Abstract: The present disclosure provides a display apparatus and a display panel. The display panel includes: a drive backplate, including a substrate and a drive circuit layer on the substrate, where the drive circuit layer includes drive circuit regions arranged at intervals and transparent regions around the drive circuit regions, the transparent region includes concave parts, and a number of layers in the respective concave parts of the drive circuit layer is less than a number of layers in the respective drive circuit regions; a side of the drive circuit regions of the drive circuit layer away from the substrate includes pads for connecting a light-emitting unit; a light-transmissive glue for filling the concave parts, and a surface of the light-transmissive glue away from the substrate is on a side of the pads away from the substrate. The present disclosure can improve display effect.

Abstract: An image compression method comprises: performing feature extraction on the target image to obtain a first feature map comprising a plurality of channels; grouping the channels of the first feature map to obtain a plurality of second feature maps; performing spatial context feature extraction on the second feature maps to determine first spatial redundancy features corresponding to the second feature maps; and performing channel context feature extraction on the second feature maps to determine first channel redundancy features corresponding to the second feature maps; determining compression information corresponding to each of the second feature maps based on a first spatial redundancy feature and a first channel redundancy feature corresponding to each of the second feature maps and thus determining first compressed data corresponding to the target image, and performing deep compression processing based on the first feature map to determine second compressed data corresponding to the target image.

Abstract: A video processing method, an electronic device and a storage medium are provided. The method includes: a target frame is determined in a video to be processed; at least two first energy images corresponding to the target frame are determined based on at least two preset macro block sizes respectively, each first energy image represents alternating current energy of first macro block(s) corresponding to a respective macro block size and the first macro block(s) are obtained by segmenting the target frame based on the respective macro block size; a first energy map corresponding to the target frame is determined based on the first energy images, the first energy map represents energy distribution in the target frame; and an adaptive quantization parameter corresponding to the target frame is determined based on the first energy map and the target frame is encoded using the adaptive quantization parameter.

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.