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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.17996 |
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| _version_ | 1866912346388037632 |
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| author | Ouyang, Yuanbing Liang, Yizhuo Li, Qingpeng Guo, Xinfei Luo, Yiming Wu, Di Wang, Hao Pan, Yushan |
| author_facet | Ouyang, Yuanbing Liang, Yizhuo Li, Qingpeng Guo, Xinfei Luo, Yiming Wu, Di Wang, Hao Pan, Yushan |
| contents | Vision Transformers (ViTs) excel in semantic segmentation but demand significant computation, posing challenges for deployment on resource-constrained devices. Existing token pruning methods often overlook fundamental visual data characteristics. This study introduces 'LVTP', a progressive token pruning framework guided by multi-scale Tsallis entropy and low-level visual features with twice clustering. It integrates high-level semantics and basic visual attributes for precise segmentation. A novel dynamic scoring mechanism using multi-scale Tsallis entropy weighting overcomes limitations of traditional single-parameter entropy. The framework also incorporates low-level feature analysis to preserve critical edge information while optimizing computational cost. As a plug-and-play module, it requires no architectural changes or additional training. Evaluations across multiple datasets show 20%-45% computational reductions with negligible performance loss, outperforming existing methods in balancing cost and accuracy, especially in complex edge regions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17996 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning Ouyang, Yuanbing Liang, Yizhuo Li, Qingpeng Guo, Xinfei Luo, Yiming Wu, Di Wang, Hao Pan, Yushan Computer Vision and Pattern Recognition Vision Transformers (ViTs) excel in semantic segmentation but demand significant computation, posing challenges for deployment on resource-constrained devices. Existing token pruning methods often overlook fundamental visual data characteristics. This study introduces 'LVTP', a progressive token pruning framework guided by multi-scale Tsallis entropy and low-level visual features with twice clustering. It integrates high-level semantics and basic visual attributes for precise segmentation. A novel dynamic scoring mechanism using multi-scale Tsallis entropy weighting overcomes limitations of traditional single-parameter entropy. The framework also incorporates low-level feature analysis to preserve critical edge information while optimizing computational cost. As a plug-and-play module, it requires no architectural changes or additional training. Evaluations across multiple datasets show 20%-45% computational reductions with negligible performance loss, outperforming existing methods in balancing cost and accuracy, especially in complex edge regions. |
| title | Back to Fundamentals: Low-Level Visual Features Guided Progressive Token Pruning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.17996 |