Visual Grounding
Visual grounding is the task of connecting natural language descriptions to corresponding regions within an image or 3D scene. Current research focuses on improving the accuracy and efficiency of visual grounding models, often employing transformer-based architectures and leveraging large multimodal language models (MLLMs) for enhanced feature fusion and reasoning capabilities. This field is crucial for advancing embodied AI, enabling robots and other agents to understand and interact with the world through natural language, and has significant implications for applications such as robotic manipulation, visual question answering, and medical image analysis.
Papers
ResVG: Enhancing Relation and Semantic Understanding in Multiple Instances for Visual Grounding
Minghang Zheng, Jiahua Zhang, Qingchao Chen, Yuxin Peng, Yang Liu
M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation
Jonggwon Park, Soobum Kim, Byungmu Yoon, Jihun Hyun, Kyoyun Choi
Learning Visual Grounding from Generative Vision and Language Model
Shijie Wang, Dahun Kim, Ali Taalimi, Chen Sun, Weicheng Kuo
Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Models
Xiaoyu Zhu, Hao Zhou, Pengfei Xing, Long Zhao, Hao Xu, Junwei Liang, Alexander Hauptmann, Ting Liu, Andrew Gallagher
Multi-branch Collaborative Learning Network for 3D Visual Grounding
Zhipeng Qian, Yiwei Ma, Zhekai Lin, Jiayi Ji, Xiawu Zheng, Xiaoshuai Sun, Rongrong Ji
Exploring Phrase-Level Grounding with Text-to-Image Diffusion Model
Danni Yang, Ruohan Dong, Jiayi Ji, Yiwei Ma, Haowei Wang, Xiaoshuai Sun, Rongrong Ji