4 papers
Fine-grained CLIP fine-tuning with self-annotated region alignment
Chenyang Zhao, Wei Lin, Janet H. Hsiao +1
Contrastive Language-Image Pre-training (CLIP) has been shown to have limitations in its fine-grained dense feature representation, due to its pre-training focusing on matching the…
Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting
Wei Lin, Chenyang Zhao, Antoni B. Chan
Point detection has been developed to locate pedestrians in crowded scenes by training a counter through a point-to-point (P2P) supervision scheme. Despite its excellent localizati…
Density-based Object Detection in Crowded Scenes
Chenyang Zhao, Jia Wan, Antoni B. Chan
Compared with the generic scenes, crowded scenes contain highly-overlapped instances, which result in: 1) more ambiguous anchors during training of object detectors, and 2) more pr…
Grad-ECLIP: Gradient-based Visual and Textual Explanations for CLIP
Chenyang Zhao, Kun Wang, Janet H. Hsiao +1
Significant progress has been achieved on the improvement and downstream usages of the Contrastive Language-Image Pre-training (CLIP) vision-language model, while less attention is…