mlsanityData to 14 Oct 2026

CATALOG: A Camera Trap Language-guided Contrastive Learning Model

arXiv 2412.10624 ↗First posted 14 Dec 2024cs.CV cs.LGcited by 6

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Abstract (arXiv)

Foundation Models (FMs) have been successful in various computer vision tasks like image classification, object detection and image segmentation. However, these tasks remain challenging when these models are tested on datasets with different distributions from the training dataset, a problem known as domain shift. This is especially problematic for recognizing animal species in camera-trap images where we have variability in factors like lighting, camouflage and occlusions. In this paper, we propose the Camera Trap Language-guided Contrastive Learning (CATALOG) model to address these issues. Our approach combines multiple FMs to extract visual and textual features from camera-trap data and uses a contrastive loss function to train the model. We evaluate CATALOG on two benchmark datasets and show that it outperforms previous state-of-the-art methods in camera-trap image recognition, especially when the training and testing data have different animal species or come from different geographical areas. Our approach demonstrates the potential of using FMs in combination with multi-modal fusion and contrastive learning for addressing domain shifts in camera-trap image recognition. The code of CATALOG is publicly available at https://github.com/Julian075/CATALOG.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 31 of its 41 references that are in the map. First posted on arXiv 14 Dec 2024.

Builds on

31 of 41 references.

References · 31

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19 more
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  15. 2016Towards Automatic Wild Animal Monitoring: Identification of Animal Species in Camera-trap Images using Very Deep Convolutional Neural Networkscited by 28
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.

  1. 2025BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learningcited by 50
  2. 2026Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Timecited by 3
  3. 2026WildIng: A Wildlife Image Invariant Representation Model for Geographical Domain Shiftcited by 1
  4. 2026From Visual to Multimodal: Systematic Ablation of Encoders and Fusion Strategies in Animal Identificationcited by 0
  5. 2026Scout: Open-World Species Recognition on the Edgecited by 0
  6. 2026trACT: temporal revelation Airborne Camera Trapcited by 0