Evaluating Promptable Segmentation with Uniform Point Grids and Bounding Boxes on Diverse Datasets
Promptable segmentation is evaluated using uniform point grids and bounding boxes on diverse datasets. Oracle performance is measured with 1-pt and 1-box IoU, showcasing the effectiveness of the approach across datasets like iShape, which includes antenna, branch, fence, and more.
:::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 Links
3. Method and 3.1. Problem definition
3.2. Baseline and 3.3. Uni-OVSeg framework
4. Experiments
6. Broader impacts and References
B. Promptable segmentation.
Evaluation details. We perform a prompt segmentation evaluation on a wide range of datasets in various domains. For the point prompt, we adopt a uniform point grid h×w as input prompts (e.g., 20 × 20). For the box prompt, we use ground-truth bounding boxes as input prompts. 1-pt IoU denotes the oracle performance of one point by evaluating the intersection-overunion (IoU) of the predicted masks that best match ground truth. 1-box IoU denotes is similar to 1-pt IoU. More evaluation results are reported in Fig. 7, Fig. 8 and Fig. 9.
\ Dataset details. A description of each dataset is given in Tab. 5. The iShape dataset has 6 subsets, including antenna, branch, fence, hanger, log and wire.
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:::info This paper is available on arxiv under CC BY 4.0 DEED license.
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