# Zero-Shot Wildlife Sorting Using Vision Transformers: Evaluating Clustering and Continuous Similarity Ordering

arXiv: https://arxiv.org/abs/2510.14596 · HTML: https://mlsanity.com/paper/2510.14596/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 16 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Efficient Pipeline for Camera Trap Image Review](https://mlsanity.com/paper/1907.06772/) (2019)
- [A Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysis](https://mlsanity.com/paper/2008.12603/) (2020)

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

- [Fursee: Hybrid YOLO-DINOv3 Framework for Fursuit Identity Retrieval and Clustering](https://mlsanity.com/paper/2606.22872/) (2026)

## Abstract (arXiv)

Camera traps generate millions of wildlife images, yet many datasets contain species that are absent from existing classifiers. This work evaluates zero-shot approaches for organizing unlabeled wildlife imagery using self-supervised vision transformers, developed and tested within the Animal Detect platform for camera trap analysis. We compare unsupervised clustering methods (DBSCAN, GMM) across three architectures (CLIP, DINOv2, MegaDescriptor) combined with dimensionality reduction techniques (PCA, UMAP), and we demonstrate continuous 1D similarity ordering via t-SNE projection. On a 5-species test set with ground truth labels used only for evaluation, DINOv2 with UMAP and GMM achieves 88.6 percent accuracy (macro-F1 = 0.874), while 1D sorting reaches 88.2 percent coherence for mammals and birds and 95.2 percent for fish across 1,500 images. Based on these findings, we deployed continuous similarity ordering in production, enabling rapid exploratory analysis and accelerating manual annotation workflows for biodiversity monitoring.