Author name cluster

Yijie Lin

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

AAAI Conference 2026 Conference Paper

Endowing Vision-Language Models with System 2 Thinking for Fine-grained Visual Recognition

Yutong Yang
Lifu Huang
Yijie Lin
Xi Peng
Mouxing Yang

Vision-Language Models (VLMs) excel at extracting salient visual features from query images, thus exhibiting promising visual recognition performance. However, VLMs would encounter significant degradation in fine-grained scenarios due to their deficiency in distinguishing nuanced differences among candidate categories. As a remedy, we draw inspiration from the ``System 1 & System 2" cognitive theory of humans, paving the way to achieve fine-grained recognition for VLMs. To be specific, we observe that VLMs naturally align with System 1, quickly identifying candidate categories but leaving easily-confused ones unresolved. Based on the observation, we propose System-2 enhanCed visuAl recogNition (SCAN), a novel plug-and-play approach that makes VLMs aware of nuanced differences. In brief, SCAN first specifies and abstracts the discriminative attributes for the confused candidate categories and query images by resorting to off-the-shelf large foundation models, respectively. After that, SCAN adaptively integrates the salient visual features from System 1 with the nuanced differences derived from System 2, resolving confusion in candidates with estimated uncertainty. Extensive experiments on eight widely used fine-grained recognition benchmarks against 10 state-of-the-art baselines verify the effectiveness and superiority of SCAN.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Mamba-Driven Multi-View Discriminative Clustering via Global-Local Cross-View Sequence Modeling

Yuanyang Zhang
Xinhang Wan
Chao Zhang
Jie Xu
Cunjian Chen
Tien-Tsin Wong
Li Yao
Yijie Lin

Multi-view clustering (MVC) has recently garnered increasing attention for its ability to partition unlabeled samples into distinct clusters by leveraging complementary and consistent information from different views. Existing MVC methods primarily combine deep neural networks with contrastive learning for cross-view representation learning, yet often overlook the inherent global-local structural relationships among samples. While GNN-based methods capture local structures, they struggle to model global dependencies, leading to inferior inter-cluster separability. In contrast, Transformer-based methods excel at global aggregation but suffer from quadratic complexity, and their attention smoothing effect weakens fine-grained local structures, resulting in suboptimal intra-cluster compactness. To address these limitations, we propose a novel end-to-end MVC framework called Mamba-Driven Multi-View Discriminative Clustering via Global-Local Cross-View Sequence Modeling (MGLC). By flexibly constructing multi-view sequences, MGLC fully exploits the efficient sequence modeling capabilities of Mamba to jointly model cross-view dependencies and global-local structural relationships among samples. Furthermore, MGLC introduces a Cross-Mamba Fusion module to dynamically integrate cross-view and global-local structural representations. Additionally, MGLC incorporates a Dual Calibration Contrastive Learning module, guided by high-confidence pseudo-labels, that adaptively refines both feature and semantic representations while mitigating false negatives among semantically similar samples. Extensive comparative experiments and ablation studies demonstrate the effectiveness of MGLC.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Incomplete Multi-view Clustering via Diffusion Contrastive Generation

Yuanyang Zhang
Yijie Lin
Weiqing Yan
Li Yao
Xinhang Wan
Guangyuan Li
Chao Zhang
Guanzhou Ke

Incomplete multi-view clustering (IMVC) has garnered increasing attention in recent years due to the common issue of missing data in multi-view datasets. The primary approach to address this challenge involves recovering the missing views before applying conventional multi-view clustering methods. Although imputation-based IMVC methods have achieved significant improvements, they still encounter notable limitations: 1) heavy reliance on paired data for training the data recovery module, which is impractical in real scenarios with high missing data rates; 2) the generated data often lacks diversity and discriminability, resulting in suboptimal clustering results. To address these shortcomings, we propose a novel IMVC method called Diffusion Contrastive Generation (DCG). Motivated by the consistency between the diffusion and clustering processes, DCG learns the distribution characteristics to enhance clustering by applying forward diffusion and reverse denoising processes to intra-view data. By performing contrastive learning on a limited set of paired multi-view samples, DCG can align the generated views with the real views, facilitating accurate recovery of views across arbitrary missing view scenarios. Additionally, DCG integrates instance-level and category-level interactive learning to exploit the consistent and complementary information available in multi-view data, achieving robust and end-to-end clustering. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches.

PDF Details DOI

AAAI Conference 2024 Conference Paper

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

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. Although promising performance has been achieved by these methods, the false negative issue is still far from addressed and the false positive issue emerges because all in- and out-of-neighborhood samples are simply treated as positive and negative, respectively. To address the issues, we propose a novel robust method, dubbed decoupled contrastive multi-view clustering with high-order random walks (DIVIDE). In brief, DIVIDE leverages random walks to progressively identify data pairs in a global instead of local manner. As a result, DIVIDE could identify in-neighborhood negatives and out-of-neighborhood positives. Moreover, DIVIDE embraces a novel MvC architecture to perform inter- and intra-view contrastive learning in different embedding spaces, thus boosting clustering performance and embracing the robustness against missing views. To verify the efficacy of DIVIDE, we carry out extensive experiments on four benchmark datasets comparing with nine state-of-the-art MvC methods in both complete and incomplete MvC settings. The code is released on https://github.com/XLearning-SCU/2024-AAAI-DIVIDE.

PDF Details DOI

NeurIPS Conference 2024 Conference Paper

Robust Contrastive Multi-view Clustering against Dual Noisy Correspondence

Ruiming Guo
Mouxing Yang
Yijie Lin
Xi Peng
Peng Hu

Recently, contrastive multi-view clustering (MvC) has emerged as a promising avenue for analyzing data from heterogeneous sources, typically leveraging the off-the-shelf instances as positives and randomly sampled ones as negatives. In practice, however, this paradigm would unavoidably suffer from the Dual Noisy Correspondence (DNC) problem, where noise compromises the constructions of both positive and negative pairs. Specifically, the complexity of data collection and transmission might mistake some unassociated pairs as positive (namely, false positive correspondence), while the intrinsic one-to-many contrast nature of contrastive MvC would sample some intra-cluster samples as negative (namely, false negative correspondence). To handle this daunting problem, we propose a novel method, dubbed Contextually-spectral based correspondence refinery (CANDY). CANDY dexterously exploits inter-view similarities as \textit{context} to uncover false negatives. Furthermore, it employs a spectral-based module to denoise correspondence, alleviating the negative influence of false positives. Extensive experiments on five widely-used multi-view benchmarks, in comparison with eight competitive multi-view clustering methods, verify the effectiveness of our method in addressing the DNC problem. The code is available at https: //github. com/XLearning-SCU/2024-NeurIPS-CANDY.

PDF Details DOI