Nov 12, 2024
Defining Open-Vocabulary Segmentation: Problem Setup, Baseline, and the Uni-OVSeg Framework by@segmentation
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Defining Open-Vocabulary Segmentation: Problem Setup, Baseline, and the Uni-OVSeg Framework
by SegmentationNovember 12th, 2024
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In this section, we define open-vocabulary segmentation as the task of segmenting images into masks linked to semantic categories not seen during training. We then introduce a baseline approach and present our proposed Uni-OVSeg framework, which tackles the challenge of segmenting novel categories using natural language representations of test categories.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
3. Method
In this section, we first define the problem of openvocabulary segmentation in Sec. 3.1. We then introduce a straightforward baseline in Sec. 3.2. Finally, we present our proposed Uni-OVSeg framework in Sec. 3.3, including an overview, mask generation, mask-text alignment, and open-vocabulary inference.
3.1. Problem definition
This paper is available on arxiv under CC BY 4.0 DEED license.
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