# Fine-grained Recognition Datasets for Biodiversity Analysis

arXiv: https://arxiv.org/abs/1507.00913 · HTML: https://mlsanity.com/paper/1507.00913/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2015.

## Built on by (most cited first, 5 of 5)

- [Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification](https://mlsanity.com/paper/2307.15427/) (2023)
- [Geometrically Constrained and Token-Based Probabilistic Spatial Transformers](https://mlsanity.com/paper/2509.11218/) (2025)
- [Simplified Concrete Dropout -- Improving the Generation of Attribution Masks for Fine-grained Classification](https://mlsanity.com/paper/2307.14825/) (2023)
- [Saccadic Vision for Fine-Grained Visual Classification](https://mlsanity.com/paper/2509.15688/) (2025)
- [Automated Visual Monitoring of Nocturnal Insects with Light-based Camera Traps](https://mlsanity.com/paper/2307.15433/) (2023)

## Abstract (arXiv)

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.