Work

CATALOG: A Camera Trap Language-Guided Contrastive Learning Model

Year: 2025

Type: conference-paper

Abstract: 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.

Source: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

DOI: 10.1109/wacv61041.2025.00124

Authors: Julian D. Santamaria, Claudia Isaza, Jhony H. Giraldo

Institutions: Universidad de Antioquia, Institut Polytechnique de Paris, Laboratoire Traitement et Communication de l’Information

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Agence Nationale de la Recherche, Universidad de Antioquia

Awards: G3214861372, Deep Simplicial Neural Networks for Advanced Geometry Processing