2024-11-13 06:27:11 Author: hackernoon.com(查看原文) 阅读量:3 收藏
Authors:
(1) Zhaoqing Wang, The University of Sydney and AI2Robotics;
(2) Xiaobo Xia, The University of Sydney;
(3) Ziye Chen, The University of Melbourne;
(4) Xiao He, AI2Robotics;
(5) Yandong Guo, AI2Robotics;
(6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence;
(7) Tongliang Liu, The University of Sydney.
Table of Links
3. Method and 3.1. Problem definition
3.2. Baseline and 3.3. Uni-OVSeg framework
4. Experiments
6. Broader impacts and References
5. Conclusion
In conclusion, this paper proposes an innovative framework for weakly-supervised open-vocabulary segmentation, named Uni-OVSeg. Using independent image-text and image-mask pairs, Uni-OVSeg effectively reduces the dependency on labour-intensive image-mask-text triplets, meanwhile achieving impressive segmentation performance in open-vocabulary settings. Using the LVLM to refine text descriptions and multi-scale ensemble to enhance the quality of region embeddings, we alleviate the noise in masktext correspondences, achieving substantial performance improvements. Notably, Uni-OVSeg significantly outper
forms previous state-of-the-art weakly-supervised methods and even surpasses the cutting-edge fully-supervised method on the Challenging PASCAL Context-459 dataset. This impressive advancement demonstrates the superiority of our proposed framework and paves the way for further research.
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