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Xiaoyan Yu

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

Disentangled Hypergraph-Guided Mamba Scanning for Fine-Grained Visual Recognition

Zhongwei Xiong
Hao Wang
Xiaoyan Yu
Lingling Li
Xuezhuan Zhao
Taisong Jin

Fine-grained Visual Recognition (FGVR) aims to distinguish between categories with subtle inter-class differences and large intra-class variations. While Vision Transformers with attention mechanisms have been widely adopted for FGVR, they usually suffer from high computational complexity and entangled global representations. Recent advancements in state-space models, exemplified by Mamba, have showcased substantial potential in vision-related tasks due to their linear scalability and rich sequence modeling capacity. To this end, we propose DHMamba, a novel Mamba based FGVR method. The proposed method leverages hypergraph to guide selective scanning and strengthen Mamba’s capability in modeling fine-grained semantics. Furthermore, a Disentangled Local Scanning (DLS) module is introduced to utilize hyperedges to allocate distinct informative patches into independent channels for mitigating the representational entanglement. Extensive experiments conducted on multiple FGVR benchmarks demonstrate that the proposed DHMamba outperforms the state-of-the-art methods, validating the efficacy of combining state-space modeling with hypergraph-based feature structuring.

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AAAI Conference 2026 Conference Paper

Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection

Li Sun
Lanxu Yang
Jiayu Tian
Bowen Fang
Xiaoyan Yu
Junda Ye
Peng Tang
Hao Peng

Detecting Out-of-Distribution (OOD) graphs—those are drawn from a different distribution from the training data-is a critical task for ensuring the safety and reliability of Graph Neural Networks. The main challenge in unsupervised graph-level Out-of-Distribution detection lies in its common reliance on purely in-distribution (ID) data. This ID-only training paradigm leads to an incomplete characterization of the feature space, resulting in decision boundaries that lack the robustness needed to effectively separate ID from OOD samples. While incorporating synthesized outliers into the training process is a promising direction, existing generation methods are limited by their dependence on pre-defined, non-adaptive sampling heuristics (e.g., distance- or density-based). Such fixed strategies lack the flexibility to systematically explore the most informative OOD regions for refining decision boundaries. To overcome this limitation, we propose a novel Policy-Guided Outlier Synthesis (PGOS) framework that replaces static heuristics with a learned, adaptive exploration policy. PGOS trains a reinforcement learning agent to autonomously navigate low-density regions within a structured latent space, sampling representations that are maximally effective for regularizing the OOD decision boundary. These sampled points are then decoded into high-quality pseudo-OOD graphs to enhance the detector's robustness. Extensive experiments demonstrate the strong performance of our method, state-of-the-art results on multiple graph OOD and anomaly detection benchmarks.

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AAAI Conference 2026 Conference Paper

Structural Entropy Guided Incremental Learning for Open-World Multimodal Social Event Detection

Zhiwei Yang
Haimei Qin
Xiaoyan Yu
Hao Peng
Lei Jiang
Li Sun
Zhiqin Yang

With the explosive growth of multimodal data streams on social media, the timely detection of emerging social events has become increasingly important. As a result, Multimodal Social Event Detection in open-world settings is receiving growing attention. However, most existing methods face two major limitations: (1) They overlook the dynamic nature of open-world social media data and fail to design dedicated incremental learning frameworks. (2) They ignore the impact of noise in streaming data, leading to performance degradation over long-term detection. To overcome these limitations, we propose SeInEvent (**S**tructural **E**ntropy Guided **In**cremental Learning for Open-World Multimodal Social **Event** Detection). Our innovations are as follows: **First**, considering data dynamics, we design a self-supervised alternating incremental contrastive learning mechanism. Through knowledge distillation, historical event clusters were reviewed and consolidated, and contrastive learning was combined to absorb knowledge of unknown events, ultimately achieving incremental learning without labels. **Second**, addressing the impact of noise, we propose a Pointwise Structural Entropy-based noise filter, which quantifies each sample’s informational contribution to the event clustering structure. It enables automatic removal of noisy data and supports robust long-term detection. Extensive experiments on two public datasets demonstrate that SeInEvent achieves superior performance.

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IJCAI Conference 2025 Conference Paper

SetKE: Knowledge Editing for Knowledge Elements Overlap

Yifan Wei
Xiaoyan Yu
Ran Song
Hao Peng
Angsheng Li

Large Language Models (LLMs) excel in tasks such as retrieval and question answering but require updates to incorporate new knowledge and reduce inaccuracies and hallucinations. Traditional updating methods, like fine-tuning and incremental learning, face challenges such as overfitting and high computational costs. Knowledge Editing (KE) provides a promising alternative but often overlooks the Knowledge Element Overlap (KEO) phenomenon, where multiple triplets share common elements, leading to editing conflicts. We identify the prevalence of KEO in existing KE datasets and show its significant impact on current KE methods, causing performance degradation in handling such triplets. To address this, we propose a new formulation, Knowledge Set Editing (KSE), and introduce SetKE, a method that edits sets of triplets simultaneously. Experimental results demonstrate that SetKE outperforms existing methods in KEO scenarios on mainstream LLMs. Additionally, we introduce EditSet, a dataset containing KEO triplets, providing a comprehensive benchmark.

