通过环境元数据推进野生动物保护的多模态动物再识别
Advancing Wildlife Conservation through Multimodal Animal Re-Identification with Environmental Metadata
查看机构详情
- University of Auckland（奥克兰大学）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
本文提出MetaWild数据集和轻量级MFA模块，将环境元数据融入基于视觉语言模型的动物再识别，实验证明结合元数据可稳定提升识别性能，助力野生动物保护。
中文摘要 AI 辅助
识别个体动物对于有效的野生动物监测和保护工作至关重要。计算机视觉的最新进展通过利用相机陷阱数据在动物再识别（Animal ReID）方面显示出潜力。然而，现有的动物再识别数据集仅依赖视觉数据，忽视了生态学家认为与动物行为和身份高度相关的环境元数据，如温度和昼夜节律。同时，现代视觉-语言模型（VLM）提供了丰富的多模态推理能力，但现有资源未能充分利用其文本处理潜力。为解决这些限制，我们提出了MetaWild，一个多模态动物再识别数据集，包含六个物种的20,890张图像，并配以从嵌入式相机陷阱覆盖层和场景上下文中提取的环境元数据。此外，为了便于在现有再识别方法中使用元数据，我们提出了元特征适配器（MFA），这是一个轻量级模块，可集成到现有的基于VLM的再识别方法中，使再识别模型能够利用环境元数据和视觉信息来提高再识别性能。在MetaWild上的实验表明，与仅使用视觉信息相比，将基线再识别模型与MFA结合以纳入元数据持续提高了性能，验证了在再识别中纳入元数据的有效性。
英文摘要
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.
