mlsanityData to 14 Oct 2026

Addressing the Elephant in the Room: Robust Animal Re-Identification with Unsupervised Part-Based Feature Alignment

arXiv 2405.13781 ↗First posted 22 May 2024cs.CVcited by 4

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Abstract (arXiv)

Animal Re-ID is crucial for wildlife conservation, yet it faces unique challenges compared to person Re-ID. First, the scarcity and lack of diversity in datasets lead to background-biased models. Second, animal Re-ID depends on subtle, species-specific cues, further complicated by variations in pose, background, and lighting. This study addresses background biases by proposing a method to systematically remove backgrounds in both training and evaluation phases. And unlike prior works that depend on pose annotations, our approach utilizes an unsupervised technique for feature alignment across body parts and pose variations, enhancing practicality. Our method achieves superior results on three key animal Re-ID datasets: ATRW, YakReID-103, and ELPephants.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 20 of its 33 references that are in the map. First posted on arXiv 22 May 2024.

Builds on

20 of 33 references.

References · 20

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8 more
  1. 2020Circle Loss: A Unified Perspective of Pair Similarity Optimizationcited by 403
  2. 2018Learning Discriminative Features with Multiple Granularities for Person Re-Identificationcited by 291
  3. 2022CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labelscited by 181
  4. 2021TransFG: A Transformer Architecture for Fine-grained Recognitioncited by 132
  5. 2018Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approachcited by 58
  6. 2019Unsupervised Learning of Landmarks by Descriptor Vector Exchangecited by 39
  7. 2020The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identificationcited by 39
  8. 2020Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Networkcited by 26

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 3.

4 papers.