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arXiv 2106.021412021-06-03cs.CV

Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention

West Virginia University（西弗吉尼亚大学）

Matthew R. Keaton, Ram J. Zaveri, Meghana Kovur, Cole Henderson, Donald A. Adjeroh, Gianfranco Doretto

备注：: 6 pages, 4 figures. Accepted to the CVPR 2021 FGVC Workshop. Models, testing code, and link to dataset can be found at https://github.com/wvuvl/DARMA

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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.

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