21 citations · 21 across the 7 of their papers we have counts for
7 papers
0.5%>100%: Bidirectional Reciprocal Learning for Referring Image Segmentation
Xiaoqiang Lu, Licheng Jiao, Lingling Li +5
Recent advances in vision foundation models (VFMs) have shown remarkable capabilities across diverse unimodal visual tasks. However, adapting VFMs to referring image segmentation (…
DeCo: Efficient Decouple-to-Couple Learning for Multi-Task Visual Grounding
Xiaoqiang Lu, Licheng Jiao, Long Sun +5
Multi-task visual grounding requires models to jointly understand linguistic semantics and perform accurate visual localization and segmentation. Despite the success of multimodal…
Improving the Multi-label Atomic Activity Recognition by Robust Visual Feature and Advanced Attention @ ROAD++ Atomic Activity Recognition 2024
Jiamin Cao, Lingqi Wang, Kexin Zhang +3
Road++ Track3 proposes a multi-label atomic activity recognition task in traffic scenarios, which can be standardized as a 64-class multi-label video action recognition task. In th…
Technique Report of CVPR 2024 PBDL Challenges
Ying Fu, Yu Li, Shaodi You +96
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to info…
3rd Place Solution for MOSE Track in CVPR 2024 PVUW workshop: Complex Video Object Segmentation
Xinyu Liu, Jing Zhang, Kexin Zhang +3
Video Object Segmentation (VOS) is a vital task in computer vision, focusing on distinguishing foreground objects from the background across video frames. Our work draws inspiratio…
The Robust Semantic Segmentation UNCV2023 Challenge Results
Xuanlong Yu, Yi Zuo, Zitao Wang +34
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segment…
Transformers Meet Visual Learning Understanding: A Comprehensive Review
Yuting Yang, Licheng Jiao, Xu Liu +4
Dynamic attention mechanism and global modeling ability make Transformer show strong feature learning ability. In recent years, Transformer has become comparable to CNNs methods in…