mlsanityData to 7 Oct 2026

The 2021 Hotel-ID to Combat Human Trafficking Competition Dataset

arXiv 2106.05746 ↗First posted 10 Jun 2021cs.CVcited by 4

Not on the map. Why?

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.

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

Builds on

7 of 14 references.

References · 7

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2019When Does Label Smoothing Help?cited by 985
  3. 2015Deep Metric Learning via Lifted Structured Feature Embeddingcited by 449
  4. 2020Hard negative examples are hard, but usefulcited by 82
  5. 2019Improved Embeddings with Easy Positive Triplet Miningcited by 66
  6. 2019Hotels-50K: A Global Hotel Recognition Datasetcited by 13
  7. 2019TraffickCam: Explainable Image Matching For Sex Trafficking Investigationscited by 3

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 1.

4 papers.