Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention
This work addresses fine-grained visual classification for plant species identification, which is an incremental improvement in a domain-specific area.
The paper tackled plant species identification in the wild by using object detection of plant organs as a form of attention to address data variability and long-tail distribution issues, resulting in a new curated dataset for evaluation.
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.