5 papers · 1 filter
ICED: Concept-level Machine Unlearning via Interpretable Concept Decomposition
Shen Lin, Jing Lin, Junhao Dong +2
Machine unlearning in Vision-Language Models (VLMs) is typically performed at the image or instance level, making it difficult to precisely remove target knowledge without affectin…
DeCo-DETR: Decoupled Cognition DETR for efficient Open-Vocabulary Object Detection
Siheng Wang, Yanshu Li, Bohan Hu +12
Open-vocabulary object detection (OVOD) enables models to recognize objects beyond predefined categories, but existing approaches remain limited in practical deployment. On the one…
Hierarchically Robust Zero-shot Vision-language Models
Junhao Dong, Yifei Zhang, Hao Zhu +2
Vision-Language Models (VLMs) can perform zero-shot classification but are susceptible to adversarial attacks. While robust fine-tuning improves their robustness, existing approach…
MMT-ARD: Multimodal Multi-Teacher Adversarial Distillation for Robust Vision-Language Models
Yuqi Li, Junhao Dong, Chuanguang Yang +5
Vision-Language Models (VLMs) are increasingly deployed in safety-critical applications, making their adversarial robustness a crucial concern. While adversarial knowledge distilla…
C3-OWD: A Curriculum Cross-modal Contrastive Learning Framework for Open-World Detection
Siheng Wang, Zhengdao Li, Yanshu Li +12
Object detection has advanced significantly in the closed-set setting, but real-world deployment remains limited by two challenges: poor generalization to unseen categories and ins…