  Interactive image segmentation aims to segment the target from the background
with the manual guidance, which takes as input multimodal data such as images,
clicks, scribbles, and bounding boxes. Recently, vision transformers have
achieved a great success in several downstream visual tasks, and a few efforts
have been made to bring this powerful architecture to interactive segmentation
task. However, the previous works neglect the relations between two modalities
and directly mock the way of processing purely visual information with
self-attentions. In this paper, we propose a simple yet effective network for
click-based interactive segmentation with cross-modality vision transformers.
Cross-modality transformers exploits mutual information to better guide the
learning process. The experiments on several benchmarks show that the proposed
method achieves superior performance in comparison to the previous
state-of-the-art models. The stability of our method in term of avoiding
failure cases shows its potential to be a practical annotation tool. The code
and pretrained models will be released under
https://github.com/lik1996/iCMFormer.
