Autoregressive video diffusion enables interactive streaming generation, but suffers from error accumulation over long rollouts. Self-rollout training reduces exposure bias, yet finite rollouts leave long-range drift unresolved. We observe that the noise level of the history key-value (K/V) representations trades visua...
Weiqiang Wang, Zhuo-Kun Chen, Yu-Sheng Dai et al.· 0 citations
CANVAS (Consistency-Aware Navigation via Visual Adaptive Sampling), a training-free, render-aware inference framework that combines power-sharpened trajectory likelihood with visual feedback from rendered futures and derives a stroke-wise navigation rule, is introduced.
Yi-Cheng Wu, Haoxuan Qu, Yihang Lou et al.· 0 citations
The results reveal that attention-space regulation offers a considerably more promising path to safer diffusion transformer based image generation than the existing concept erasing mechanism.
Object Hallucination in large vision-language models (LVLMs), where models generate non-factual content about input images, remains a critical barrier to their reliability in real-world applications. Existing mitigation strategies can be categorized into training-based and training-free methods. Training-based methods...
Mehran Tamjidi, Hamidreza Dastmalchi, Ali Cheraghian et al.· 0 citations
This work proposes a training-free hallucination mitigation framework for dynamic, per-instance suppression at test time, and proposes a dynamically combined projection that selectively suppresses the most probable hallucination directions while preserving image-grounded semantics.
Ali Cheraghian, Hamidreza Dastmalchi, Hamed Barzamini et al.· 0 citations
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