Abstract: A video processing method includes: obtaining a first video frame and a second video frame in a target video, the first video frame being a previous frame of the second video frame; inputting the first video frame and the second video frame to a target neural network, and obtaining a target intermediate video frame output by the target neural network, the target neural network being trained based on an optical flow distillation constraint and a feature consistency constraint; and interpolating the target intermediate video frame between the first video frame and the second video frame.

Abstract: The present subject matter discloses an action recognition method, apparatus and device, a storage medium, and a computer program product, belonging to the field of image recognition. Multiple video frames in a target video are obtained. Feature extraction is performed on the multiple video frames respectively according to multiple dimensions to obtain multiple multi-channel feature patterns. Each video frame corresponds to one multi-channel feature pattern. Each channel represents one dimension. An attention weight of each multi-channel feature pattern is determined based on a similarity between every two multi-channel feature patterns. The attention weight is used for representing a degree of correlation between a corresponding multi-channel feature pattern and an action performed by an object in the target video. A type of the action is determined based on the multiple multi-channel feature patterns and the determined multiple attention weights.

Abstract: The present disclosure relates to a video processing method and an apparatus, an electronic device, and a storage medium. The method includes: performing a feature extraction on a plurality of target video frames of a video to be processed through a feature extraction network, to obtain feature maps of the plurality of target video frames; performing an action recognition process on the feature maps of the plurality of target video frames through an M-level action recognition network, to obtain action recognition features of the plurality of target video frames; and determining a classification result of the video to be processed according to the action recognition features of the plurality of target video frames.