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Mulin Chen

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7 papers

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AAAI Conference 2026 Conference Paper

Adaptive Evidential Learning for Temporal-Semantic Robustness in Moment Retrieval

Haojian Huang
Kaijing Ma
Jin Chen
Haodong Chen
Zhou Wu
Xianghao Zang
Han Fang
Chao Ban

In the domain of moment retrieval, accurately identifying temporal segments within videos based on natural language queries remains challenging. Traditional methods often employ pre-trained models that struggle with fine-grained information and deterministic reasoning, leading to difficulties in aligning with complex or ambiguous moments. To overcome these limitations, we explore Deep Evidential Regression (DER) to construct a vanilla Evidential baseline. However, this approach encounters two major issues: the inability to effectively handle modality imbalance and the structural differences in DER's heuristic uncertainty regularizer, which adversely affect uncertainty estimation. This misalignment results in high uncertainty being incorrectly associated with accurate samples rather than challenging ones. Our observations indicate that existing methods lack the adaptability required for complex video scenarios. In response, we propose Debiased Evidential Learning for Moment Retrieval (DEMR), a novel framework that incorporates a Reflective Flipped Fusion (RFF) block for cross-modal alignment and a query reconstruction task to enhance text sensitivity, thereby reducing bias in uncertainty estimation. Additionally, we introduce a Geom-regularizer to refine uncertainty predictions, enabling adaptive alignment with difficult moments and improving retrieval accuracy. Extensive testing on standard datasets and debiased datasets ActivityNet-CD and Charades-CD demonstrates significant enhancements in effectiveness, robustness, and interpretability, positioning our approach as a promising solution for temporal-semantic robustness in moment retrieval.

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IJCAI Conference 2025 Conference Paper

Multi-Task Curriculum Graph Contrastive Learning with Clustering Entropy Guidance

Chusheng Zeng
Bocheng Wang
Jinghui Yuan
Mulin Chen
Xuelong Li

Recent advances in unsupervised deep graph clustering have been significantly promoted by contrastive learning. Despite the strides, most graph contrastive learning models face challenges: 1) graph augmentation is used to improve learning diversity, but commonly used random augmentation methods may destroy inherent semantics and cause noise; 2) the fixed positive and negative sample selection strategy ignores the difficulty distribution of samples when deal with complex real data, thereby impeding the model’s capability to capture fine-grained patterns and trapping the model in sub-optimal for clustering. To reduce these problems, we propose the Clustering-guided Curriculum Graph contrastive Learning (CurGL) framework. CurGL uses clustering entropy as the guidance of the following graph augmentation and contrastive learning. Specifically, according to the clustering entropy, the intra-class edges and important features are emphasized in augmentation. Then, a multi-task curriculum learning scheme is proposed, which employs the clustering guidance to shift the focus from the discrimination task to the clustering task. In this way, the sample selection strategy of contrastive learning can be adjusted adaptively from early to late stage, which enhances the model's flexibility for complex data structure. Experimental results demonstrate that CurGL has achieved excellent performance compared to state-of-the-art competitors.

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AAAI Conference 2025 Conference Paper

Towards Learnable Anchor for Deep Multi-View Clustering

Bocheng Wang
Chusheng Zeng
Mulin Chen
Xuelong Li

Deep multi-view clustering incorporating graph learning has presented tremendous potential. Most methods encounter costly square time consumption w.r.t. data size. Theoretically, anchor-based graph learning can alleviate this limitation, but related deep models mainly rely on manual discretization approaches to select anchors, which indicates that 1) the anchors are fixed during model training and 2) they may deviate from the true cluster distribution. Consequently, the unreliable anchors may corrupt clustering results. In this paper, we propose the Deep Multi-view Anchor Clustering (DMAC) model that performs clustering in linear time. Concretely, the initial anchors are intervened by the positive-incentive noise sampled from Gaussian distribution, such that they can be optimized with a newly designed anchor learning loss, which promotes a clear relationship between samples and anchors. Afterwards, anchor graph convolution is devised to model the cluster structure formed by the anchors, and the mutual information maximization loss is built to provide cross-view clustering guidance. In this way, the learned anchors can better represent clusters. With the optimal anchors, the full sample graph is calculated to derive a discriminative embedding for clustering. Extensive experiments on several datasets demonstrate the superior performance and efficiency of DMAC compared to state-of-the-art competitors.

