15 papers
One Ranking, Any Budget: Matryoshka Evidence-to-Context Frame Selection for Long-Video Understanding
Wang Chen, Yu Chen, Xiang Wang +3
Frame selection is essential for applying Large Multimodal Models (LMMs) to long videos due to severe frame redundancy and limited context windows. Since the appropriate frame budg…
TimePLE: Rethinking Temporal Representation for Video Temporal Grounding
Yuhui Zeng, Xinyu Mao, Xiaokun Liu +4
Video temporal grounding (VTG) aims to localize the continuous video interval described by a natural-language query. However, current VLM-based methods typically produce this inter…
WaveZip: Wavelet-Driven Space-Time Decoupling for Video Token Condensation
Yuhui Zeng, Wang Chen, Jinfa Huang +5
Existing Large Vision-Language Models (LVLMs) struggle with long-form video understanding due to the quadratic computational cost of visual tokens. While recent efficient methods a…
SpecEyes: Accelerating Agentic Multimodal LLMs via Speculative Perception and Planning
Haoyu Huang, Jinfa Huang, Zhongwei Wan +3
Agentic multimodal large language models (MLLMs) (e.g., OpenAI o3 and Gemini Agentic Vision) achieve remarkable reasoning capabilities through iterative visual tool invocation. How…
SocialOmni: Benchmarking Audio-Visual Social Interactivity in Omni Models
Tianyu Xie, Jinfa Huang, Yuexiao Ma +11
Omni-modal large language models (OLMs) redefine human-machine interaction by natively integrating audio, vision, and text. However, existing OLM benchmarks remain anchored to stat…
Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension
Yongdong Luo, Xiawu Zheng, Guilin Li +8
Existing large video-language models (LVLMs) struggle to comprehend long videos correctly due to limited context. To address this problem, fine-tuning long-context LVLMs and employ…