TENCENT TECHNOLOGY (SHENZHEN) COMPANY LTD

Embodiments of the disclosure provide an image processing method and apparatus, a computer-readable medium, and an electronic device. The image processing method includes: extracting a feature map of a target image; dividing the feature map into target regions; determining weights of the target regions according to feature vectors of the target regions; and generating a feature vector of the target image according to the weights of the target regions and the feature vectors of the target regions.

A method for recognizing a key time point in a video includes: obtaining at least one video segment by processing each image frame in the video by an image classification model; determining a target video segment in the at least one video segment based on a shot type; obtaining respective locations of a first object and a second object in an image frame of the target video segment by an image detection model; and based on a distance between the location of the first object and the location of the second object in the image frame satisfying a preset condition, determining a time point of the image frame as the key time point of the video.

An image recognition method, an apparatus, an electronic device, and a storage medium. The method may include extracting input matrices from a target image through input channels of a convolutional layer in a convolutional neural network model, obtaining first and second result matrices by traversing input matrices and performing input transformation and convolution kernel transformation, respectively, on the traversed input matrices through the row transformation reconstruction, performing matrix multiplication reconstruction on the first and second result matrices to obtain to-be-transformed matrices, performing output transformation on the to-be-transformed matrices through the row transformation reconstruction to obtain output matrices, splicing the output matrices and outputting the spliced output matrices through output channels of the convolutional layer to obtain a convolution result, and obtaining an image recognition result based on the convolution result into a pooling layer in the convolutional neural network model to perform an image recognition on the target image.

An image processing method includes: obtaining a target image; performing feature extraction on the target image based on a residual network, to obtain image feature information; and performing recognition processing on the target image according to the image feature information. The residual network includes a plurality of residual blocks that are successively connected, each of the residual blocks including a convolution branch and a residual branch, a size of a convolution kernel of a first convolutional layer in the convolution branch being less than a size of a convolution kernel of a second convolutional layer located after the first convolutional layer, and a convolution stride of the second convolutional layer being greater than a convolution stride of the first convolutional layer and less than a width of the convolution kernel of the second convolutional layer.

Provided is a video processing method, including: obtaining a to-be-processed video and generating a first gait energy diagram, the to-be-processed video including an object with a to-be-recognized identity; obtaining a second gait energy diagram, the second gait energy diagram being generated based on a video including an object with a known identity; inputting the first gait energy diagram and the second gait energy diagram into a deep neural network; extracting respective identity information of the first gait energy diagram and the second gait energy diagram, and determining a fused gait feature vector from gait feature vectors of the first gait energy diagram and the second gait energy diagram; and calculating a similarity based on at least the fused gait feature vector. The identity information of the first gait energy diagram includes gait feature vectors, and the identity information of the second gait energy diagram includes gait feature vectors.

An image processing method performed by a computing device deployed with a deep-learning neural network is provided. An image, including an object to be segmented from the image, is acquired. The object is segmented from the image by using the deep-learning neural network to acquire a first segmentation result. Correction information input by a user with respect to the first segmentation result is acquired. Based on correction information, the first segmentation result is modified by using the deep-learning neural network, to acquire a second segmentation result.

Embodiments of the disclosure provide an image processing method and apparatus, a computer-readable medium, and an electronic device. The image processing method includes: extracting a feature map of a target image; dividing the feature map into target regions; determining weights of the target regions according to feature vectors of the target regions; and generating a feature vector of the target image according to the weights of the target regions and the feature vectors of the target regions.