Advancing Wildlife Conservation through Multimodal Animal Re-Identification with Environmental Metadata

2026-09-28

AI Summary

Existing animal re-identification studies rely exclusively on visual information, overlooking environmental metadata that is highly correlated with animal behavior, and fail to fully leverage the textual processing capabilities of multimodal models. To address these limitations, this work introduces MetaWild, the first multimodal animal re-identification dataset incorporating environmental metadata. Furthermore, we propose a lightweight, plug-and-play Meta-Feature Adapter (MFA) module designed to efficiently integrate visual features with non-visual environmental information. Experimental results demonstrate that the MFA module significantly enhances the re-identification performance across multiple baseline models, thereby validating both the necessity and effectiveness of incorporating environmental metadata into animal re-identification frameworks.

Abstract

Identifying individual animals is crucial 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. Meanwhile, modern vision-language models (VLMs) offer rich multimodal reasoning capabilities, but existing resources underutilize their text-processing potential. To address these limitations, we propose MetaWild, a multimodal Animal ReID dataset comprising 20,890 images across six species, paired with environmental metadata extracted from embedded camera trap overlays and scene contexts. 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 VLM-based 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.

Problem

• Animal Re-Identification
• Environmental Metadata
• Vision-Language Models
• Wildlife Conservation

Innovation

• Animal Re-Identification
• Multimodal Learning
• Environmental Metadata
• Vision-Language Models
• Meta-Feature Adapter