Search Results for author: Mouxing Yang

Found 8 papers, 6 papers with code

An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model

no code implementations13 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.

Decoupled Contrastive Multi-View Clustering with High-Order Random Walks

1 code implementation22 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.

Clustering Contrastive Learning

Semantic Invariant Multi-view Clustering with Fully Incomplete Information

1 code implementation22 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.

Clustering MULTI-VIEW LEARNING

Incomplete Multi-view Clustering via Prototype-based Imputation

no code implementations26 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.

Clustering Contrastive Learning +2

Graph Matching with Bi-level Noisy Correspondence

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).

Contrastive Learning Graph Learning +1

Twin Contrastive Learning for Online Clustering

2 code implementations21 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.

Clustering Contrastive Learning +3

Partially View-aligned Representation Learning with Noise-robust Contrastive Loss

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.

Clustering Contrastive Learning +2

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