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NeurIPS Conference 2025 Conference Paper

Structural Entropy Guided Agent for Detecting and Repairing Knowledge Deficiencies in LLMs

Yifan Wei
Xiaoyan Yu
Tengfei Pan
Angsheng Li
Li Du

Large language models (LLMs) have achieved unprecedented performance by leveraging vast pretraining corpora, yet their performance remains suboptimal in knowledge-intensive domains such as medicine and scientific research, where high factual precision is required. While synthetic data provides a promising avenue for augmenting domain knowledge, existing methods frequently generate redundant samples that do not align with the model’s true knowledge gaps. To overcome this limitation, we propose a novel Structural Entropy-guided Knowledge Navigator (SENATOR) framework that addresses the intrinsic knowledge deficiencies of LLMs. Our approach employs the Structure Entropy (SE) metric to quantify uncertainty along knowledge graph paths and leverages Monte Carlo Tree Search (MCTS) to selectively explore regions where the model lacks domain-specific knowledge. Guided by these insights, the framework generates targeted synthetic data for supervised fine-tuning, enabling continuous self-improvement. Experimental results on LLaMA-3 and Qwen2 across multiple domain-specific benchmarks show that SENATOR effectively detects and repairs knowledge deficiencies, achieving notable performance improvements.

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AAAI Conference 2025 Conference Paper

Towards Effective, Efficient and Unsupervised Social Event Detection in the Hyperbolic Space

Xiaoyan Yu
Yifan Wei
Shuaishuai Zhou
Zhiwei Yang
Li Sun
Hao Peng
Liehuang Zhu
Philip S. Yu

The vast, complex, and dynamic nature of social message data has posed challenges to social event detection (SED). Despite considerable effort, these challenges persist, often resulting in inadequately expressive message representations (ineffective) and prolonged learning durations (inefficient). In response to the challenges, this work introduces an unsupervised framework, HyperSED (Hyperbolic SED). Specifically, the proposed framework first models social messages into semantic-based message anchors, and then leverages the structure of the anchor graph and the expressiveness of the hyperbolic space to acquire structure- and geometry-aware anchor representations. Finally, HyperSED builds the partitioning tree of the anchor message graph by incorporating differentiable structural information as the reflection of the detected events. Extensive experiments on public datasets demonstrate HyperSED's competitive performance, along with a substantial improvement in efficiency compared to the current state-of-the-art unsupervised paradigm. Statistically, HyperSED boosts incremental SED by an average of 2%, 2%, and 25% in NMI, AMI, and ARI, respectively; enhancing efficiency by up to 37.41 times and at least 12.10 times, illustrating the advancement of the proposed framework.

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NeurIPS Conference 2024 Conference Paper

Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification

Jannik Franzen
Claudia Winklmayr
Vanessa E. Guarino
Christoph Karg
Xiaoyan Yu
Nora Koreuber
Jan P. Albrecht
Philip Bischoff

Uncertainty Quantification (UQ) is crucial for reliable image segmentation. Yet, while the field sees continual development of novel methods, a lack of agreed-upon benchmarks limits their systematic comparison and evaluation: Current UQ methods are typically tested either on overly simplistic toy datasets or on complex real-world datasets that do not allow to discern true uncertainty. To unify both controllability and complexity, we introduce Arctique, a procedurally generated dataset modeled after histopathological colon images. We chose histopathological images for two reasons: 1) their complexity in terms of intricate object structures and highly variable appearance, which yields challenging segmentation problems, and 2) their broad prevalence for medical diagnosis and respective relevance of high-quality UQ. To generate Arctique, we established a Blender-based framework for 3D scene creation with intrinsic noise manipulation. Arctique contains up to 50, 000 rendered images with precise masks as well as noisy label simulations. We show that by independently controlling the uncertainty in both images and labels, we can effectively study the performance of several commonly used UQ methods. Hence, Arctique serves as a critical resource for benchmarking and advancing UQ techniques and other methodologies in complex, multi-object environments, bridging the gap between realism and controllability. All code is publicly available, allowing re-creation and controlled manipulations of our shipped images as well as creation and rendering of new scenes.

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ICRA Conference 2014 Conference Paper

Dynamic modeling and control of a free-flying space robot with flexible-link and flexible-joints

Xiaoyan Yu
Li Chen

Free-flying space robot's nonlinearity and strong coupling characters make the dynamics and control of such system more complicated than a terrestrial robot system. The Space robots are always built in very light for saving the launch energy. The flexibility of the joints and links is considerable arising from its elasticity. Controlling this manipulator is more complex than controlling one with rigid joints due to the interactions of rigid and flexible motion, in which only a single actuation signal can be applied at each joint and has to control the flexure of both the joint itself and the link attached to it. To discussing an under-actuated flexible-link flexible-joint space manipulator, a free-flying space manipulator with one flexible link and two flexible revolute joints is presented in this paper. The dynamical Lagrange equation is established, and a singularly perturbed model has been formulated and used for designing a reduced-order controller. This controller consists of a rigid control component and two fast control components. Numerical simulations show that the link and joint vibrations have been stabilized effectively with good tracking performance.

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