Evaluating Promptable Segmentation with Uniform Point Grids and Bounding Boxes on Diverse Datasets

:::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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Abstract and 1. Introduction

2. Related works

3. Method and 3.1. Problem definition

3.2. Baseline and 3.3. Uni-OVSeg framework

4. Experiments

4.1. Implementation details

4.2. Main results

4.3. Ablation study

5. Conclusion

6. Broader impacts and References

\ A. Framework details

B. Promptable segmentation

C. Visualisation

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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