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arXiv · 2401.04403

MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation

Long Xu·Shanghong Li·Yongquan Chen·Jun Luo·Shiwu Lai
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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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Long Xu, Shanghong Li, Yongquan Chen, Jun Luo, Shiwu Lai. 2024-01-09. MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation. https://arxiv.org/abs/2401.04403

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