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arXiv · 2505.16447

TAT-VPR: Ternary Adaptive Transformer for Dynamic and Efficient Visual Place Recognition

Oliver Grainge·Michael Milford·Indu Bodala·Sarvapali D. Ramchurn·Shoaib Ehsan
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Abstract

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.

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Oliver Grainge, Michael Milford, Indu Bodala, Sarvapali D. Ramchurn, Shoaib Ehsan. 2025-05-22. TAT-VPR: Ternary Adaptive Transformer for Dynamic and Efficient Visual Place Recognition. https://arxiv.org/abs/2505.16447

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