Fine-grained alignment between text and video object trajectories is a core challenge in multimodal understanding. Existing embedding models excel at global retrieval, yet their single global representation entangles multiple objects and their spatiotemporal dynamics, limiting direct object-level grounding. To address...
Rui-Zhi Chen, Zi-Ning Fan, Yu-Di Guo et al.· 0 citations
While reinforcement learning with verifiable rewards provides reliable outcome supervision for VideoLLMs, sequence-level rewards offer limited token-level guidance. On-policy self-distillation addresses this limitation by conditioning a self-teacher on privileged information to provide dense token-level supervision. Ho...
Wei-Jie Zhu, Han Fang, Han-Yun Fu et al.· 0 citations
GeoAnchor decomposes 3D spatial information into three complementary components: position latents for object grounding, direction latents for relational orientation, and geometry latents for scene structure, and is introduced a collaborative training strategy that guides the model from local spatial perception to compr...
Hao Li, Han Fang, Zixin Pan et al.· arXiv.org· 0 citations
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