# Revisiting Hotels-50K and Hotel-ID

arXiv: https://arxiv.org/abs/2207.10200 · HTML: https://mlsanity.com/paper/2207.10200/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Circle Loss: A Unified Perspective of Pair Similarity Optimization](https://mlsanity.com/paper/2002.10857/) (2020)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [Large-Scale Image Retrieval with Attentive Deep Local Features](https://mlsanity.com/paper/1612.06321/) (2016)
- [Proxy Anchor Loss for Deep Metric Learning](https://mlsanity.com/paper/2003.13911/) (2020)
- [SoftTriple Loss: Deep Metric Learning Without Triplet Sampling](https://mlsanity.com/paper/1909.05235/) (2019)
- [ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis](https://mlsanity.com/paper/2004.01113/) (2020)
- [Improved Embeddings with Easy Positive Triplet Mining](https://mlsanity.com/paper/1904.04370/) (2019)
- [Combination of Multiple Global Descriptors for Image Retrieval](https://mlsanity.com/paper/1903.10663/) (2019)
- [Hotels-50K: A Global Hotel Recognition Dataset](https://mlsanity.com/paper/1901.11397/) (2019)
- [An Entity Resolution approach to isolate instances of Human Trafficking online](https://mlsanity.com/paper/1509.06659/) (2015)
- [The 2021 Hotel-ID to Combat Human Trafficking Competition Dataset](https://mlsanity.com/paper/2106.05746/) (2021)
- [A Non-Parametric Learning Approach to Identify Online Human Trafficking](https://mlsanity.com/paper/1607.08691/) (2016)
- [Sex Trafficking Detection with Ordinal Regression Neural Networks](https://mlsanity.com/paper/1908.05434/) (2019)

## Built on by (most cited first, 1 of 1)

- [GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised Learning](https://mlsanity.com/paper/2401.01990/) (2024)

## Abstract (arXiv)

In this paper, we propose revisited versions for two recent hotel recognition datasets: Hotels50K and Hotel-ID. The revisited versions provide evaluation setups with different levels of difficulty to better align with the intended real-world application, i.e. countering human trafficking. Real-world scenarios involve hotels and locations that are not captured in the current data sets, therefore it is important to consider evaluation settings where classes are truly unseen. We test this setup using multiple state-of-the-art image retrieval models and show that as expected, the models' performances decrease as the evaluation gets closer to the real-world unseen settings. The rankings of the best performing models also change across the different evaluation settings, which further motivates using the proposed revisited datasets.