:::info 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.
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Table of Links3. Method and 3.1. Problem definition
3.2. Baseline and 3.3. Uni-OVSeg framework
4. Experiments
6. Broader impacts and References
6. Broader impactsThe Uni-OVSeg framework represents a significant advancement in open-vocabulary segmentation by reducing the dependency on labour-intensive image-mask-text triplet annotations. This innovation has the potential to democratise access to cutting-edge vision perception systems, offering substantial benefits across various sectors, such as medical imaging and autonomous vehicles. The development of a more efficient and accurate vision perception system contributes to the community, potentially leading to more innovative applications and research in machine learning, computer vision, and related areas. As with any AI model, there is a risk of bias in the data used for training. Efforts must be made to ensure that the datasets are diverse and representative to avoid perpetuating or amplifying biases.
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