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cs.CV2026
ViPO: Visual Preference Optimization at Scale
Ming Li, Jie Wu, Justin Cui +3
While preference optimization is crucial for improving visual generative models, how to effectively scale this paradigm remains largely unexplored. Current open-source preference d…
cs.CV2026
Learning from Noisy Preferences: A Semi-Supervised Learning Approach to Direct Preference Optimization
Xinxin Liu, Ming Li, Zonglin Lyu +2
Human visual preferences are inherently multi-dimensional, encompassing aesthetics, detail fidelity, and semantic alignment. However, existing datasets provide only single, holisti…