提出可变序列长度的高效时空变换器，提升视觉定位精度与实时性。
Flexible and Efficient Spatio-Temporal Transformer for Sequential Visual Place Recognition
- 用循环可变形编码器迭代融合多帧信息，支持任意序列长度。
- 在三个数据集上召回率最高提升17%，推理时间减少36%。
- 适合需要快速、低内存运行的实时视觉定位场景。
序列视觉位置识别（Seq-VPR）利用变换器有效捕捉时空特征。实际应用中，基于变换器的Seq-VPR模型需适应不同长度序列、实现快速推理并保持低内存占用，以满足实时性要求。然而，现有方法往往以牺牲灵活性和效率为代价追求性能。为此，我们提出Adapt-STformer，其核心是新型循环可变形变换器编码器（Recurrent-DTE），通过迭代循环机制融合多帧信息。该设计天然支持可变序列长度，实现快速推理与低内存使用。在Nordland、Oxford和NuScenes数据集上的实验表明，Adapt-STformer相较最优基线模型，召回率最高提升17%，序列提取时间降低36%，内存使用减少35%。代码已开源：https://ai4ce.github.io/Adapt-STFormer/
原文摘要 · Abstract (English)
Sequential Visual Place Recognition (Seq-VPR) leverages transformers to capture spatio-temporal features effectively. In practice, a transformer-based Seq-VPR model should be flexible to the number of frames per sequence (seq- length), deliver fast inference, and have low memory usage to meet real-time constraints. However, existing approaches prioritize performance at the expense of flexibility and effi- ciency. To address this gap, we propose Adapt-STformer, a Seq-VPR method built around our novel Recurrent Deformable Transformer Encoder (Recurrent-DTE), which uses an iterative recurrent mechanism to fuse information from multiple sequen- tial frames. This design naturally supports variable seq-lengths, fast inference, and low memory usage. Experiments on the Nordland, Oxford, and NuScenes datasets show that Adapt- STformer boosts recall by up to 17% while reducing sequence extraction time by 36% and lowering memory usage by 35% relative to our best comparable baseline. Our code is released at https://ai4ce.github.io/Adapt-STFormer/.
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