Region-Aware Multimodal Interleaving for Animal Re-Identification

Animal Re-Identification (Animal ReID) is crucial for eco-logical monitoring in the wild, yet remains challenging due to the fine-grained and diverse visual features of animals, including intricate tex-tures, distinctive patterns, and various pose or form representations.While current Animal ReID methods mostly rely on holistic global fea-tures, they frequently overfit to the surrounding environment rather thancapturing localised, identity-relevant cues. Existing pattern-based meth-ods primarily focus on global generalised signals that are sensitive tobackground interventions and often fail to highlight identity evidencefor individual discrimination. Additionally, these methods are mainlyconstrained to the visual space only and lack pattern-based semanticinteractions from the multimodal space. To address these challenges,we introduce a Region-Aware Multimodal Interleaving (RAMI) frame-work, which formulates Animal ReID as a visual-textual semantic in-teraction over informative, localised regions. Specifically, RAMI projectssegmentation-derived region tokens into a shared token space and inter-leaves them with semantic tokens within a Transformer-based module tofacilitate dense cross-modal interactions. Given the lack of region-leveldata, we design a simple pipeline to produce biologically representativeregions from segmented animals. To the best of our knowledge, we are thefirst to formulate multimodal interleaving tailored for fine-grained dis-criminative tasks in Vision-Language Models (VLMs). Experiments showthat RAMI outperforms state-of-the-art methods across both benchmarkand in-the-wild datasets, with an average mAP gain of up to 18.7%. Ourcode is available at https://github.com/ML-4-SocialGood/RAMI.git.