TAT-VPR: Ternary Adaptive Transformer for Dynamic and Efficient Visual Place Recognition
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908374336012288 |
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| author | Grainge, Oliver Milford, Michael Bodala, Indu Ramchurn, Sarvapali D. Ehsan, Shoaib |
| author_facet | Grainge, Oliver Milford, Michael Bodala, Indu Ramchurn, Sarvapali D. Ehsan, Shoaib |
| contents | TAT-VPR is a ternary-quantized transformer that brings dynamic accuracy-efficiency trade-offs to visual SLAM loop-closure. By fusing ternary weights with a learned activation-sparsity gate, the model can control computation by up to 40% at run-time without degrading performance (Recall@1). The proposed two-stage distillation pipeline preserves descriptor quality, letting it run on micro-UAV and embedded SLAM stacks while matching state-of-the-art localization accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16447 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TAT-VPR: Ternary Adaptive Transformer for Dynamic and Efficient Visual Place Recognition Grainge, Oliver Milford, Michael Bodala, Indu Ramchurn, Sarvapali D. Ehsan, Shoaib Computer Vision and Pattern Recognition TAT-VPR is a ternary-quantized transformer that brings dynamic accuracy-efficiency trade-offs to visual SLAM loop-closure. By fusing ternary weights with a learned activation-sparsity gate, the model can control computation by up to 40% at run-time without degrading performance (Recall@1). The proposed two-stage distillation pipeline preserves descriptor quality, letting it run on micro-UAV and embedded SLAM stacks while matching state-of-the-art localization accuracy. |
| title | TAT-VPR: Ternary Adaptive Transformer for Dynamic and Efficient Visual Place Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.16447 |