Aggregating diverse deep attention networks for large-scale plant species identification

Abstract

In this paper, a novel fusion method is proposed to deal with large-scale plant species identification by aggregating diverse outputs from multiple deep networks, where each deep network focus on one subset of the whole plant species. Firstly, a fixed plant taxonomy is constructed for organizing large number of fine-grained plant species hierarchically and it is further used as a guideline to help generating diverse but overlapped task groups. Secondly, an attention-based deep hierarchical multi-task learning (AHMTL) algorithm is proposed to recognize fine-grained plant species belonging to the same task group effectively by learning more discriminative deep features and classifiers jointly. Finally, we fuse all outputs from multiple deep networks to obtain the final high-level feature representation and give the prediction probability for each plant species. The experimental results have proved the effectiveness of our proposed method on large-scale plant species identification.