Other

Shanghai Jiaotong University

Qin Yang

Yu Wang

Chenglin Li

Hongkai Xiong

Junni Zou

Wenrui Dai

Qijun Qian

Mengchao Wu

Haoran Lv

The present disclosure provides a saliency prediction method and system for a 360-degree image based on a graph convolutional neural network. The method includes: firstly, constructing a spherical graph signal of an image of an equidistant rectangular projection format by using a geodesic icosahedron composition method; then inputting the spherical graph signal into the proposed graph convolutional neural network for feature extraction and generation of a spherical saliency graph signal; and then reconstructing the spherical saliency graph signal into a saliency map of an equidistant rectangular projection format by using a proposed spherical crown based interpolation algorithm. The present disclosure further proposes a KL divergence loss function with sparse consistency. The method can achieve excellent saliency prediction performance subjectively and objectively, and is superior to an existing method in computational complexity.

Saliency prediction method and system for 360-degree image

The present invention provides a recombinant virus containing a nucleotide sequence encoding a tumor-therapeutic full-length antibody with human constant regions, and uses thereof. After a nucleotide sequence of a gene encoding a tumor-therapeutic full-length antibody with human constant regions of the light chain and the heavy chain is inserted into the genome of a recombinant virus, the tumor-therapeutic full-length antibody with human constant regions can be efficiently expressed in tumor cells, thereby inhibit the growth and metastasis of tumors.

Recombinant virus comprising an intact tumor-therapeutic antibody with human constant regions and the use thereof

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Shanghai, China

Qin Yang