Improving Taxonomic Image-based Out-of-distribution Detection With DNA Barcodes
This addresses the challenge of reliable biodiversity monitoring by enabling better detection of unseen species, though it is incremental as it builds on existing OOD methods.
The paper tackled the problem of improving out-of-distribution (OOD) detection in image-based species identification by leveraging DNA barcodes, showing that their re-ordering approach enhances taxonomic OOD detection compared to common baselines.
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.