# Learning landmark guided embeddings for animal re-identification

arXiv: https://arxiv.org/abs/2001.02801 · HTML: https://mlsanity.com/paper/2001.02801/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2020.

## Built on by (most cited first, 8 of 8)

- [SealID: Saimaa ringed seal re-identification dataset](https://mlsanity.com/paper/2206.02260/) (2022)
- [Transformer for Object Re-Identification: A Survey](https://mlsanity.com/paper/2401.06960/) (2024)
- [EDEN: Deep Feature Distribution Pooling for Saimaa Ringed Seals Pattern Matching](https://mlsanity.com/paper/2105.13979/) (2021)
- [MOO: A Multi-view Oriented Observations Dataset for Viewpoint Analysis in Cattle Re-Identification](https://mlsanity.com/paper/2603.04314/) (2026)
- [Recurrence over Video Frames (RoVF) for the Re-identification of Meerkats](https://mlsanity.com/paper/2406.13002/) (2024)
- [Parameter-Efficient Vision-Language Adaptation with Continuous Metadata Conditioning for Animal Re-Identification](https://mlsanity.com/paper/2607.09443/) (2026)
- [Towards Visual Re-Identification of Fish using Fine-Grained Classification for Electronic Monitoring in Fisheries](https://mlsanity.com/paper/2512.08400/) (2025)
- [Spotted: Location-informed Reidentification of Hyenas and Leopards in Camera Trap Surveys](https://mlsanity.com/paper/2607.00804/) (2026)

## Abstract (arXiv)

Re-identification of individual animals in images can be ambiguous due to subtle variations in body markings between different individuals and no constraints on the poses of animals in the wild. Person re-identification is a similar task and it has been approached with a deep convolutional neural network (CNN) that learns discriminative embeddings for images of people. However, learning discriminative features for an individual animal is more challenging than for a person's appearance due to the relatively small size of ecological datasets compared to labelled datasets of person's identities. We propose to improve embedding learning by exploiting body landmarks information explicitly. Body landmarks are provided to the input of a CNN as confidence heatmaps that can be obtained from a separate body landmark predictor. The model is encouraged to use heatmaps by learning an auxiliary task of reconstructing input heatmaps. Body landmarks guide a feature extraction network to learn the representation of a distinctive pattern and its position on the body. We evaluate the proposed method on a large synthetic dataset and a small real dataset. Our method outperforms the same model without body landmarks input by 26% and 18% on the synthetic and the real datasets respectively. The method is robust to noise in input coordinates and can tolerate an error in coordinates up to 10% of the image size.