MetaWild: A Multimodal Dataset for Animal Re-Identification with Environmental Metadata

TL;DR

MetaWild introduces a multimodal animal re-identification dataset incorporating environmental metadata, enabling models to leverage both visual and contextual data for improved accuracy in wildlife monitoring.

Contribution

The paper presents MetaWild, a novel dataset combining visual and environmental metadata, and proposes the Meta-Feature Adapter to enhance multimodal Animal ReID methods.

Findings

Incorporating environmental metadata improves ReID accuracy.

The Meta-Feature Adapter effectively leverages metadata in existing models.

Multimodal approaches outperform visual-only methods in Animal ReID.

Abstract

Identifying individual animals within large wildlife populations is essential for effective wildlife monitoring and conservation efforts. Recent advancements in computer vision have shown promise in animal re-identification (Animal ReID) by leveraging data from camera traps. However, existing Animal ReID datasets rely exclusively on visual data, overlooking environmental metadata that ecologists have identified as highly correlated with animal behavior and identity, such as temperature and circadian rhythms. Moreover, the emergence of multimodal models capable of jointly processing visual and textual data presents new opportunities for Animal ReID, but existing datasets fail to leverage these models' text-processing capabilities, limiting their full potential. Additionally, to facilitate the use of metadata in existing ReID methods, we propose the Meta-Feature Adapter (MFA), a lightweight module that can be incorporated into existing vision-language model (VLM)-based Animal ReID methods, allowing ReID models to leverage both environmental metadata and visual information to improve ReID performance. Experiments on MetaWild show that combining baseline ReID models with MFA to incorporate metadata consistently improves performance compared to using visual information alone, validating the effectiveness of incorporating metadata in re-identification. We hope that our proposed dataset can inspire further exploration of multimodal approaches for Animal ReID.