Abstract: This application relates to a video compression and video decompression that includes: generating, based on a key frame and a forward search frame of a video, a pixel kernel of each pixel in the key frame; performing smoothness constraint processing on the pixel kernel, to obtain a target pixel kernel; compressing the key frame and the target pixel kernel, to obtain a compressed key frame and a compressed pixel kernel; compressing a residual graph between the forward search frame and a predicted frame, to obtain a compressed graph, the predicted frame being a video frame generated based on the target pixel kernel and the key frame; and obtaining a compressed video packet according to the compressed graph, the compressed key frame, and the compressed pixel kernel.

Abstract: This application discloses a video compression method, a video decoding method, and related apparatuses. The method includes: extracting a key point from a to-be-processed video frame and a previous video frame respectively to obtain first position information and second position information; performing motion estimation based on the first position information and the second position information to obtain motion information; performing image inpainting based on the motion information and the previous video frame to obtain an initial video frame; determining a latent feature based on the to-be-processed video frame and the initial video frame; and performing video compression based on the first position information, the second position information, and the latent feature to obtain a video compressed file.

Abstract: This application discloses a method for determining a lesion region, and a model training method and apparatus, and relates to the field of computer vision technologies. The method includes the following steps: sampling a pathological image by a first sampling way to obtain at least two first instance images (310); determining a candidate lesion region in the pathological image, based on feature information extracted from the at least two first instance images (320); sampling the candidate lesion region by a second sampling way to obtain at least two second instance images, where an overlap degree between the second instance images is greater than that between the first instance images (330); and determining lesion indication information of the pathological image, based on feature information extracted from the at least two second instance images, where the lesion indication information is used for indicating the lesion region in the pathological image (340).

Abstract: Provided is a method for searching for a whole slide image performed by a computer device, which relate to the field of artificial intelligence. The method includes: cropping a whole slide image into a plurality of tissue images; generating, through an image encoder, image feature vectors respectively corresponding to the plurality of tissue images; clustering the image feature vectors respectively corresponding to the plurality of tissue images, to determine at least one key image from the plurality of tissue images; querying, based on image feature vectors respectively corresponding to the at least one key image, a database to obtain at least one target image package corresponding to the at least one key image; and determining a whole slide image to which at least one candidate tissue image comprised in the at least one target image package respectively belongs as a final search result.

Abstract: A whole slide image (WSI) search method is performed by a computer device, which belong to the field of artificial intelligence. The method includes: acquiring a plurality of tissue images obtained by cropping a WSI; inputting the plurality of tissue images into an image encoder to obtain image feature vectors respectively corresponding to the plurality of tissue images; determining at least one key image from the image feature vectors respectively corresponding to the plurality of tissue images; querying, based on image feature vectors respectively corresponding to the at least one key image, a database for at least one candidate image package respectively corresponding to the at least one key image; and determining at least one screened image package from the at least one candidate image package according to attributes of the at least one candidate image package as search results corresponding to the WSI.

Abstract: A method for training an image recognition model is performed by a computer device. The method includes: obtaining a sample image and a corresponding sample label; obtaining a sample image patch bag of sample image patches corresponding to the sample image, the sample patch bag having a bag label corresponding to the sample label of the sample image; performing feature analysis on the sample patch bag and the sample image patches in the sample patch bag, respectively, by using an image recognition model; determining a relative entropy loss, a first cross entropy loss corresponding to the sample patch bag and a second cross entropy loss corresponding to the sample image patches based on corresponding bag feature analysis and patch analysis results, respectively; and training the image recognition model based on the relative entropy loss, the first cross entropy loss, and the second cross entropy loss.

Abstract: An image detection method and apparatus are disclosed.