activity
20242026
most citedVideo-XL-2: Towards Very Long-Video Understanding Through Task-Aware KV Sparsification

1 citations · 1 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CV2026

BigEarthNet.txt: A Large-Scale Multi-Sensor Image-Text Dataset and Benchmark for Earth Observation

Johann-Ludwig Herzog, Mathis Jürgen Adler, Leonard Hackel +5

Vision-langugage models (VLMs) have shown strong performance in computer vision (CV), yet their performance on remote sensing (RS) data remains limited due to the lack of large-sca…

cs.CV2026

Video-Browser: Towards Agentic Open-web Video Browsing

Zhengyang Liang, Yan Shu, Xiangrui Liu +5

The evolution of autonomous agents is redefining information seeking, transitioning from passive retrieval to proactive, open-ended web research. However, a significant modality ga…

cs.CV2025

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…

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

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…