MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation

• URL: http://arxiv.org/abs/2401.04403v2
• Date: Sat, 3 Feb 2024 03:50:42 GMT
• Title: MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation
• Authors: Long Xu, Shanghong Li, Yongquan Chen, Jun Luo, Shiwu Lai
• Abstract summary: We propose a novel multi-scale token adaptation algorithm for interactive segmentation. By performing top-k operations across multi-scale tokens, the computational complexity is greatly simplified. We also propose a token learning algorithm based on contrastive loss.
• Score: 8.46894039954642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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