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...
arXiv:2607. 09443v1 Announce Type: cross Abstract: Long-term animal re-identification (ReID) must remain robust to gradual morphological evolution and seasonal appearance shifts.
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...
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.
arXiv:2512.07776v2 Announce Type: replace Abstract: Monitoring critically endangered western lowland gorillas is currently hampered by the immense manual effort required to re-identify individuals fr...
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.
arXiv:2609.36560v1 Announce Type: cross Abstract: Object re-identification (ReID) faces a recurring challenge: different identities can share highly similar global appearances, while the cues that di...
Object re-identification (ReID) faces a recurring challenge: different identities can share highly similar global appearances, while the cues that distinguish them are localized, heterogeneous, and vi...
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.
arXiv:2606. 08484v1 Announce Type: cross Abstract: Joint Species Distribution Modeling (JSDM) is a key enabler for biodiversity monitoring and conservation planning.
PRIMA is a framework for recovering articulated 3D animal meshes from single images. It introduces taxonomic shape priors derived from BioCLIP image features to embed species-level knowledge, and an adaptation-driven bootstrapping method that converts abundant 2D annotations into reliable pseudo-3D supervision, producing the Quadruped3D dataset with about 9K pseudo-3D annotations. These components address the long-tailed distribution of species and limited 3D supervision, achieving state‑of‑the‑art performance on several animal mesh reconstruction benchmarks, especially for rare species and challenging poses.
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.