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cs.CV2025
RISE-T2V: Rephrasing and Injecting Semantics with LLM for Expansive Text-to-Video Generation
Xiangjun Zhang, Litong Gong, Yinglin Zheng +6
Most text-to-video(T2V) diffusion models depend on pre-trained text encoders for semantic alignment, yet they often fail to maintain video quality when provided with concise prompt…
cs.CV2025
VC4VG: Optimizing Video Captions for Text-to-Video Generation
Yang Du, Zhuoran Lin, Kaiqiang Song +5
Recent advances in text-to-video (T2V) generation highlight the critical role of high-quality video-text pairs in training models capable of producing coherent and instruction-alig…
cs.CV2025
R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning
Biao Wang, Wenwen Li, Jiawei Ge
Visual single object tracking aims to continuously localize and estimate the scale of a target in subsequent video frames, given only its initial state in the first frame. This tas…