Mouxing Yang
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View article: LLaVA-ReID: Selective Multi-image Questioner for Interactive Person Re-Identification
LLaVA-ReID: Selective Multi-image Questioner for Interactive Person Re-Identification Open
Traditional text-based person ReID assumes that person descriptions from witnesses are complete and provided at once. However, in real-world scenarios, such descriptions are often partial or vague. To address this limitation, we introduce …
View article: Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels Open
The success of most federated learning (FL) methods heavily depends on label quality, which is often inaccessible in real-world scenarios, such as medicine, leading to the federated label-noise (F-LN) problem. In this study, we observe tha…
View article: Test-time Adaptation for Cross-modal Retrieval with Query Shift
Test-time Adaptation for Cross-modal Retrieval with Query Shift Open
The success of most existing cross-modal retrieval methods heavily relies on the assumption that the given queries follow the same distribution of the source domain. However, such an assumption is easily violated in real-world scenarios du…
View article: Decoupled Contrastive Multi-View Clustering with High-Order Random Walks
Decoupled Contrastive Multi-View Clustering with High-Order Random Walks Open
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 negativ…
View article: An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model
An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model Open
Recent studies applied Parameter Efficient Fine-Tuning techniques (PEFTs) to efficiently narrow the performance gap between pre-training and downstream. There are two important factors for various PEFTs, namely, the accessible data size an…
View article: Decoupled Contrastive Multi-View Clustering with High-Order Random Walks
Decoupled Contrastive Multi-View Clustering with High-Order Random Walks Open
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 negativ…
View article: Incomplete Multi-view Clustering via Prototype-based Imputation
Incomplete Multi-view Clustering via Prototype-based Imputation Open
In this paper, we study how to achieve two characteristics highly-expected by incomplete multi-view clustering (IMvC). Namely, i) instance commonality refers to that within-cluster instances should share a common pattern, and ii) view vers…
View article: Semantic Invariant Multi-view Clustering with Fully Incomplete Information
Semantic Invariant Multi-view Clustering with Fully Incomplete Information Open
Robust multi-view learning with incomplete information has received significant attention due to issues such as incomplete correspondences and incomplete instances that commonly affect real-world multi-view applications. Existing approache…
View article: Incomplete Multi-view Clustering via Prototype-based Imputation
Incomplete Multi-view Clustering via Prototype-based Imputation Open
In this paper, we study how to achieve two characteristics highly-expected by incomplete multi-view clustering (IMvC). Namely, i) instance commonality refers to that within-cluster instances should share a common pattern, and ii) view vers…
View article: Graph Matching with Bi-level Noisy Correspondence
Graph Matching with Bi-level Noisy Correspondence Open
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). In brief, on …