most citedUniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement Learning

1 citations · 1 across the 3 of their papers we have counts for

collaborators

7 papers

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.CV2025

Stepping Out of Similar Semantic Space for Open-Vocabulary Segmentation

Yong Liu, SongLi Wu, Sule Bai +3

Open-vocabulary segmentation aims to achieve segmentation of arbitrary categories given unlimited text inputs as guidance. To achieve this, recent works have focused on developing…

cs.CV2025

DreamLight: Towards Harmonious and Consistent Image Relighting

Yong Liu, Wenpeng Xiao, Qianqian Wang +5

We introduce a model named DreamLight for universal image relighting in this work, which can seamlessly composite subjects into a new background while maintaining aesthetic uniform…

cs.CV2025

SAM2-LOVE: Segment Anything Model 2 in Language-aided Audio-Visual Scenes

Yuji Wang, Haoran Xu, Yong Liu +2

Reference Audio-Visual Segmentation (Ref-AVS) aims to provide a pixel-wise scene understanding in Language-aided Audio-Visual Scenes (LAVS). This task requires the model to continu…

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.CV2025

IteRPrimE: Zero-shot Referring Image Segmentation with Iterative Grad-CAM Refinement and Primary Word Emphasis

Yuji Wang, Jingchen Ni, Yong Liu +2

Zero-shot Referring Image Segmentation (RIS) identifies the instance mask that best aligns with a specified referring expression without training and fine-tuning, significantly red…