62 citations · 100 across the 26 of their papers we have counts for
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cs.CV2025
ViSS-R1: Self-Supervised Reinforcement Video Reasoning
Bo Fang, Yuxin Song, Qiangqiang Wu +3
Complex video reasoning remains a significant challenge for Multimodal Large Language Models (MLLMs), as current R1-based methodologies often prioritize text-centric reasoning deri…
cs.CV2025
Query-Kontext: An Unified Multimodal Model for Image Generation and Editing
Yuxin Song, Wenkai Dong, Shizun Wang +8
Unified Multimodal Models (UMMs) have demonstrated remarkable performance in text-to-image generation (T2I) and editing (TI2I), whether instantiated as assembled unified frameworks…
cs.CV2025
Threading Keyframe with Narratives: MLLMs as Strong Long Video Comprehenders
Bo Fang, Wenhao Wu, Qiangqiang Wu +2
Employing Multimodal Large Language Models (MLLMs) for long video understanding remains a challenging problem due to the dilemma between the substantial number of video frames (i.e…