6 papers · 1 filter
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…
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…
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…
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…
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…