UniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement Learning
Sule Bai, Mingxing Li, Yong Liu, Jing Tang, Haoji Zhang, Lei Sun, Xiangxiang Chu, Yansong Tang
2025-05-22
Summary
This paper talks about UniVG-R1, a new AI model that gets really good at matching words or phrases to the right objects in pictures by using smart reasoning and special training techniques.
What's the problem?
AI often struggles to accurately connect language with the correct parts of an image, especially when the task is tricky or the pictures are very different from what the model has seen before.
What's the solution?
The researchers created UniVG-R1, which uses reinforcement learning and a strategy that pays attention to how hard each example is, helping the AI learn to reason better and perform well even on tough or new images.
Why it matters?
This matters because it makes AI more reliable for real-world uses like helping visually impaired people, improving search tools, and making robots better at understanding their surroundings.
Abstract
UniVG-R1, a reasoning-guided multimodal large language model, enhances visual grounding by leveraging reinforcement learning and a difficulty-aware strategy, achieving state-of-the-art results and strong generalizability.