Interactive Image Segmentation with First Click Attention

Zheng Lin , Zhao Zhang , Lin-Zhuo Chen , Ming-Ming Cheng, Shao-Ping Lu*


In the task of interactive image segmentation, users initially click one point to segment the main body of the target object and then provide more points on mislabeled regions iteratively for a precise segmentation. Existing methods treat all interaction points indiscriminately, ignoring the difference between the first click and the remaining ones. In this paper, we demonstrate the critical role of the first click about providing the location and main body information of the target object. A deep framework, named First Click Attention Network (FCA-Net), is proposed to make better use of the first click. In this network, the interactive segmentation result can be much improved with the following benefits: focus invariance, location guidance, and error-tolerant ability. We then put forward a click-based loss function and a structural integrity strategy for better segmentation effect. The visualized segmentation results and sufficient experiments on five datasets demonstrate the importance of the first click and the superiority of our FCA-Net.


Interactive Image Segmentation with First Click Attention, Zheng Lin, Zhao Zhang, Lin-Zhuo Chen, Ming-Ming Cheng, Shao-Ping Lu*, IEEE CVPR, 2020, (CCF-A) [ pdf | code | 中译版 ]

title={Interactive Image Segmentation with First Click Attention},
author={Zheng Lin and Zhao Zhang and Lin-Zhuo Chen and Ming-Ming Cheng and Shao-Ping Lu},
booktitle={IEEE CVPR},




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中国人工智能学会-华为 MindSpore 学术奖励基金项目(CAAI-Huawei Open Fund)