1 code implementation • 30 Jan 2024 • Yijie Lin, Jie Zhang, Zhenyu Huang, Jia Liu, Zujie Wen, Xi Peng
Existing video-language studies mainly focus on learning short video clips, leaving long-term temporal dependencies rarely explored due to over-high computational cost of modeling long videos.
Ranked #1 on Zero-Shot Video Retrieval on YouCook2
Action Segmentation Long Video Retrieval (Background Removed) +2
1 code implementation • 22 Aug 2023 • Yiding Lu, Yijie Lin, Mouxing Yang, Dezhong Peng, Peng Hu, Xi Peng
In recent, some robust contrastive multi-view clustering (MvC) methods have been proposed, which construct data pairs from neighborhoods to alleviate the false negative issue, i. e., some intra-cluster samples are wrongly treated as negative pairs.
1 code implementation • IEEE Transactions on Pattern Analysis and Machine Intelligence 2023 • Yijie Lin, Yuanbiao Gou, Xiaotian Liu, Jinfeng Bai, Jiancheng Lv, Xi Peng
In this article, we propose a unified framework to solve the following two challenging problems in incomplete multi-view representation learning: i) how to learn a consistent representation unifying different views, and ii) how to recover the missing views.
3 code implementations • ICCV 2023 • Yijie Lin, Mouxing Yang, Jun Yu, Peng Hu, Changqing Zhang, Xi Peng
In this paper, we study a novel and widely existing problem in graph matching (GM), namely, Bi-level Noisy Correspondence (BNC), which refers to node-level noisy correspondence (NNC) and edge-level noisy correspondence (ENC).
Ranked #1 on Graph Matching on Willow Object Class
no code implementations • 14 Jul 2021 • Boyun Li, Yijie Lin, Xiao Liu, Peng Hu, Jiancheng Lv, Xi Peng
To generate plausible haze, we study two less-touched but challenging problems in hazy image rendering, namely, i) how to estimate the transmission map from a single image without auxiliary information, and ii) how to adaptively learn the airlight from exemplars, i. e., unpaired real hazy images.
2 code implementations • CVPR 2021 • Yijie Lin, Yuanbiao Gou, Zitao Liu, Boyun Li, Jiancheng Lv, Xi Peng
In this paper, we study two challenging problems in incomplete multi-view clustering analysis, namely, i) how to learn an informative and consistent representation among different views without the help of labels and ii) how to recover the missing views from data.
Ranked #1 on Incomplete multi-view clustering on n-MNIST