11 citations · 30 across the 16 of their papers we have counts for
6 papers · 1 filter
: Synonymous Semantic Space for Improving Zero-Shot Generalization of Vision-Language Models
Xiaojie Yin, Qilong Wang, Bing Cao +1
Recently, many studies have been conducted to enhance the zero-shot generalization ability of vision-language models (e.g., CLIP) by addressing the semantic misalignment between im…
Conditional Controllable Image Fusion
Bing Cao, Xingxin Xu, Pengfei Zhu +2
Image fusion aims to integrate complementary information from multiple input images acquired through various sources to synthesize a new fused image. Existing methods usually emplo…
TAMT: Temporal-Aware Model Tuning for Cross-Domain Few-Shot Action Recognition
Yilong Wang, Zilin Gao, Qilong Wang +3
Going beyond few-shot action recognition (FSAR), cross-domain FSAR (CDFSAR) has attracted recent research interests by solving the domain gap lying in source-to-target transfer lea…
Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark
Bing Cao, Quanhao Lu, Jiekang Feng +3
The dynamic imbalance of the fore-background is a major challenge in video object counting, which is usually caused by the sparsity of target objects. This remains understudied in…
Fine-Grained Domain Generalization with Feature Structuralization
Wenlong Yu, Dongyue Chen, Qilong Wang +1
Fine-grained domain generalization (FGDG) is a more challenging task than traditional DG tasks due to its small inter-class variations and relatively large intra-class disparities.…
AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning
Yuwei Tang, Zhenyi Lin, Qilong Wang +2
Recently, pre-trained vision-language models (e.g., CLIP) have shown great potential in few-shot learning and attracted a lot of research interest. Although efforts have been made…