# Hotels-50K: A Global Hotel Recognition Dataset

arXiv: https://arxiv.org/abs/1901.11397 · HTML: https://mlsanity.com/paper/1901.11397/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2019.

## Built on by (most cited first, 13 of 13)

- [Inside Out Visual Place Recognition](https://mlsanity.com/paper/2111.13546/) (2021)
- [Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval](https://mlsanity.com/paper/2302.01332/) (2023)
- [ConText-CIR: Learning from Concepts in Text for Composed Image Retrieval](https://mlsanity.com/paper/2505.20764/) (2025)
- [The 2021 Hotel-ID to Combat Human Trafficking Competition Dataset](https://mlsanity.com/paper/2106.05746/) (2021)
- [good4cir: Generating Detailed Synthetic Captions for Composed Image Retrieval](https://mlsanity.com/paper/2503.17871/) (2025)
- [GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised Learning](https://mlsanity.com/paper/2401.01990/) (2024)
- [Computer Vision for Multimedia Geolocation in Human Trafficking Investigation: A Systematic Literature Review](https://mlsanity.com/paper/2402.15448/) (2024)
- [QuARI: Query Adaptive Retrieval Improvement](https://mlsanity.com/paper/2505.21647/) (2025)
- [Hotel Recognition via Latent Image Embedding](https://mlsanity.com/paper/2106.08042/) (2021)
- [Revisiting Hotels-50K and Hotel-ID](https://mlsanity.com/paper/2207.10200/) (2022)
- [Design and Evaluation of Camera-Centric Mobile Crowdsourcing Applications](https://mlsanity.com/paper/2409.03012/) (2024)
- [VAAS: Vision-Attention Anomaly Scoring for Image Manipulation Detection in Digital Forensics](https://mlsanity.com/paper/2512.15512/) (2025)
- [Plug to Place: Indoor Multimedia Geolocation from Electrical Sockets for Digital Investigation](https://mlsanity.com/paper/2512.16620/) (2025)

## Abstract (arXiv)

Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have been trafficked, and where their traffickers might move them or others in the future. Recognizing the hotel from images is challenging because of low image quality, uncommon camera perspectives, large occlusions (often the victim), and the similarity of objects (e.g., furniture, art, bedding) across different hotel rooms. To support efforts towards this hotel recognition task, we have curated a dataset of over 1 million annotated hotel room images from 50,000 hotels. These images include professionally captured photographs from travel websites and crowd-sourced images from a mobile application, which are more similar to the types of images analyzed in real-world investigations. We present a baseline approach based on a standard network architecture and a collection of data-augmentation approaches tuned to this problem domain.