An Individual Identity-Driven Framework for Animal Re-Identification

TL;DR

This paper introduces IndivAID, a novel two-stage framework leveraging CLIP's cross-modal capabilities to improve animal re-identification by generating and utilizing individual-specific textual descriptions, enhancing accuracy across diverse datasets.

Contribution

The paper presents a new two-stage animal re-identification framework that combines text description generation with visual feature refinement using CLIP, addressing limitations of existing methods.

Findings

Outperforms state-of-the-art methods on eight benchmark datasets.

Demonstrates robustness and applicability on a real-world Stoat dataset.

Effectively captures diverse visual concepts of individual animals.

Abstract

Reliable re-identification of individuals within large wildlife populations is crucial for biological studies, ecological research, and wildlife conservation. Classic computer vision techniques offer a promising direction for Animal Re-identification (Animal ReID), but their backbones' close-set nature limits their applicability and generalizability. Despite the demonstrated effectiveness of vision-language models like CLIP in re-identifying persons and vehicles, their application to Animal ReID remains limited due to unique challenges, such as the various visual representations of animals, including variations in poses and forms. To address these limitations, we leverage CLIP's cross-modal capabilities to introduce a two-stage framework, the \textbf{Indiv}idual \textbf{A}nimal \textbf{ID}entity-Driven (IndivAID) framework, specifically designed for Animal ReID. In the first stage, IndivAID trains a text description generator by extracting individual semantic information from each image, generating both image-specific and individual-specific textual descriptions that fully capture the diverse visual concepts of each individual across animal images. In the second stage, IndivAID refines its learning of visual concepts by dynamically incorporating individual-specific textual descriptions with an integrated attention module to further highlight discriminative features of individuals for Animal ReID. Evaluation against state-of-the-art methods across eight benchmark datasets and a real-world Stoat dataset demonstrates IndivAID's effectiveness and applicability. Code is available at \url{https://github.com/ywu840/IndivAID}.