arXiv Computer Vision October 12, 2026 By Yuzhuo Li, Di Zhao, Tingrui Qiao, Yihao Wu, Bo Pang, Yun Sing Koh

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

Read the original on arXiv Computer Vision →

The paper introduces MetaWild, a multimodal animal re-identification dataset that pairs 20,890 images from six species with environmental metadata such as temperature and circadian rhythms. It also proposes the Meta-Feature Adapter (MFA), a lightweight module that integrates this metadata into vision-language models for re-identification tasks. Experiments demonstrate that adding MFA consistently improves re-identification performance over visual-only baselines.

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