Flexible and Efficient Spatio-Temporal Transformer for Sequential Visual Place 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/.
Code (0)
등록된 구현이 없습니다.
Tasks
Visual Place RecognitionResults from the Paper
| Rank | Task | Dataset | Model | Metrics |
|---|---|---|---|---|
| #15 | Visual Place Recognition | Nordland | Adapt-STformer | Recall@1: 17 |
Similar Papers 제목 키워드 기반
STPFormer: A State-of-the-Art Pattern-Aware Spatio-Temporal Transformer for Traffic Forecasting
Spatio-temporal traffic forecasting is challenging due to complex temporal patterns, dynamic spatial structures, and diverse input formats. Although Transformer-based models offer strong global modeling, they often strug…
Representation LearningGraph MatchingSpacetime $E(n)$-Transformer: Equivariant Attention for Spatio-temporal Graphs
We introduce an $E(n)$-equivariant Transformer architecture for spatio-temporal graph data. By imposing rotation, translation, and permutation equivariance inductive biases in both space and time, we show that the Spacet…
TranslationShifted Chunk Transformer for Spatio-Temporal Representational Learning
Spatio-temporal representational learning has been widely adopted in various fields such as action recognition, video object segmentation, and action anticipation. Previous spatio-temporal representational learning appro…
Action AnticipationAction Recognitionimage-classificationImage Classification+3IVT: An End-to-End Instance-guided Video Transformer for 3D Pose Estimation
Video 3D human pose estimation aims to localize the 3D coordinates of human joints from videos. Recent transformer-based approaches focus on capturing the spatiotemporal information from sequential 2D poses, which cannot…
2D Pose Estimation3D Human Pose Estimation3D Multi-Person Pose Estimation3D Pose Estimation+1Deepfake Video Detection with Spatiotemporal Dropout Transformer
While the abuse of deepfake technology has caused serious concerns recently, how to detect deepfake videos is still a challenge due to the high photo-realistic synthesis of each frame. Existing image-level approaches oft…
Data AugmentationFace Swapping