Interactive Object Segmentation With Inside-Outside Guidance

CVPR 2020

Authors: Shiyin Zhang, Jun Hao Liew, Yunchao Wei, Shikui Wei, Yao Zhao Description: This paper explores how to harvest precise object segmentation masks while minimizing the human interaction cost. To achieve this, we propose an Inside-Outside Guidance (IOG) approach in this work. Concretely, we leverage an inside point that is clicked near the object center and two outside points at the symmetrical corner locations (top-left and bottom-right or top-right and bottom-left) of a tight bounding box that encloses the target object. This results in a total of one foreground click and four background clicks for segmentation. The advantages of our IOG is four-fold: 1) the two outside points can help to remove distractions from other objects or background. 2) the inside point can help to eliminate the unrelated regions inside the bounding box. 3) the inside and outside points are easily identified, reducing the confusion raised by the state-of-the-art DEXTR in labeling some extreme samples. 4) our approach naturally supports additional clicks annotations for further correction. Despite its simplicity, our IOG not only achieves state-of-the-art performance on several popular benchmarks, but also demonstrates strong generalization capability across different domains such as street scenes, aerial imagery and medical images, without fine-tuning. In addition, we also propose a simple two-stage solution that enables our IOG to produce high quality instance segmentation masks from existing datasets with off-the-shelf bounding boxes such as ImageNet and Open Images, demonstrating the superiority of our IOG as an annotation tool.