# The FathomNet2023 Competition Dataset

arXiv: https://arxiv.org/abs/2307.08781 · HTML: https://mlsanity.com/paper/2307.08781/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Recognition in Terra Incognita](https://mlsanity.com/paper/1807.04975/) (2018)
- [Generalized Category Discovery](https://mlsanity.com/paper/2201.02609/) (2022)
- [Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time](https://mlsanity.com/paper/2211.14238/) (2022)
- [FathomNet: A global image database for enabling artificial intelligence in the ocean](https://mlsanity.com/paper/2109.14646/) (2021)
- [Continual Learning with Evolving Class Ontologies](https://mlsanity.com/paper/2210.04993/) (2022)
- [Taxonomic Class Incremental Learning](https://mlsanity.com/paper/2304.05547/) (2023)

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

- [FathomVerse: A community science dataset for ocean animal discovery](https://mlsanity.com/paper/2412.01701/) (2024)

## Abstract (arXiv)

Ocean scientists have been collecting visual data to study marine organisms for decades. These images and videos are extremely valuable both for basic science and environmental monitoring tasks. There are tools for automatically processing these data, but none that are capable of handling the extreme variability in sample populations, image quality, and habitat characteristics that are common in visual sampling of the ocean. Such distribution shifts can occur over very short physical distances and in narrow time windows. Creating models that are able to recognize when an image or video sequence contains a new organism, an unusual collection of animals, or is otherwise out-of-sample is critical to fully leverage visual data in the ocean. The FathomNet2023 competition dataset presents a realistic scenario where the set of animals in the target data differs from the training data. The challenge is both to identify the organisms in a target image and assess whether it is out-of-sample.