Interactive segmentation has gained significant attention for its application in human-computer interaction and data annotation. To address the target scale variation issue in interactive segmentation, a novel multi-scale token adaptation algorithm is proposed. By performing top-k operations across multi-scale tokens, the computational complexity is greatly simplified while ensuring performance. To enhance the robustness of multi-scale token selection, we also propose a token learning algorithm based on contrastive loss. This algorithm can effectively improve the performance of multi-scale token adaptation. Extensive benchmarking shows that the algorithm achieves state-of-the-art (SOTA) performance, compared to current methods. An interactive demo and all reproducible codes will be released at https://github.com/hahamyt/mst.
@article{arxiv.2401.04403,
title = {MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation},
author = {Long Xu and Shanghong Li and Yongquan Chen and Jun Luo and Shiwu Lai},
journal= {arXiv preprint arXiv:2401.04403},
year = {2024}
}