CATALOG: A Camera Trap Language-guided Contrastive Learning Model
It addresses the problem of recognizing animal species in camera-trap images for ecological monitoring, but it is incremental as it builds on existing foundation models and contrastive learning techniques.
The paper tackles domain shift in camera-trap image recognition by proposing CATALOG, a model that combines foundation models with multi-modal fusion and contrastive learning, and shows it outperforms previous state-of-the-art methods on benchmark datasets, especially with different species or geographical areas.
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.