6 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…
GenLie: A Global-Enhanced Lie Detection Network under Sparsity and Semantic Interference
Zongshun Zhang, Yao Liu, Qiao Liu +5
Video-based lie detection aims to identify deceptive behaviors from visual cues. Despite recent progress, its core challenge lies in learning sparse yet discriminative representati…
Exclusivity-Guided Mask Learning for Semi-Supervised Crowd Instance Segmentation and Counting
Jiyang Huang, Hongru Chen, Wei Lin +2
Semi-supervised crowd analysis is a prominent area of research, as unlabeled data are typically abundant and inexpensive to obtain. However, traditional point-based annotations con…
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…
A Fixed-Point Approach to Unified Prompt-Based Counting
Wei Lin, Antoni B. Chan
Existing class-agnostic counting models typically rely on a single type of prompt, e.g., box annotations. This paper aims to establish a comprehensive prompt-based counting framewo…
Robust Zero-Shot Crowd Counting and Localization With Adaptive Resolution SAM
Jia Wan, Qiangqiang Wu, Wei Lin +1
The existing crowd counting models require extensive training data, which is time-consuming to annotate. To tackle this issue, we propose a simple yet effective crowd counting meth…