Adaptive High-Frequency Transformer for Diverse Wildlife Re-Identification

TL;DR

This paper introduces an Adaptive High-Frequency Transformer that enhances wildlife re-identification across multiple species by focusing on high-frequency features and employing an adaptive selection strategy, achieving superior and robust results.

Contribution

The paper presents a unified multi-species wildlife ReID framework using a novel high-frequency transformer and an adaptive selection strategy for better feature learning and generalization.

Findings

Outperforms state-of-the-art ReID methods on multiple wildlife datasets.

Demonstrates strong generalization to unseen species.

Effectively captures detailed features like fur textures.

Abstract

Wildlife ReID involves utilizing visual technology to identify specific individuals of wild animals in different scenarios, holding significant importance for wildlife conservation, ecological research, and environmental monitoring. Existing wildlife ReID methods are predominantly tailored to specific species, exhibiting limited applicability. Although some approaches leverage extensively studied person ReID techniques, they struggle to address the unique challenges posed by wildlife. Therefore, in this paper, we present a unified, multi-species general framework for wildlife ReID. Given that high-frequency information is a consistent representation of unique features in various species, significantly aiding in identifying contours and details such as fur textures, we propose the Adaptive High-Frequency Transformer model with the goal of enhancing high-frequency information learning. To mitigate the inevitable high-frequency interference in the wilderness environment, we introduce an object-aware high-frequency selection strategy to adaptively capture more valuable high-frequency components. Notably, we unify the experimental settings of multiple wildlife datasets for ReID, achieving superior performance over state-of-the-art ReID methods. In domain generalization scenarios, our approach demonstrates robust generalization to unknown species.