# WildFusion: Individual Animal Identification with Calibrated Similarity Fusion

arXiv: https://arxiv.org/abs/2408.12934 · HTML: https://mlsanity.com/paper/2408.12934/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 69 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Builds on (most cited first, 24 of 24)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [DISK: Learning local features with policy gradient](https://mlsanity.com/paper/2006.13566/) (2020)
- [BioCLIP: A Vision Foundation Model for the Tree of Life](https://mlsanity.com/paper/2311.18803/) (2023)
- [ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation](https://mlsanity.com/paper/2304.03608/) (2023)
- [WildlifeDatasets: An open-source toolkit for animal re-identification](https://mlsanity.com/paper/2311.09118/) (2023)
- [Perspectives on individual animal identification from biology and computer vision](https://mlsanity.com/paper/2103.00560/) (2021)
- [SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification](https://mlsanity.com/paper/2211.10307/) (2022)
- [NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation](https://mlsanity.com/paper/2005.13359/) (2020)
- [SealID: Saimaa ringed seal re-identification dataset](https://mlsanity.com/paper/2206.02260/) (2022)
- [Towards Self-Supervision for Video Identification of Individual Holstein-Friesian Cattle: The Cows2021 Dataset](https://mlsanity.com/paper/2105.01938/) (2021)
- [Face Recognition: Primates in the Wild](https://mlsanity.com/paper/1804.08790/) (2018)
- [Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings](https://mlsanity.com/paper/1902.10847/) (2019)
- [Integral Curvature Representation and Matching Algorithms for Identification of Dolphins and Whales](https://mlsanity.com/paper/1708.07785/) (2017)
- [Where Is My Puppy? Retrieving Lost Dogs by Facial Features](https://mlsanity.com/paper/1510.02781/) (2015)

## Built on by (most cited first, 9 of 9)

- [Active Learning for Animal Re-Identification with Ambiguity-Aware Sampling](https://mlsanity.com/paper/2511.06658/) (2025)
- [Are We Recognizing the Jaguar or Its Background? A Diagnostic Framework for Jaguar Re-Identification](https://mlsanity.com/paper/2604.09690/) (2026)
- [DS@GT ARC at AnimalCLEF 2026: Species-Aware Graph Construction for Multi-Species Animal Re-Identification](https://mlsanity.com/paper/2607.16453/) (2026)
- [Re-Identifying K=ak=a with AI-Automated Video Key Frame Extraction](https://mlsanity.com/paper/2510.08775/) (2025)
- [Near-perfect photo-ID of the Hula painted frog with zero-shot deep local-feature matching](https://mlsanity.com/paper/2601.08798/) (2026)
- [From Visual to Multimodal: Systematic Ablation of Encoders and Fusion Strategies in Animal Identification](https://mlsanity.com/paper/2603.02270/) (2026)
- [Centering Ecological Goals in Automated Identification of Individual Animals](https://mlsanity.com/paper/2604.20626/) (2026)
- [Calibrated Similarity and Graph Clustering for Open-Set Animal Re-Identification](https://mlsanity.com/paper/2608.02469/) (2026)
- [SLAP: Selective Local Vision-Language Alignment for Fish Re-Identification via Partial Optimal Transport](https://mlsanity.com/paper/2608.08840/) (2026)

## Abstract (arXiv)

We propose a new method - WildFusion - for individual identification of a broad range of animal species. The method fuses deep scores (e.g., MegaDescriptor or DINOv2) and local matching similarity (e.g., LoFTR and LightGlue) to identify individual animals. The global and local information fusion is facilitated by similarity score calibration. In a zero-shot setting, relying on local similarity score only, WildFusion achieved mean accuracy, measured on 17 datasets, of 76.2%. This is better than the state-of-the-art model, MegaDescriptor-L, whose training set included 15 of the 17 datasets. If a dataset-specific calibration is applied, mean accuracy increases by 2.3% percentage points. WildFusion, with both local and global similarity scores, outperforms the state-of-the-art significantly - mean accuracy reached 84.0%, an increase of 8.5 percentage points; the mean relative error drops by 35%. We make the code and pre-trained models publicly available5, enabling immediate use in ecology and conservation.