This paper presents our solution for the 2nd COVID-19 Competition, occurring in the framework of the AIMIA Workshop at the European Conference on Computer Vision (ECCV 2022).
In our approach, we devise a novel infection-aware 3D Contrastive Mixup Classification network for severity grading.
Previous methods typically start from frame-to-frame similarity matrix generated by cosine similarity between frame-level features of the input video pair, and then detect and refine the boundaries of copied segments on similarity matrix under temporal constraints.
However, the image-text pairs co-occurrent on the Internet typically lack explicit alignment information, which is suboptimal for VLP.
1 code implementation • • Sifeng He, Xudong Yang, Chen Jiang, Gang Liang, Wei zhang, Tan Pan, Qing Wang, Furong Xu, Chunguang Li, Jingxiong Liu, Hui Xu, Kaiming Huang, Yuan Cheng, Feng Qian, Xiaobo Zhang, Lei Yang
In this paper, we introduce VCSL (Video Copy Segment Localization), a new comprehensive segment-level annotated video copy dataset.