Hotel Recognition via Latent Image Embedding
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Abstract (arXiv)
We approach the problem of hotel recognition with deep metric learning. We overview the existing approaches and propose a modification to Contrastive loss called Contrastive-Triplet loss. We construct a robust pipeline for benchmarking metric learning models and perform experiments on Hotels-50K and CUB200 datasets. Contrastive-Triplet loss is shown to achieve better retrieval on Hotels-50k. We open-source our code.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 28 references that are in the map. First posted on arXiv 15 Jun 2021.
Builds on
11 of 28 references.
References · 11
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- 2019Combination of Multiple Global Descriptors for Image Retrievalcited by 21
- 2019Hotels-50K: A Global Hotel Recognition Datasetcited by 13
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Citing papers per year: 2022: 1.
1 paper.