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Peng Wang

Northwestern Polytechnical University, School of Computer Science, Xi'an, China

22 papers at tracked venues · 20 at CORE A* · active 20242026

Venues

Frequent coauthors

Papers

  1. Better Matching, Less Forgetting: A Quality-Guided Matcher for Transformer-based Incremental Object Detection
  2. Do Large Language Models Reason About Uncertainty Like Humans? A Benchmark on Hurricane Forecast Visualization Comprehension
  3. TargetVAU: Multimodal Anomaly-Aware Reasoning for Target Behavior Understanding in Videos
  4. Demystifying Catastrophic Forgetting in Two-Stage Incremental Object Detector
  5. Gradient Decomposition and Alignment for Incremental Object Detection
  6. LA-MOTR: End-to-End Multi-Object Tracking by Learnable Association
    ICCV 2025 · Peng Wang
  7. Mitigating Information Loss under High Pruning Rates for Efficient Large Vision Language Models
  8. Octopus: Alleviating Hallucination via Dynamic Contrastive Decoding
  9. Pruning All-Rounder: Rethinking and Improving Inference Efficiency for Large Vision Language Models
  10. SeCap: Self-Calibrating and Adaptive Prompts for Cross-view Person Re-Identification in Aerial-Ground Networks
  11. Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers
  12. Unlocking Generalization Power in LiDAR Point Cloud Registration
  13. VLN-ChEnv: Vision-language Navigation in Changeable Environments
  14. VarCMP: Adapting Cross-Modal Pre-Training Models for Video Anomaly Retrieval
  15. A Plug-and-Play Method for Rare Human-Object Interactions Detection by Bridging Domain Gap
  16. C3L: Content Correlated Vision-Language Instruction Tuning Data Generation via Contrastive Learning
  17. Open-Vocabulary Video Anomaly Detection
  18. Rethinking and Improving Visual Prompt Selection for In-Context Learning Segmentation
  19. Sustainable Self-evolution Adversarial Training
  20. VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection
  21. Visual Prompt Tuning in Null Space for Continual Learning
  22. Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts