no code implementations • 26 Jan 2023 • Haobin Li, Yunfan Li, Mouxing Yang, Peng Hu, Dezhong Peng, Xi Peng
Thanks to our dual-stream model, both cluster- and view-specific information could be captured, and thus the instance commonality and view versatility could be preserved to facilitate IMvC.
no code implementations • 8 Dec 2022 • 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
2 code implementations • 21 Oct 2022 • Yunfan Li, Mouxing Yang, Dezhong Peng, Taihao Li, Jiantao Huang, Xi Peng
Specifically, we find that when the data is projected into a feature space with a dimensionality of the target cluster number, the rows and columns of its feature matrix correspond to the instance and cluster representation, respectively.
Ranked #1 on Short Text Clustering on Biomedical
1 code implementation • CVPR 2022 • Mouxing Yang, Zhenyu Huang, Peng Hu, Taihao Li, Jiancheng Lv, Xi Peng
To solve the TNL problem, we propose a novel method for robust VI-ReID, termed DuAlly Robust Training (DART).
1 code implementation • CVPR 2021 • Mouxing Yang, Yunfan Li, Zhenyu Huang, Zitao Liu, Peng Hu, Xi Peng
To solve such a less-touched problem without the help of labels, we propose simultaneously learning representation and aligning data using a noise-robust contrastive loss.
Contrastive Learning Partially View-aligned Multi-view Learning +1