MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929232299425792 |
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| author | Xu, Long Li, Shanghong Chen, Yongquan Luo, Jun Lai, Shiwu |
| author_facet | Xu, Long Li, Shanghong Chen, Yongquan Luo, Jun Lai, Shiwu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_04403 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation Xu, Long Li, Shanghong Chen, Yongquan Luo, Jun Lai, Shiwu Computer Vision and Pattern Recognition 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. |
| title | MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.04403 |