3 papers
cs.CV2026
When Preference Labels Fall Short: Aligning Diffusion Models from Real Data
Weiyan Chen, Weijian Deng, Yao Xiao +5
Preference alignment aims to guide generative models by learning from comparisons between preferred and non-preferred samples. In practice, most existing approaches rely on prefere…
cs.CV2026
Unveiling Perceptual Artifacts: A Fine-Grained Benchmark for Interpretable AI-Generated Image Detection
Yao Xiao, Weiyan Chen, Jiahao Chen +8
Current AI-Generated Image (AIGI) detection approaches predominantly rely on binary classification to distinguish real from synthetic images, often lacking interpretable or convinc…
cs.CV2025
Decoder-Only LLMs are Better Controllers for Diffusion Models
Ziyi Dong, Yao Xiao, Pengxu Wei +1
Groundbreaking advancements in text-to-image generation have recently been achieved with the emergence of diffusion models. These models exhibit a remarkable ability to generate hi…