Weakly Supervised Fine-grained Recognition based on Combined Learning for Small Data and Coarse Label

Abstract

Learning with weak supervision already becomes one of the research trends in fine-grained image recognition. These methods aim to learn feature representation in the case of less manual cost or expert knowledge. Most existing weakly supervised methods are based on incomplete annotation or inexact annotation, which is difficult to perform well limited by supervision information. Therefore, using these two kind of annotations for training at the same time could mine more relevance while the annotating burden will not increase much. In this paper, we propose a combined learning framework by coarse-grained large data and fine-grained small data for weakly supervised fine-grained recognition. Combined learning contains two significant modules: 1) a discriminant module, which maintains the structure information consistent between coarse label and fine label by attention map and part sampling, 2) a cluster division strategy, which mines the detail differences between fine categories by feature subtraction. Experiment results show that our method outperforms weakly supervised methods and achieves the performance close to fully supervised methods in CUB-200-2011 and Stanford Cars datasets.