Embodiments of the present disclosure provide method for processing images and apparatuses, method for identity verifications and apparatuses, electronic devices, and storage media. The method for processing images includes: obtaining first feature data of a first user image; comparing the first feature data with at least one piece of second feature data included in a database to obtain a comparison result; and determining, according to the comparison result, whether to update the database. According to the embodiments of the present disclosure, it is beneficial to the database to adapt to identity verification in different scenarios and changes in user's appearance generated over time, thereby improving the user experience.

A method for determining authenticity of a video in a surveillance system, whereby a sequence of image frames of a scene is captured, and an object is tracked. A current image quality measure in an image area corresponding to the tracked object is determined in at least a first and second image frame. chosen such that the object has moved at least a predetermined distance between the first and second image frames. A current image quality measure variation for the object is determined, the image quality measure variation describing the image quality measure as a function of position of the object in the image frames. The current image quality measure variation is compared to a known image quality measure variation. In response to the current image quality measure variation deviating from the known pixel density variation by less than a predetermined amount, it is determined that the video is authentic.

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.

A method and apparatus for processing an image based on partial images. The method includes extracting a feature of a current partial processing region of an input image frame by inputting pixel data of the current partial processing region into a convolutional neural network (CNN), updating a hidden state of a recurrent neural network (RNN) for a context between the current partial processing region and at least one previous partial processing region by inputting the extracted feature into the RNN, and generating an image processing result for the input image frame based on the updated hidden state.

A method and a device for video clip extraction, and a storage medium are disclosed. The method includes: obtaining a video, and sampling the video to obtain N video frames, wherein N is a positive integer; inputting the N video frames to a pre-trained frame feature extraction model to obtain a feature vector of each video frame in N video frames; determining scores of the N video frames based on a pre-trained scoring model; and extracting target video clips from the video based on the scores of the N video frames.

Systems and techniques are provided for performing video-based activity recognition. For example, a process can include extracting, using a first machine learning model, first one or more features from a first frame and second one or more features from a second frame. The first one or more features and the second one or more features are associated with a person driving a vehicle. The process can include processing, using a second machine learning model, the first one or more features and the second one or more features. The process can include determining, based on processing of the first one or more features and the second one or more features using the second machine learning model, at least one activity associated with the person driving the vehicle.

This document describes techniques for fine-motion virtual-reality or augmented-reality control using radar. These techniques enable small motions and displacements to be tracked, even in the millimeter or sub-millimeter scale, for user control actions even when those actions are small, fast, or obscured due to darkness or varying light. Further, these techniques enable fine resolution and real-time control, unlike conventional RF-tracking or optical-tracking techniques.

Provided are a point cloud segmentation method and apparatus, a device, and a storage medium. The point cloud segmentation method includes: acquiring a to-be-processed point cloud; obtaining, in a gridding scenario space to which respective point in the point cloud belongs, a target grid corresponding to the respective point through a pre-trained neural network, wherein the pre-trained neural network is obtained by training a sample point cloud and a sample target grid corresponding to the sample point cloud in a sample gridding scenario space; and outputting a point cloud corresponding to a respective instance according to an instance category corresponding to the target grid, wherein the same target grid has the same instance category.

An image generation method includes: extracting a content feature of a first image; respectively extracting an overall image style feature of a second image, and an object style feature of a partial image block, which includes an object, in the second image; determining a target style feature at least according to the overall image style feature and the object style feature; and generating a third image according to the content feature and the target style feature.

A video processing method, an electronic device and a storage medium are provided, and relate to the field of artificial intelligence, and particularly relates to the fields of deep learning, model training, knowledge mapping, video processing and the like. The method includes: acquiring a plurality of first video frames, and performing fine-grained splitting on the plurality of first video frames to obtain a plurality of second video frames; performing feature encoding on the plurality of second video frames according to multi-mode information related to the plurality of second video frames, to obtain feature fusion information for characterizing fusion of the multi-mode information; and performing similarity matching on the plurality of second video frames according to the feature fusion information, and obtaining a target video according to a result of the similarity matching.