1 citations · 2 across the 7 of their papers we have counts for
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DyCo-RL: Dynamic Cross-Modal Coordination for Visual Reasoning
Hangui Lin, Yan Shu, Zhengyang Liang +8
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a leading paradigm for enhancing visual reasoning in Multimodal Large Language Models (MLLMs). However, existin…
TimeScope: Towards Task-Oriented Temporal Grounding In Long Videos
Xiangrui Liu, Minghao Qin, Yan Shu +5
Identifying key temporal intervals within long videos, known as temporal grounding (TG), is important to video understanding and reasoning tasks. In this paper, we introduce a new…
Video-XL-2: Towards Very Long-Video Understanding Through Task-Aware KV Sparsification
Minghao Qin, Xiangrui Liu, Zhengyang Liang +6
Multi-modal large language models (MLLMs) models have made significant progress in video understanding over the past few years. However, processing long video inputs remains a majo…
MomentSeeker: A Task-Oriented Benchmark For Long-Video Moment Retrieval
Huaying Yuan, Jian Ni, Zheng Liu +7
Accurately locating key moments within long videos is crucial for solving long video understanding (LVU) tasks. However, existing benchmarks are either severely limited in terms of…
Unveiling the Ignorance of MLLMs: Seeing Clearly, Answering Incorrectly
Yexin Liu, Zhengyang Liang, Yueze Wang +8
Multimodal Large Language Models (MLLMs) have displayed remarkable performance in multi-modal tasks, particularly in visual comprehension. However, we reveal that MLLMs often gener…