2 citations · 3 across the 7 of their papers we have counts for
8 papers
PETR: Prompt Ensembling with Training-free Routing for Vision-Language Models
Weihan Cai, Hao Tan, Xinping Gao +2
Prompt learning efficiently adapts vision-language models (VLMs) to downstream tasks, but gains on seen classes often come at the expense of generalization to unseen classes. To ad…
MotionPhys: Detecting AI-Generated Videos via Physical Consistency of Optical-Flow Trajectories
Haojin He, Hao Tan, Zichang Tan +2
Modern AI video generation models can produce videos with high visual fidelity and seemingly smooth temporal transitions. However, visual realism does not necessarily imply physica…
Unleashing the Potential of Vision-Language Models for Generalizable AI-Generated Image Detection
Weihan Cai, Hao Tan, Zichang Tan +2
Recent work has shown that a simple linear probe on frozen representations from modern vision foundation models (VFMs) can achieve state-of-the-art AIGI detection performance, subs…
HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection
Senyuan Shi, Hao Tan, Zichang Tan +4
The rapid evolution of generative models has precipitated a proliferation of fabricated content, posing significant challenges to existing Synthetic Image Detection (SID) methods.…
Recover and Match: Open-Vocabulary Multi-Label Recognition through Knowledge-Constrained Optimal Transport
Hao Tan, Zichang Tan, Jun Li +3
Identifying multiple novel classes in an image, known as open-vocabulary multi-label recognition, is a challenging task in computer vision. Recent studies explore the transfer of p…
SSPA: Split-and-Synthesize Prompting with Gated Alignments for Multi-Label Image Recognition
Hao Tan, Zichang Tan, Jun Li +3
Multi-label image recognition is a fundamental task in computer vision. Recently, Vision-Language Models (VLMs) have made notable advancements in this area. However, previous metho…
PVLR: Prompt-driven Visual-Linguistic Representation Learning for Multi-Label Image Recognition
Hao Tan, Zichang Tan, Jun Li +2
Multi-label image recognition is a fundamental task in computer vision. Recently, vision-language models have made notable advancements in this area. However, previous methods ofte…
Compound Text-Guided Prompt Tuning via Image-Adaptive Cues
Hao Tan, Jun Li, Yizhuang Zhou +3
Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable generalization capabilities to downstream tasks. However, existing prompt tuning based frameworks need to pa…