# The 2021 Hotel-ID to Combat Human Trafficking Competition Dataset

arXiv: https://arxiv.org/abs/2106.05746 · HTML: https://mlsanity.com/paper/2106.05746/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 10 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [Deep Metric Learning via Lifted Structured Feature Embedding](https://mlsanity.com/paper/1511.06452/) (2015)
- [Hard negative examples are hard, but useful](https://mlsanity.com/paper/2007.12749/) (2020)
- [Improved Embeddings with Easy Positive Triplet Mining](https://mlsanity.com/paper/1904.04370/) (2019)
- [Hotels-50K: A Global Hotel Recognition Dataset](https://mlsanity.com/paper/1901.11397/) (2019)
- [TraffickCam: Explainable Image Matching For Sex Trafficking Investigations](https://mlsanity.com/paper/1910.03455/) (2019)

## Built on by (most cited first, 4 of 4)

- [GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised Learning](https://mlsanity.com/paper/2401.01990/) (2024)
- [Revisiting Hotels-50K and Hotel-ID](https://mlsanity.com/paper/2207.10200/) (2022)
- [Hierarchical mutual distillation for multi-view fusion: Learning from all possible view combinations](https://mlsanity.com/paper/2411.10077/) (2024)
- [Plug to Place: Indoor Multimedia Geolocation from Electrical Sockets for Digital Investigation](https://mlsanity.com/paper/2512.16620/) (2025)

## Abstract (arXiv)

Hotel recognition is an important task for human trafficking investigations since victims are often photographed in hotel rooms. Identifying these hotels is vital to trafficking investigations since they can help track down current and future victims who might be taken to the same places. Hotel recognition is a challenging fine grained visual classification task as there can be little similarity between different rooms within the same hotel, and high similarity between rooms from different hotels (especially if they are from the same chain). Hotel recognition to combat human trafficking poses additional challenges as investigative images are often low quality, contain uncommon camera angles and are highly occluded. Here, we present the 2021 Hotel-ID dataset to help raise awareness for this problem and generate novel approaches. The dataset consists of hotel room images that have been crowd-sourced and uploaded through the TraffickCam mobile application. The quality of these images is similar to investigative images and hence models trained on these images have good chances of accurately narrowing down on the correct hotel.