Hotel Recognition via Latent Image Embedding

TL;DR

This paper introduces a novel Contrastive-Triplet loss for deep metric learning, improving hotel recognition accuracy, and provides a benchmarking pipeline tested on Hotels-50K and CUB200 datasets.

Contribution

It proposes a new Contrastive-Triplet loss and a benchmarking pipeline for hotel recognition using deep metric learning.

Findings

Contrastive-Triplet loss outperforms existing methods on Hotels-50K.

Benchmarking pipeline facilitates fair comparison of metric learning models.

Open-sourced code promotes reproducibility and further research.

Abstract

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.