacked along the stacking direction, and k is an integer greater than 1; and determining a category of each object in the sequence based on the region feature of each segmented region. Embodiments of the present disclosure may implement recognition of stacked objects in a sequence.

Abstract: The present disclosure relates to a network training method and device, an image processing method and device, the method comprising: performing pixel shuffling on a first image in a training set to obtain a second image, wherein the first image is an image subjected to pixel shuffling; performing, by a feature extraction network of a neural network, feature extraction on the first image to obtain a first image feature, and performing, by a feature extraction network, feature extraction on the second image to obtain a second image feature; performing, by a recognition network of the neural network, recognition on the first image feature to obtain a recognition result of the first image; and training the neural network according to the recognition result, the first image feature and the second image feature. Embodiments of the present disclosure enable improvement of recognition precision of neural networks.

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.