Abstract: A target tracking method includes: obtaining feature data of a reference frame of a first image frame, wherein the first image frame and at least one second image frame have the same reference frame; and determining the position of a tracking target in the first image frame based on the feature data of the reference frame. Based on the embodiments in the present disclosure, feature data of a reference frame of a first image frame is acquired, and the position of a tracking target in the first image frame is determined based on the feature data of the reference frame.

Abstract: Provided are a target object processing method and apparatus, and a storage medium. The method includes: inputting first data into a first processing module to obtain a predicted data annotation result; inputting the data annotation result into a second processing module, and performing scene-adaptive incremental learning according to the data annotation result to obtain a neural network adapted to a scene of second data; and processing a scene corresponding to a target object according to data including the target object and the neural network.

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: Target tracking methods and apparatuses, electronic devices, and storage media are provided. The method includes: obtaining features of a plurality of reference images of a target image; determining a plurality of initial predicted positions of a tracking target in the target image based on the features of the plurality of reference images; and determining a final position of the tracking target in the target image based on the plurality of initial predicted positions.

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: Provided are a target object processing method and apparatus, and a storage medium. The method includes: inputting first data into a first processing module to obtain a predicted data annotation result; inputting the data annotation result into a second processing module, and performing scene-adaptive incremental learning according to the data annotation result to obtain a neural network adapted to a scene of second data; and processing a scene corresponding to a target object according to data including the target object and the neural network.

Abstract: A target tracking method includes: obtaining feature data of a reference frame of a first image frame, wherein the first image frame and at least one second image frame have the same reference frame; and determining the position of a tracking target in the first image frame based on the feature data of the reference frame. Based on the embodiments in the present disclosure, feature data of a reference frame of a first image frame is acquired, and the position of a tracking target in the first image frame is determined based on the feature data of the reference frame.

Abstract: Target tracking methods and apparatuses, electronic devices, and storage media are provided. The method includes: obtaining features of a plurality of reference images of a target image; determining a plurality of initial predicted positions of a tracking target in the target image based on the features of the plurality of reference images; and determining a final position of the tracking target in the target image based on the plurality of initial predicted positions.

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: 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: 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.