1 citations · 1 across the 6 of their papers we have counts for
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Each Judge Its Own Yardstick: Discovering Per-VLM Taxonomies for Physical Video Evaluation
Yu Cao, Ziquan Liu, Zhensong Zhang +3
Maintaining physical consistency in video generators and world models increasingly relies on vision-language models (VLMs) as automated judges that provide reward signals, ranking…
LatSearch: Latent Reward-Guided Search for Faster Inference-Time Scaling in Video Diffusion
Zengqun Zhao, Ziquan Liu, Yu Cao +5
The recent success of inference-time scaling in large language models has inspired similar explorations in video diffusion. In particular, motivated by the existence of "golden noi…
Temporal Score Analysis for Understanding and Correcting Diffusion Artifacts
Yu Cao, Zengqun Zhao, Ioannis Patras +1
Visual artifacts remain a persistent challenge in diffusion models, even with training on massive datasets. Current solutions primarily rely on supervised detectors, yet lack under…
AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic Data
Zengqun Zhao, Ziquan Liu, Yu Cao +2
Recent advances in generative models have sparked research on improving model fairness with AI-generated data. However, existing methods often face limitations in the diversity and…
Few-Shot Image Generation by Conditional Relaxing Diffusion Inversion
Yu Cao, Shaogang Gong
In the field of Few-Shot Image Generation (FSIG) using Deep Generative Models (DGMs), accurately estimating the distribution of target domain with minimal samples poses a significa…
Enhancing Zero-Shot Facial Expression Recognition by LLM Knowledge Transfer
Zengqun Zhao, Yu Cao, Shaogang Gong +1
Current facial expression recognition (FER) models are often designed in a supervised learning manner and thus are constrained by the lack of large-scale facial expression images w…