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Shide Du

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7 papers

1 author row

AAAI Conference 2026 Conference Paper

DIN: Dual Impulse Network for Multi-view Representation Learning

Yilin Wu
Weihong Lin
Renjie Lin
Zihan Fang
Shide Du
Shiping Wang

Multi-view representation learning, which utilizes multiple channels to improve perceptual accuracy, is recognized for its effectiveness in the analysis of multi-view data. However, deploying these methods in real-world scenarios presents two primary challenges. 1) Lack of Variegation: Multi-view representation techniques commonly observe along a singular axis, i.e., the attribute axis; 2) Insufficient Relationship: Most multi-view models lack mechanisms for exploring potential relationships between attribute axis and channel axis. To mitigate these obstacles, we design a Dual Impulse Network framework for multi-view representation learning (DIN) to train a feature representation. In this framework, a strategy observed along the channel axis and attribute axis simultaneously is introduced, and two different representations are generated by two analogous impulse networks, which are capable of extracting information corresponding to different axes. Furthermore, we incorporate an integration network that analyzes the potential relationship between attribute axis and channel axis to generate two attention matrices. The final two feature representations derived from these attention matrices are aggregated to amplify the expression of internal information. Comprehensive experimental results support the efficacy and superiority of the proposed framework, demonstrating improvements in classification performance compared to state-of-the-art methods.

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AAAI Conference 2026 Conference Paper

From Static to Active: Knowledge-Aware Node State Selection in Multi-view Graph Learning

Weiran Liao
Jielong Lu
Yuhong Chen
Shide Du
Hongrong Chen
Shiping Wang

Multimedia technologies leverage multi-source to alleviate real-world data incompleteness, providing a versatile platform for multi-view learning. Among existing research, graph-based multi-view learning has achieved notable success. However, prior studies always immerse in comprehensive collaboration across all views and nodes to pursue consistency and complementary, which ignore the negative contribution of nodes from low-quality views. To overcome the above limitation, we explore node behavior selection in multi-view dynamic modeling and propose a knowledge-aware multi-view state space model. Specifically, nodes autonomously select either activation sequences or static sequences according to their current knowledge. In the former, we design the mask-based attention mechanism to capture the dynamics of node behaviors. In the latter, we construct a history pool and simulate synaptic signals to regulate the behavioral distribution of nodes. Moreover, the proposed model provides a directional inter-view diffusion equation that selectively propagates information to alleviate interference from low-quality nodes across views. Extensive experiments demonstrate that the proposed model outperforms baselines on multiple benchmarks and achieves significant performance improvement.

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AAAI Conference 2026 Conference Paper

Graph Meets Deep Unfolding: An Interpretable Mutual-benefit Multi-view Learning Network

Renjie Lin
Hongzhi He
Yilin Wu
Shide Du
Le Zhang

Significant efforts have been focused on enhancing the utilization of multiple node features and topological structures in multi-view graph learning through explicit model-driven and implicit deep learning-based methodologies. The former excels in embedding prior knowledge, thereby offering theoretical interpretability but is limited in application flexibility due to manual parameter selection. In contrast, the latter leverages automatic differentiation, providing greater flexibility but lacking theoretical interpretability due to their opaque nature. Motivated by these observations, we propose an interpretable deep unfolding network for mutual-benefit multi-view graph learning, aiming to combine the strengths of both approaches. Specifically, we employ the Alternating Direction Method of Multipliers (ADMM) to solve a multi-view graph learning model with sparse and low-rank constraints. This solution is then integrated into deep unfolding networks to enhance interpretability. Furthermore, we convert optimization conditions into implicit losses and utilize automatic differentiation to update parameters, reducing the need for manual tuning and increasing flexibility. This integration optimizes multi-view learning for a graph representation that balances interpretability and flexibility. Empirical evaluations on six diverse datasets demonstrate the effectiveness and superiority of the proposed method over state-of-the-art approaches.

