Shudong Huang

Venues

Frequent coauthors

Papers

1. PCSR: Pseudo-label Consistency-Guided Sample Refinement for Noisy Correspondence Learning
   AAAI 2026 ·

   Zhuoyao Liu

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2. S3Net: Spatiotemporally Separated Sparse Network for Neuromorphic Vision Processing
   AAAI 2026 ·

   Ping He

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3. Aligning Information Capacity Between Vision and Language via Dense-to-Sparse Feature Distillation for Image-Text Matching
   ICCV 2025 ·

   Yang Liu

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4. Asymmetric Visual Semantic Embedding Framework for Efficient Vision-Language Alignment
   AAAI 2025 ·

   Yang Liu

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5. Crocodile: Cross Experts Covariance for Disentangled Learning in Multi-Domain Recommendation
   CIKM 2025 ·

   Zhutian Lin

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6. DONIS: Importance Sampling for Training Physics-Informed DeepONet
   IJCAI 2025 · Shudong Huang
7. Dual Prompt Learning for Adapting Vision-Language Models to Downstream Image-Text Retrieval
   ACM MM 2025 ·

   Yifan Wang

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8. Learning Dynamic Similarity by Bidirectional Hierarchical Sliding Semantic Probe for Efficient Text Video Retrieval
   AAAI 2025 ·

   Yang Liu

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9. Multi-view Granular-ball Contrastive Clustering
   AAAI 2025 ·

   Peng Su

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10. Towards Unifying Feature Interaction Models for Click-Through Rate Prediction
    ECML-PKDD 2025 ·

    Yu Kang

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11. Adaptive Instance-wise Multi-view Clustering
    ACM MM 2024 · Shudong Huang
12. An Effective Augmented Lagrangian Method for Fine-Grained Multi-View Optimization
    AAAI 2024 ·

    Yuze Tan

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13. Multi-Sequence Attentive User Representation Learning for Side-information Integrated Sequential Recommendation
    WSDM 2024 ·

    Xiaolin Lin

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14. Multi-View Clustering by Inter-cluster Connectivity Guided Reward
    ICML 2024 ·

    Hao Dai

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15. Robust Contrastive Multi-view Kernel Clustering
    IJCAI 2024 ·

    Peng Su

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16. Understanding the Ranking Loss for Recommendation with Sparse User Feedback
    SIGKDD 2024 ·

    Zhutian Lin

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17. With a Little Help from Language: Semantic Enhanced Visual Prototype Framework for Few-Shot Learning
    IJCAI 2024 ·

    Hecheng Cai

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