Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs
This addresses a practical limitation in biodiversity monitoring for ecologists and conservationists, though it is incremental as it builds on existing multimodal and knowledge graph methods.
The paper tackles poor generalization in camera trap species classification by incorporating structured multimodal context, achieving competitive performance on out-of-distribution datasets like iWildCam2020-WILDS and Snapshot Mountain Zebra, and improving sample efficiency for underrepresented species.
Camera traps are important tools in animal ecology for biodiversity monitoring and conservation. However, their practical application is limited by issues such as poor generalization to new and unseen locations. Images are typically associated with diverse forms of context, which may exist in different modalities. In this work, we exploit the structured context linked to camera trap images to boost out-of-distribution generalization for species classification tasks in camera traps. For instance, a picture of a wild animal could be linked to details about the time and place it was captured, as well as structured biological knowledge about the animal species. While often overlooked by existing studies, incorporating such context offers several potential benefits for better image understanding, such as addressing data scarcity and enhancing generalization. However, effectively incorporating such heterogeneous context into the visual domain is a challenging problem. To address this, we propose a novel framework that transforms species classification as link prediction in a multimodal knowledge graph (KG). This framework enables the seamless integration of diverse multimodal contexts for visual recognition. We apply this framework for out-of-distribution species classification on the iWildCam2020-WILDS and Snapshot Mountain Zebra datasets and achieve competitive performance with state-of-the-art approaches. Furthermore, our framework enhances sample efficiency for recognizing under-represented species.