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EAAI Journal 2025 Journal Article

Dual prototypes contrastive learning based semi-supervised segmentation method for intelligent medical applications

Tianai Yue
Rongtao Xu
Jingqian Wu
Wenjie Yang
Shide Du
Changwei Wang

In medical intelligence applications, the labeling of medical data is crucial and expensive, so it becomes urgent to explore labeling-efficient ways to train applications. Semi-supervised techniques for medical image segmentation have demonstrated potential, effectively training models using scarce labeled data alongside a wealth of unlabeled data. Therefore, semi-supervised medical image segmentation is a key issue in engineering applications of medical intelligence. Consistency constraints based on prototype alignment provide an intuitively sensible way to discover valuable insights from unlabeled data that can motivate segmentation performance. In this work, we propose a Dual prototypes Contrastive Network to motivate semi-supervised medical segmentation accuracy by imposing image-level global prototype and pixel-level local prototype constraints. First, we introduce a Background-Separation Global Prototype Contrastive Learning technique that utilizes the natural mutual exclusivity of foreground and background to separate the inter-class distances and encourage the segmentation network to obtain segmentation results that are more complete and do not contain background regions. Second, we design a Cross-Consistent Local Prototype Contrastive Learning techniques to extend the perturbation consistency of the two networks to the prototype’s localized response to the feature map, thereby shaping a more stable intra-class prototype space and producing accurate and robust pixel-level predictions. Finally, we comprehensively evaluate our method on mainstream semi-supervised medical image segmentation benchmarks and settings, and experimental results show that our proposed method outperforms current state-of-the-art methods. Specifically, our method achieves a Dice Coefficient score of 91. 8 on the Automatic Cardiac Diagnosis Challenge dataset using only 10% labeled data training, 1. 1% ahead of the second best method. Code is available at https: //github. com/yuelily2024/DPC.

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AAAI Conference 2025 Conference Paper

Focus on Local: Finding Reliable Discriminative Regions for Visual Place Recognition

Changwei Wang
Shunpeng Chen
Yukun Song
Rongtao Xu
Zherui Zhang
Jiguang Zhang
Haoran Yang
Yu Zhang

Visual Place Recognition (VPR) is aimed at predicting the location of a query image by referencing a database of geotagged images. For VPR task, often fewer discriminative local regions in an image produce important effects while mundane background regions do not contribute or even cause perceptual aliasing because of easy overlap. However, existing methods lack precisely modeling and full exploitation of these discriminative regions. In addition, the lack of pixel-level correspondence supervision in the VPR dataset hinders further improvement of the local feature matching capability in the re-ranking stage. In this paper, we propose the Focus on Local (FoL) approach to stimulate the performance of image retrieval and re-ranking in VPR simultaneously by mining and exploiting reliable discriminative local regions in images and introducing pseudo-correlation supervision. First, we design two losses, Extraction-Aggregation Spatial Alignment Loss (SAL) and Foreground-Background Contrast Enhancement Loss (CEL), to explicitly model reliable discriminative local regions and use them to guide the generation of global representations and efficient re-ranking. Second, we introduce a weakly-supervised local feature training strategy based on pseudo-correspondences obtained from aggregating global features to alleviate the lack of local correspondences ground truth for the VPR task. Third, we suggest an efficient re-ranking pipeline that is efficiently and precisely based on discriminative region guidance. Finally, experimental results show that our FoL achieves the state-of-the-art on multiple VPR benchmarks in both image retrieval and re-ranking stages and also significantly outperforms existing two-stage VPR methods in terms of computational efficiency.

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IJCAI Conference 2025 Conference Paper

HiTuner: Hierarchical Semantic Fusion Model Fine-Tuning on Text-Attributed Graphs

Zihan Fang
Zhiling Cai
Yuxuan Zheng
Shide Du
Yanchao Tan
Shiping Wang

Text-Attributed Graphs (TAGs) are vital for modeling entity relationships across various domains. Graph Neural Networks have become cornerstone for processing graph structures, while the integration of text attributes remains a prominent research. The development of Large Language Models (LLMs) provides new opportunities for advancing textual encoding in TAGs. However, LLMs face challenges in specialized domains due to their limited task-specific knowledge, and fine-tuning them for specific tasks demands significant resources. To cope with the above challenges, we propose HiTuner, a novel framework that leverages fine-tuned Pre-trained Language Models (PLMs) with domain expertise as tuner to enhance the hierarchical LLM contextualized representations for modeling TAGs. Specifically, we first strategically select hierarchical hidden states of LLM to form a set of diverse and complementary descriptions as input for the sparse projection operator. Concurrently, a hybrid representation learning is developed to amalgamate the broad linguistic comprehension of LLMs with task-specific insights of the fine-tuned PLMs. Finally, HiTuner employs a confidence network to adaptively fuse the semantically-augmented representations. Empirical results across benchmark datasets spanning various domains validate the effectiveness of the proposed framework. Our codes are available at: https: //github. com/ZihanFang11/HiTuner

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AAAI Conference 2025 Conference Paper

OpenViewer: Openness-Aware Multi-View Learning

Shide Du
Zihan Fang
Yanchao Tan
Changwei Wang
Shiping Wang
Wenzhong Guo

Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained; 2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding Network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples.

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