16 papers
InternVideo-Next: Towards General Video Foundation Models without Video-Text Supervision
Chenting Wang, Yuhan Zhu, Yicheng Xu +6
Large-scale video-text pretraining achieves strong performance but depends on noisy, synthetic captions with limited semantic coverage, often overlooking implicit world knowledge s…
A Renaissance of Explicit Motion Information Mining from Transformers for Action Recognition
Peiqin Zhuang, Lei Bai, Yichao Wu +4
Recently, action recognition has been dominated by transformer-based methods, thanks to their spatiotemporal contextual aggregation capacities. However, despite the significant pro…
VideoChat-R1.5: Visual Test-Time Scaling to Reinforce Multimodal Reasoning by Iterative Perception
Ziang Yan, Xinhao Li, Yinan He +6
Inducing reasoning in multimodal large language models (MLLMs) is critical for achieving human-level perception and understanding. Existing methods mainly leverage LLM reasoning to…
VRBench: A Benchmark for Multi-Step Reasoning in Long Narrative Videos
Jiashuo Yu, Yue Wu, Meng Chu +14
We present VRBench, the first long narrative video benchmark crafted for evaluating large models' multi-step reasoning capabilities, addressing limitations in existing evaluations…
Super Encoding Network: Recursive Association of Multi-Modal Encoders for Video Understanding
Boyu Chen, Siran Chen, Kunchang Li +3
Video understanding has been considered as one critical step towards world modeling, which is an important long-term problem in AI research. Recently, multimodal foundation models…
VideoChat-A1: Thinking with Long Videos by Chain-of-Shot Reasoning
Zikang Wang, Boyu Chen, Zhengrong Yue +4
Recent advances in video understanding have been driven by MLLMs. But these MLLMs are good at analyzing short videos, while suffering from difficulties in understanding videos with…