SIMformer: Single-Layer Vanilla Transformer Can Learn Free-Space Trajectory Similarity
Summary: SIMformer shows that a single-layer vanilla Transformer can learn free-space trajectory similarity without triplet supervision or auxiliary information. Tailored non-Euclidean representation similarities mitigate dimensionality collapse while improving accuracy, speed, and scalability over prior embedding methods. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Chuang Yang (University of Tokyo)
- 2. Renhe Jiang (University of Tokyo)
- 3. Xiaohang Xu (University of Tokyo)
- 4. Chuan Xiao (Osaka University)
- 5. Kaoru Sezaki (University of Tokyo)
BibTeX Citation
@article{yang_vldb25,
title = {{SIMformer: Single-Layer Vanilla Transformer Can Learn Free-Space Trajectory Similarity}},
author = {Yang, Chuang and Jiang, Renhe and Xu, Xiaohang and Xiao, Chuan and Sezaki, Kaoru},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {2},
pages = {390--398},
doi = {10.14778/3705829.3705853},
url = {https://doi.org/10.14778/3705829.3705853},
year = {2025}
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Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,046 | Distributed Trajectory Similarity Search | 2017 | VLDB | 9.260657e-05 |
| 2,416 | DITA: Distributed In-Memory Trajectory Analytics | 2018 | SIGMOD | 8.6068712e-05 |
| 4,803 | Subtrajectory Clustering: Models and Algorithms | 2018 | PODS | 6.5016379e-05 |
| 5,071 | Fast Subtrajectory Similarity Search in Road Networks under Weighted Edit Distance Constraints | 2020 | VLDB | 6.3761034e-05 |
| 5,112 | Fast Large-Scale Trajectory Clustering | 2020 | VLDB | 6.3596697e-05 |
| 6,953 | Trajectory Similarity Measurement: An Efficiency Perspective | 2024 | VLDB | 5.7303405e-05 |
| 7,155 | VRE: A Versatile, Robust, and Economical Trajectory Data System | 2022 | VLDB | 5.6869288e-05 |
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