no code implementations • 13 Mar 2024 • Yuxin Tian, Mouxing Yang, Yunfan Li, Dayiheng Liu, Xingzhang Ren, Xi Peng, Jiancheng Lv
A natural expectation for PEFTs is that the performance of various PEFTs is positively related to the data size and fine-tunable parameter size.
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 • 22 May 2023 • Pengxin Zeng, Mouxing Yang, Yiding Lu, Changqing Zhang, Peng Hu, Xi Peng
To address this problem, we present a novel framework called SeMantic Invariance LEarning (SMILE) for multi-view clustering with incomplete information that does not require any paired samples.
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.
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
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.