TMLKD: Few-shot Trajectory Metric Learning via Knowledge Distillation
Summary: TMLKD: a knowledge-distillation framework for few-shot trajectory metric learning that tackles domain shift by adversarially separating domain-invariant from domain-specific features to transfer robust representations. Enriches sparse target labels via teachers' list-wise rank knowledge with adaptive reliability weighting to avoid misleading supervision; empirically outperforms baselines on three real datasets. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Danling Lai
- 2. Jiajie Xu
- 3. Jianfeng Qu
- 4. Pingfu Chao
- 5. Junhua Fang
- 6. Chengfei Liu
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| Rank | Cited Paper | Year | Venue | Pagerank |
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| 251 | Robust and Fast Similarity Search for Moving Object Trajectories | 2005 | SIGMOD | 0.00030644658 |
| 358 | On The Marriage of Lp-norms and Edit Distance | 2004 | VLDB | 0.0002599481 |
| 3,518 | FTW: Fast Similarity Search under the Time Warping Distance | 2005 | PODS | 7.0153323e-05 |
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