Fine-grained Recognition Datasets for Biodiversity Analysis
This work addresses the problem of species identification for biodiversity researchers by providing new datasets, but it is incremental as it applies existing methods to new data.
The paper introduced two new fine-grained visual classification datasets with up to 675 highly similar classes for biodiversity analysis and presented initial results using localized features with CNNs.
In the following paper, we present and discuss challenging applications for fine-grained visual classification (FGVC): biodiversity and species analysis. We not only give details about two challenging new datasets suitable for computer vision research with up to 675 highly similar classes, but also present first results with localized features using convolutional neural networks (CNN). We conclude with a list of challenging new research directions in the area of visual classification for biodiversity research.