Improving Taxonomic Image-based Out-of-distribution Detection With DNA Barcodes

TL;DR

This paper explores how DNA barcodes can enhance image-based out-of-distribution detection for species identification, improving accuracy by leveraging genetic similarity to identify unseen classes.

Contribution

It introduces a re-ordering method that integrates DNA barcode information with existing OOD detection models, improving taxonomic outlier detection performance.

Findings

DNA barcode proximity correlates with visual similarity.

The proposed method outperforms common baselines in OOD detection.

Applicable to any pre-trained model and existing OOD methods.

Abstract

Image-based species identification could help scaling biodiversity monitoring to a global scale. Many challenges still need to be solved in order to implement these systems in real-world applications. A reliable image-based monitoring system must detect out-of-distribution (OOD) classes it has not been presented before. This is challenging especially with fine-grained classes. Emerging environmental monitoring techniques, DNA metabarcoding and eDNA, can help by providing information on OOD classes that are present in a sample. In this paper, we study if DNA barcodes can also support in finding the outlier images based on the outlier DNA sequence's similarity to the seen classes. We propose a re-ordering approach that can be easily applied on any pre-trained models and existing OOD detection methods. We experimentally show that the proposed approach improves taxonomic OOD detection compared to all common baselines. We also show that the method works thanks to a correlation between visual similarity and DNA barcode proximity. The code and data are available at https://github.com/mikkoim/dnaimg-ood.