# Categorical Keypoint Positional Embedding for Robust Animal Re-Identification

arXiv: https://arxiv.org/abs/2412.00818 · HTML: https://mlsanity.com/paper/2412.00818/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 1 Dec 2024.

## Builds on (most cited first, 20 of 20)

- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Zero-shot Image-to-Image Translation](https://mlsanity.com/paper/2302.03027/) (2023)
- [Emergent Correspondence from Image Diffusion](https://mlsanity.com/paper/2306.03881/) (2023)
- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [Deep ViT Features as Dense Visual Descriptors](https://mlsanity.com/paper/2112.05814/) (2021)
- [PifPaf: Composite Fields for Human Pose Estimation](https://mlsanity.com/paper/1903.06593/) (2019)
- [Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer](https://mlsanity.com/paper/2106.04095/) (2021)
- [Unsupervised Semantic Correspondence Using Stable Diffusion](https://mlsanity.com/paper/2305.15581/) (2023)
- [Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification](https://mlsanity.com/paper/2205.02151/) (2022)
- [Pose-guided Feature Disentangling for Occluded Person Re-identification Based on Transformer](https://mlsanity.com/paper/2112.02466/) (2021)
- [WildlifeDatasets: An open-source toolkit for animal re-identification](https://mlsanity.com/paper/2311.09118/) (2023)
- [HAT: Hierarchical Aggregation Transformers for Person Re-identification](https://mlsanity.com/paper/2107.05946/) (2021)
- [Perspectives on individual animal identification from biology and computer vision](https://mlsanity.com/paper/2103.00560/) (2021)
- [Transformer for Object Re-Identification: A Survey](https://mlsanity.com/paper/2401.06960/) (2024)
- [Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings](https://mlsanity.com/paper/1902.10847/) (2019)
- [PetFace: A Large-Scale Dataset and Benchmark for Animal Identification](https://mlsanity.com/paper/2407.13555/) (2024)
- [Keypoint Promptable Re-Identification](https://mlsanity.com/paper/2407.18112/) (2024)
- [Integral Curvature Representation and Matching Algorithms for Identification of Dolphins and Whales](https://mlsanity.com/paper/1708.07785/) (2017)
- [Individual common dolphin identification via metric embedding learning](https://mlsanity.com/paper/1901.03662/) (2019)

## Abstract (arXiv)

Animal re-identification (ReID) has become an indispensable tool in ecological research, playing a critical role in tracking population dynamics, analyzing behavioral patterns, and assessing ecological impacts, all of which are vital for informed conservation strategies. Unlike human ReID, animal ReID faces significant challenges due to the high variability in animal poses, diverse environmental conditions, and the inability to directly apply pre-trained models to animal data, making the identification process across species more complex. This work introduces an innovative keypoint propagation mechanism, which utilizes a single annotated image and a pre-trained diffusion model to propagate keypoints across an entire dataset, significantly reducing the cost of manual annotation. Additionally, we enhance the Vision Transformer (ViT) by implementing Keypoint Positional Encoding (KPE) and Categorical Keypoint Positional Embedding (CKPE), enabling the ViT to learn more robust and semantically-aware representations. This provides more comprehensive and detailed keypoint representations, leading to more accurate and efficient re-identification. Our extensive experimental evaluations demonstrate that this approach significantly outperforms existing state-of-the-art methods across four wildlife datasets. The code will be publicly released.