13 papers
FIRM-Video: Check Before You Score for Reliable Text-to-Video Reward Modeling
Peiyuan Zhang, Xiangyu Zhao, Hongbo Liu +8
Reliable reward models are essential for text-to-video evaluation and alignment. However, the trade-off between evaluation accuracy and inference efficiency places high demands on…
Youtu-Parsing: Perception, Structuring and Recognition via High-Parallelism Decoding
Haoyu Cao, Kun Yin, Yunfei Wu +16
This paper presents Youtu-Parsing, an efficient and versatile document parsing model designed for high-performance content extraction. The architecture employs a native Vision Tran…
Omni-Diffusion: Unified Multimodal Understanding and Generation with Masked Discrete Diffusion
Lijiang Li, Zuwei Long, Yunhang Shen +6
While recent multimodal large language models (MLLMs) have made impressive strides, they predominantly employ a conventional autoregressive architecture as their backbone, leaving…
Moment-Video: Diagnosing Temporal Fidelity of Video MLLMs on Momentary Visual Events
Xiaolin Liu, Yilun Zhu, Xiangyu Zhao +9
Video multimodal large language models (MLLMs) have made rapid progress on general and long-form video understanding, yet their ability to preserve brief answer-critical visual evi…
Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding
Chaoyou Fu, Haozhi Yuan, Yuhao Dong +16
With the rapid advancement of video understanding, existing benchmarks are becoming increasingly saturated, exposing a critical discrepancy between inflated leaderboard scores and…
Dynamic Token Compression for Efficient Video Understanding through Reinforcement Learning
Shida Wang, YongXiang Hua, Zhou Tao +2
Multimodal Large Language Models have demonstrated remarkable capabilities in video understanding, yet face prohibitive computational costs and performance degradation from ''conte…