Shenzhen Sensetime Technology Co., Ltd.

Sun Yat-senuniversity

Ruimao Zhang

Ping Luo

Liang Lin

Xiaogang Wang

Qing Wang

Bo Jiang

Hongbin Sun

Yuying Ge

Kuanze Ren

Methods, systems, apparatus, and non-transitory computer readable storage media for image processing are provided. In one aspect, an image processing method includes: generating a garment deformation template image and a first human body template image based on a first semantic segmentation image of a human body in a first image and a target garment image, generating a target garment deformation image by performing deformation on the target garment image based on the garment deformation template image, obtaining a second human body template image by adjusting the first human body template image based on a second semantic segmentation image of the human body in the first image and the garment deformation template image, and transforming the first image into a second image including the human body wearing a target garment based on the target garment deformation image, the second human body template image, and the garment deformation template image.

Image processing method and apparatus, computer device, and storage medium

Target matching method and apparatus, electronic device, and storage medium, including: extracting feature vector of each frame in query image sequence and feature vector of each frame in candidate image sequence; determining self-expression feature vector of query image sequence, collaborative expression feature vector of the query image sequence, self-expression feature vector of candidate image sequence, and collaborative expression feature vector of candidate image sequence based on feature vector of each frame in query image sequence and feature vector of each frame in candidate image sequence; determining similarity feature vector between query image sequence and candidate image sequence based on self-expression feature vector of query image sequence, collaborative expression feature vector of query image sequence, self-expression feature vector of candidate image sequence, and collaborative expression feature vector of candidate image sequence; and determining matching result between query image sequence and candidate image sequence based on similarity feature vector.

Target matching method and apparatus, electronic device, and storage medium

This invention is an image retrieval method based on bit-scalable deep hashing learning. According to the method, the training images is used to generate a batch of image triples, wherein each of the triples contains two images with the same label and one image with a different label. The purpose of model training is to maximize a margin between matched image pairs and unmatched image pairs in the Hamming space. The deep convolutional neural network is utilized to train the model in an end-to-end fasion, where discriminative images features and has functions are simultaneously optimized. Furthermore, each bit of the hashing codes is unequally weighted so that we can manipulate the code length by truncating the insignificant bits. It is also shown that the generated bit-scalable hashing codes well preserve the discriminative powers with sorter code lengths.

Image retrieval method based on variable-length deep hash learning

Growing community of inventors

Guangdong, China

Ruimao Zhang