Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs

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 differ...

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Bibliographic Details
Published in:arXiv.org 2024-08
Main Authors: Pahuja, Vardaan, Luo, Weidi, Gu, Yu, Cheng-Hao, Tu, Hong-You, Chen, Berger-Wolf, Tanya, Stewart, Charles, Gao, Song, Wei-Lun, Chao, Su, Yu
Format: Article
Language:English
Subjects:
Cameras
Context
Ecological monitoring
Image enhancement
Knowledge representation
Species classification
Taxonomy
Wildlife conservation
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2401.00608