DARMA Dataset

Summary

Dataset can be accessed from this repository.

Related publications include (Keaton et al., 2021).

References

1. CVPRW

   Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention Keaton, M. R., Zaveri, R. J., Kovur, M., Henderson, C., Adjeroh, D. A., and Doretto, G. In Proceedings of the IEEE CVPR Workshop on Fine-Grained Visual Categorization, 2021. abstract bibTeX arXiv pdf data

   Plant species identification in the wild is a difficult problem in part due to the high variability of the input data, but also because of complications induced by the long-tail effects of the datasets distribution. Inspired by the most recent fine-grained visual classification approaches which are based on attention to mitigate the effects of data variability, we explore the idea of using object detection as a form of attention. We introduce a bottom-up approach based on detecting plant organs and fusing the predictions of a variable number of organ-based species classifiers. We also curate a new dataset with a long-tail distribution for evaluating plant organ detection and organ-based species identification, which is publicly available.

   @inproceedings{keatonZKHAD21cvprw,
     abbr = {CVPRW},
     author = {Keaton, M. R. and Zaveri, R. J. and Kovur, M. and Henderson, C. and Adjeroh, D. A. and Doretto, G.},
     title = {{Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention}},
     booktitle = {Proceedings of the IEEE CVPR Workshop on Fine-Grained Visual Categorization},
     year = {2021},
     arxiv = {2106.02141},
     data = {https://github.com/wvuvl/DARMA},
     bib2html_pubtype = {Conferences}
   }