4 papers
Uni-AdaVD: Universal Concept Erasure for Visual Generation via Orthogonal Value Decomposition
Qifan Zhou, Yuan Wang, Yanbin Hao +4
Visual generative models inevitably absorb undesirable concepts from uncurated pretraining data, making concept erasure essential for safe deployment. Existing erasure methods, how…
Think, then Score: Decoupled Reasoning and Scoring for Video Reward Modeling
Yuan Wang, Ouxiang Li, Yulong Xu +8
Recent advances in generative video models are increasingly driven by post-training and test-time scaling, both of which critically depend on the quality of video reward models (RM…
RMBRec: Robust Multi-Behavior Recommendation towards Target Behaviors
Miaomiao Cai, Zhijie Zhang, Junfeng Fang +3
Multi-behavior recommendation faces a critical challenge in practice: auxiliary behaviors (e.g., clicks, carts) are often noisy, weakly correlated, or semantically misaligned with…
Thinking with Frames: Generative Video Distortion Evaluation via Frame Reward Model
Yuan Wang, Borui Liao, Huijuan Huang +5
Recent advances in video reward models and post-training strategies have improved text-to-video (T2V) generation. While these models typically assess visual quality, motion quality…