This work proposes a streaming recognition and gated LoRA framework for intrinsic VLM safety, which is trained from unsafe prefixes, transition statements, and safe continuations, so that it learns to redirect unsafe generations back to safe responses after activation.
Cao-Yuan Ma, Tian Gu, Wen-Pu Liu et al.· 0 citations
This work shows that complex scenes with hundreds of objects can instead be generated compositionally by adapting a strong single-object 3D generative prior to multi-view observations, and introduces UE-MeshyScene, a photorealistic benchmark of densely cluttered scenes with hundreds of objects, per-object annotations,...
This work proposes QuantaFlow, the first method for dense optical flow directly from SPAD streams, which embeds SPAD representation construction into iterative flow refinement and constructs multi-scale representations containing intensity and structural cues.
Wendi Liu, Weichao Zeng, Wei-Hang Ran et al.· 0 citations
This work introduces Surprise Forcing, a training-free framework that treats both limitations as online resource-allocation problems and improves long-horizon consistency and visual quality while retaining real-time streaming throughput.
This work proposes an adapter-based framework that incorporates event-derived cues into a pre-trained image-to-video diffusion model with minimal architectural changes and consistently outperforms existing state-of-the-art approaches.
Gui-Xu Lin, Yu-Yang Yu, Xiang Ji et al.· 0 citations
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