In this paper, we address the click-based interactive segmentation task with a novel transformer network. Transformer-based approaches show promising results in various computer vision tasks. However, all modern interactive segmentation methods are still based on convolutional networks. We propose a transformer network for interactive segmentation and explore three different ways to feed click information into neural networks. Through evaluation, we show that our model sets a new click-based state-of-the-art on GrabCut, Berkeley, SBD, DAVIS, and Pascal VOC in terms of NoC (Number of Clicks) and mIoU. The source code will be publicly available upon publication.

https://ieeexplore.ieee.org/abstract/document/9897542