Flexible and Efficient Spatio-Temporal Transformer for Sequential Visual Place Recognition
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911520630243328 |
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| author | Kiu, Yu Lau Chen, Chao Jin, Ge Feng, Chen |
| author_facet | Kiu, Yu Lau Chen, Chao Jin, Ge Feng, Chen |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04282 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Flexible and Efficient Spatio-Temporal Transformer for Sequential Visual Place Recognition Kiu, Yu Lau Chen, Chao Jin, Ge Feng, Chen Computer Vision and Pattern Recognition 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/. |
| title | Flexible and Efficient Spatio-Temporal Transformer for Sequential Visual Place Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.04282 |