The paper introduces a one-stage end-to-end model for wildlife instance-level recognition that integrates detection and re-identification within a single latent space. It leverages DINOv2 for spatial geometry and MegaDescriptor for re-identification, while enhancing latent queries with prompt re-identification features. Preliminary results show a competitive mean average precision of 30.584% compared to the state-of-the-art two-stage approach of 44.89%, with qualitative evidence of effective bounding and identification of animal identities.
By Mufhumudzi Muthivhi, Jiahao Huo, Terence van Zyl, Fredrik Gustafsson
The study investigates whether Vision Transformer (ViT)-based animal re-identification models learn biologically meaningful concepts. Using a DINOv3 backbone fine‑tuned on Western lowland gorilla images, the authors find that sex and age emerge as linear directions in the model’s representations, generalizing to unseen individuals with high AUROC scores. They demonstrate that the sex direction is causally used by the model, that fine‑tuning relocates these concepts within the network, and that the representations reflect a graded biological axis encoded redundantly across the population.
By Robert Nolting, Alexandra Schild, Moritz Weckbecker, Maximilian Schall, Gerard de Melo
arXiv:2608.21281v1 Announce Type: new
Abstract: Recent advances in field technology have led to a massive influx of in-the-wild video data for ecological science. The primary bottleneck in leveraging...
By Abigail G. Grassick, Jerome Tze-Hou Hsu, Ethan Lin, Ziang Liu, Max Whitton, Madelyn Hair, Liam Gutierrez, Haozheng Yu, Kristin Branson, Vivek Jayaraman, Michael A. Gil, Andrew M. Hein, Jennifer J. Sun
arXiv:2610.32848v1 Announce Type: new
Abstract: Non-invasive camera-trap monitoring of endangered wildlife requires open-set individual identification---grouping field images into identities without...
By Yichun Peng, Yansong Li, Jin Hou, Qi Song, Xi Guo, Ning Zhang, Degang Wei, Huang Yang, Yu Guan, Jianping Ge, Lei Bao, Tianming Wang
The paper presents MaxBoxCount, the winning solution to the iWildCam 2021 Challenge, which tackles counting animals in camera‑trap image sequences without using count labels. It combines a robust species classification pipeline with a counting heuristic based on MegaDetector detections to estimate the number of unique individuals across short image bursts. The method addresses challenges posed by temporal discontinuities and the high cost of manual count annotations.
By Fagner Cunha, Juan G. Colonna, Eulanda M. dos Santos
arXiv:2603.04163v2 Announce Type: replace
Abstract: Wildlife re-identification aims to recognise individual animals by matching query images to a database of previously identified individuals, based...
By Thanos Polychronou, Luk\'a\v{s} Adam, Viktor Penchev, Kostas Papafitsoros
arXiv:2610.07384v1 Announce Type: cross
Abstract: Individual animal re-identification from camera-trap imagery is an instance retrieval problem central to non-invasive wildlife monitoring: a query im...
By Turhan Can Kargin, Piotr Kubaty, Ekaterina Rostovskaya, Izabela Wierzbowska, Bartosz Zieli\'nski, Marcin Przewi\k{e}\'zlikowski
The paper introduces Counterfactual Foreground Retrieval (CFR), an evidence‑aware evaluation protocol for automated individual animal identification. CFR assesses retrieval performance under three conditions: full images, foreground‑only, and shortcut‑only (background or silhouette). By comparing these views, CFR produces an evidence profile that reveals whether models rely on animal features or on identity‑correlated shortcuts, and the authors provide masks and code for 34 datasets to support this analysis.
By Ankit K. Upadhyay, Aleksandr Algasov, Ekaterina Nepovinnykh, Fedor Zolotarev, Tuomas Eerola, Heikki K\"alvi\"ainen, Charles V. Stewart, Lukas Picek
PuTR-CouT is a transformer‑based counting‑by‑tracking framework designed for camera‑trap image sequences. It generates synthetic training data using structural priors to create pseudo‑tracking labels, enabling the tracker to associate detections across frames and estimate per‑species counts. The method improves upon the MaxBoxCount baseline on the iWildCam 2021 benchmark, offering competitive counting results along with multi‑species predictions and track‑level verification.
By Fagner Cunha, Juan G. Colonna, Eulanda M. dos Santos
SAM 3D Animal is a promptable framework that performs multi‑animal 3D reconstruction from a single image, using the SMAL+ parametric animal model. It jointly reconstructs multiple instances and allows flexible prompts such as keypoints and masks to better handle crowded and occluded scenes. The authors also introduce Herd3D, a new dataset of over 5,000 images that increases species, interaction, and occlusion diversity, and demonstrate state‑of‑the‑art performance on several benchmark datasets.
By Xuyi Hu, Jin Lyu, Jiuming Liu, Yebin Liu, Silvia Zuffi, Liang An, Stefan Goetz
The paper introduces MetaWild, a multimodal animal re-identification dataset that pairs 20,890 images from six species with environmental metadata such as temperature and circadian rhythms. It also proposes the Meta-Feature Adapter (MFA), a lightweight module that integrates this metadata into vision-language models for re-identification tasks. Experiments demonstrate that adding MFA consistently improves re-identification performance over visual-only baselines.
By Yuzhuo Li, Di Zhao, Tingrui Qiao, Yihao Wu, Bo Pang, Yun Sing Koh
Nereus is a new large-scale underwater dataset that supports fine-grained object attribute understanding and grounded counting perception. It contains 88,000 images, 2.68 million question-answer pairs, and annotations for 136,000 marine organism objects spanning 2,690 species and 1,515 genera, along with 65,000 point annotations and 67,000 grounded counting QA pairs. Experiments with multimodal models show that Nereus improves downstream adaptation over existing underwater-domain data and provides a foundation for future research on fine-grained marine perception.
By Jingtao Deng, Zihao Jin, Yuehe Chen, Feixiang He, Shuiwang Li, Dan Zeng