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cs.CV2026
MSJoE: Jointly Evolving MLLM and Sampler for Efficient Long-Form Video Understanding
Wenhui Tan, Xiaoyi Yu, Jiaze Li +5
Efficiently understanding long-form videos remains a fundamental challenge for multimodal large language models (MLLMs). In this paper, we present MLLM-Sampler Joint Evolution (MSJ…
cs.CV2026
Think-Clip-Sample: Slow-Fast Frame Selection for Video Understanding
Wenhui Tan, Ruihua Song, Jiaze Li +2
Recent progress in multi-modal large language models (MLLMs) has significantly advanced video understanding. However, their performance on long-form videos remains limited by compu…
cs.CV2025
PlanMoGPT: Flow-Enhanced Progressive Planning for Text to Motion Synthesis
Chuhao Jin, Haosen Li, Bingzi Zhang +7
Recent advances in large language models (LLMs) have enabled breakthroughs in many multimodal generation tasks, but a significant performance gap still exists in text-to-motion gen…