WildIng: A Wildlife Image Invariant Representation Model for Geographical Domain Shift

2026-01-02

arXiv.org

AI Summary

This study addresses the significant performance degradation of wildlife image recognition models when deployed across geographically distinct regions due to environmental variations. To mitigate this domain shift, the authors propose a novel approach that integrates species-level textual descriptions with visual features, introducing appearance-based semantic information into representation learning for the first time. By leveraging vision-language models such as BioCLIP, the method enables text-guided extraction of domain-invariant features, thereby enhancing semantic alignment between images and their corresponding textual descriptions. Evaluated in a cross-domain setting—trained on African wildlife data and tested on American datasets—the model achieves a 30% improvement in accuracy over baseline methods, substantially alleviating geographic domain bias and offering an effective solution for large-scale, cross-regional wildlife monitoring.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Wildlife monitoring is crucial for studying biodiversity loss and climate change. Camera trap images provide a non-intrusive method for analyzing animal populations and identifying ecological patterns over time. However, manual analysis is time-consuming and resource-intensive. Deep learning, particularly foundation models, has been applied to automate wildlife identification, achieving strong performance when tested on data from the same geographical locations as their training sets. Yet, despite their promise, these models struggle to generalize to new geographical areas, leading to significant performance drops. For example, training an advanced vision-language model, such as CLIP with an adapter, on an African dataset achieves an accuracy of 84.77%. However, this performance drops significantly to 16.17% when the model is tested on an American dataset. This limitation partly arises because existing models rely predominantly on image-based representations, making them sensitive to geographical data distribution shifts, such as variation in background, lighting, and environmental conditions. To address this, we introduce WildIng, a Wildlife image Invariant representation model for geographical domain shift. WildIng integrates text descriptions with image features, creating a more robust representation to geographical domain shifts. By leveraging textual descriptions, our approach captures consistent semantic information, such as detailed descriptions of the appearance of the species, improving generalization across different geographical locations. Experiments show that WildIng enhances the accuracy of foundation models such as BioCLIP by 30% under geographical domain shift conditions. We evaluate WildIng on two datasets collected from different regions, namely America and Africa. The code and models are publicly available at https://github.com/Julian075/CATALOG/tree/WildIng.

Problem

• geographical domain shift
• wildlife image recognition
• model generalization
• distribution shift
• camera trap images

Innovation

• domain generalization
• vision-language model
• wildlife monitoring
• geographical shift
• invariant representation