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cs.CV2024
Filter or Compensate: Towards Invariant Representation from Distribution Shift for Anomaly Detection
Zining Chen, Xingshuang Luo, Weiqiu Wang +3
Recent Anomaly Detection (AD) methods have achieved great success with In-Distribution (ID) data. However, real-world data often exhibits distribution shift, causing huge performan…
cs.CV2024
Fleximo: Towards Flexible Text-to-Human Motion Video Generation
Yuhang Zhang, Yuan Zhou, Zeyu Liu +4
Current methods for generating human motion videos rely on extracting pose sequences from reference videos, which restricts flexibility and control. Additionally, due to the limita…
cs.CV2024
PracticalDG: Perturbation Distillation on Vision-Language Models for Hybrid Domain Generalization
Zining Chen, Weiqiu Wang, Zhicheng Zhao +3
Domain Generalization (DG) aims to resolve distribution shifts between source and target domains, and current DG methods are default to the setting that data from source and target…