most citedThinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning

2 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2025

VG-Refiner: Towards Tool-Refined Referring Grounded Reasoning via Agentic Reinforcement Learning

Yuji Wang, Wenlong Liu, Jingxuan Niu +2

Tool-integrated visual reasoning (TiVR) has demonstrated great potential in enhancing multimodal problem-solving. However, existing TiVR paradigms mainly focus on integrating vario…

cs.CV20252 cited

Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning

Haoji Zhang, Xin Gu, Jiawen Li +7

The video reasoning ability of multimodal large language models (MLLMs) is crucial for downstream tasks like video question answering and temporal grounding. While recent approache…

cs.CV2025

Flash-VStream: Efficient Real-Time Understanding for Long Video Streams

Haoji Zhang, Yiqin Wang, Yansong Tang +3

Benefiting from the advances in large language models and cross-modal alignment, existing multimodal large language models have achieved prominent performance in image and short vi…

cs.CV20251 cited

UniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement Learning

Sule Bai, Mingxing Li, Yong Liu +5

Traditional visual grounding methods primarily focus on single-image scenarios with simple textual references. However, extending these methods to real-world scenarios that involve…

cs.CV2024

Ponder & Press: Advancing Visual GUI Agent towards General Computer Control

Yiqin Wang, Haoji Zhang, Jingqi Tian +1

Most existing GUI agents typically depend on non-vision inputs like HTML source code or accessibility trees, limiting their flexibility across diverse software environments and pla…