4 citations · 6 across the 9 of their papers we have counts for
11 papers
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…
Task-Aware KV Compression For Cost-Effective Long Video Understanding
Minghao Qin, Yan Shu, Peitian Zhang +6
Long-video understanding (LVU) remains a severe challenge for existing multimodal large language models (MLLMs), primarily due to the prohibitive computational cost. Recent approac…
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…
Video-XL-Pro: Reconstructive Token Compression for Extremely Long Video Understanding
Xiangrui Liu, Yan Shu, Zheng Liu +3
Despite advanced token compression techniques, existing multimodal large language models (MLLMs) still struggle with hour-long video understanding. In this work, we propose Video-X…
STI-Bench: Are MLLMs Ready for Precise Spatial-Temporal World Understanding?
Yun Li, Yiming Zhang, Tao Lin +4
The use of Multimodal Large Language Models (MLLMs) as an end-to-end solution for Embodied AI and Autonomous Driving has become a prevailing trend. While MLLMs have been extensivel…
MMCR: Benchmarking Cross-Source Reasoning in Scientific Papers
Yang Tian, Zheng Lu, Mingqi Gao +2
Fully comprehending scientific papers by machines reflects a high level of Artificial General Intelligence, requiring the ability to reason across fragmented and heterogeneous sour…
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…
MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval
Junjie Zhou, Zheng Liu, Ze Liu +6
Despite the rapidly growing demand for multimodal retrieval, progress in this field remains severely constrained by a lack of training data. In this paper, we introduce MegaPairs,…
Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding
Yan Shu, Zheng Liu, Peitian Zhang +5
Long video understanding poses a significant challenge for current Multi-modal Large Language Models (MLLMs). Notably, the MLLMs are constrained by their limited context lengths an…
VISTA: Visualized Text Embedding For Universal Multi-Modal Retrieval
Junjie Zhou, Zheng Liu, Shitao Xiao +2
Multi-modal retrieval becomes increasingly popular in practice. However, the existing retrievers are mostly text-oriented, which lack the capability to process visual information.…
MLVU: Benchmarking Multi-task Long Video Understanding
Junjie Zhou, Yan Shu, Bo Zhao +9
The evaluation of Long Video Understanding (LVU) performance poses an important but challenging research problem. Despite previous efforts, the existing video understanding benchma…