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AAAI Conference 2024 Conference Paper

Deep Contrastive Graph Learning with Clustering-Oriented Guidance

Mulin Chen
Bocheng Wang
Xuelong Li

Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering. To handle the general clustering scenario without a prior graph, these models estimate an initial graph beforehand to apply GCN. Throughout the literature, we have witnessed that 1) most models focus on the initial graph while neglecting the original features. Therefore, the discriminability of the learned representation may be corrupted by a low-quality initial graph; 2) the training procedure lacks effective clustering guidance, which may lead to the incorporation of clustering-irrelevant information into the learned graph. To tackle these problems, the Deep Contrastive Graph Learning (DCGL) model is proposed for general data clustering. Specifically, we establish a pseudo-siamese network, which incorporates auto-encoder with GCN to emphasize both the graph structure and the original features. On this basis, feature-level contrastive learning is introduced to enhance the discriminative capacity, and the relationship between samples and centroids is employed as the clustering-oriented guidance. Afterward, a two-branch graph learning mechanism is designed to extract the local and global structural relationships, which are further embedded into a unified graph under the cluster-level contrastive guidance. Experimental results on several benchmark datasets demonstrate the superiority of DCGL against state-of-the-art algorithms.

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AAAI Conference 2017 Conference Paper

A Multiview-Based Parameter Free Framework for Group Detection

Xuelong Li
Mulin Chen
Feiping Nie
Qi Wang

Group detection is fundamentally important for analyzing crowd behaviors, and has attracted plenty of attention in arti- ﬁcial intelligence. However, existing works mostly have limitations due to the insufﬁcient utilization of crowd properties and the arbitrary processing of individuals. In this paper, we propose the Multiview-based Parameter Free (MPF) approach to detect groups in crowd scenes. The main contributions made in this study are threefold: (1) a new structural context descriptor is designed to characterize the structural property of individuals in crowd motions; (2) an selfweighted multiview clustering method is proposed to cluster feature points by incorporating their motion and context similarities; (3) a novel framework is introduced for group detection, which is able to determine the group number automatically without any parameter or threshold to be tuned. Extensive experiments on various real world datasets demonstrate the effectiveness of the proposed approach, and show its superiority against state-of-the-art group detection techniques.

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IJCAI Conference 2017 Conference Paper

Locality Adaptive Discriminant Analysis

Xuelong Li
Mulin Chen
Feiping Nie
Qi Wang

Linear Discriminant Analysis (LDA) is a popular technique for supervised dimensionality reduction, and its performance is satisfying when dealing with Gaussian distributed data. However, the neglect of local data structure makes LDA inapplicable to many real-world situations. So some works focus on the discriminant analysis between neighbor points, which can be easily affected by the noise in the original data space. In this paper, we propose a new supervised dimensionality reduction method, Locality Adaptive Discriminant Analysis (LADA), to lean a representative subspace of the data. Compared to LDA and its variants, the proposed method has three salient advantages: (1) it finds the principle projection directions without imposing any assumption on the data distribution; (2) it’s able to exploit the local manifold structure of data in the desired subspace; (3) it exploits the points’ neighbor relationship automatically without introducing any additional parameter to be tuned. Performance on synthetic datasets and real-world benchmark datasets demonstrate the superiority of the proposed method.

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AAAI Conference 2017 Conference Paper

Quantifying and Detecting Collective Motion by Manifold Learning

Qi Wang
Mulin Chen
Xuelong Li

The analysis of collective motion has attracted many researchers in artiﬁcial intelligence. Though plenty of works have been done on this topic, the achieved performance is still unsatisfying due to the complex nature of collective motions. By investigating the similarity of individuals, this paper proposes a novel framework for both quantifying and detecting collective motions. Our main contributions are threefold: (1) the time-varying dynamics of individuals are deeply investigated to better characterize the individual motion; (2) a structure-based collectiveness measurement is designed to precisely quantify both individual-level and scene-level properties of collective motions; (3) a multi-stage clustering strategy is presented to discover a more comprehensive understanding of the crowd scenes, containing both local and global collective motions. Extensive experimental results on real world data sets show that our method is capable of handling crowd scenes with complicated structures and various dynamics, and demonstrate its superior performance against state-of-the-art competitors.

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