# The Animal ID Problem: Continual Curation

arXiv: https://arxiv.org/abs/2106.10377 · HTML: https://mlsanity.com/paper/2106.10377/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 18 Jun 2021.

## Builds on (most cited first, 14 of 14)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [A Metric Learning Reality Check](https://mlsanity.com/paper/2003.08505/) (2020)
- [Towards Open World Recognition](https://mlsanity.com/paper/1412.5687/) (2014)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [Perspectives on individual animal identification from biology and computer vision](https://mlsanity.com/paper/2103.00560/) (2021)
- [Face Recognition: Primates in the Wild](https://mlsanity.com/paper/1804.08790/) (2018)
- [Similarity Learning Networks for Animal Individual Re-Identification -- Beyond the Capabilities of a Human Observer](https://mlsanity.com/paper/1902.09324/) (2019)
- [Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings](https://mlsanity.com/paper/1902.10847/) (2019)
- [Automated Visual Fin Identification of Individual Great White Sharks](https://mlsanity.com/paper/1609.06323/) (2016)
- [ATRW: A Benchmark for Amur Tiger Re-identification in the Wild](https://mlsanity.com/paper/1906.05586/) (2019)

## Built on by (most cited first, 5 of 5)

- [FathomGPT: A Natural Language Interface for Interactively Exploring Ocean Science Data](https://mlsanity.com/paper/2412.02784/) (2024)
- [Conservation Tools: The Next Generation of Engineering--Biology Collaborations](https://mlsanity.com/paper/2301.01103/) (2023)
- [Teaching Computer Vision for Ecology](https://mlsanity.com/paper/2301.02211/) (2023)
- [Degradation-based augmented training for robust individual animal re-identification](https://mlsanity.com/paper/2603.04163/) (2026)
- [Centering Ecological Goals in Automated Identification of Individual Animals](https://mlsanity.com/paper/2604.20626/) (2026)

## Abstract (arXiv)

Hoping to stimulate new research in individual animal identification from images, we propose to formulate the problem as the human-machine Continual Curation of images and animal identities. This is an open world recognition problem, where most new animals enter the system after its algorithms are initially trained and deployed. Continual Curation, as defined here, requires (1) an improvement in the effectiveness of current recognition methods, (2) a pairwise verification algorithm that allows the possibility of no decision, and (3) an algorithmic decision mechanism that seeks human input to guide the curation process. Error metrics must evaluate the ability of recognition algorithms to identify not only animals that have been seen just once or twice but also recognize new animals not in the database. An important measure of overall system performance is accuracy as a function of the amount of human input required.