most citedVideo-XL-2: Towards Very Long-Video Understanding Through Task-Aware KV Sparsification

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cs.CV2025

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…

cs.CV20251 cited

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…

cs.CV2025

OmniGen2: Towards Instruction-Aligned Multimodal Generation

Chenyuan Wu, Pengfei Zheng, Ruiran Yan +19

In this work, we introduce OmniGen2, a versatile and open-source generative model designed to provide a unified solution for diverse generation tasks, including text-to-image, imag…

cs.CV2025

VideoExplorer: Think With Videos For Agentic Long-Video Understanding

Huaying Yuan, Zheng Liu, Junjie Zhou +5

Long-video understanding~(LVU) is a challenging problem in computer vision. Existing methods either downsample frames for single-pass reasoning, sacrificing fine-grained details, o…

cs.CV2025

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…

cs.CV20241 cited

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,…