Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and interclass similarity. While prior work has addressed intra-class variation using localization and segmentation techniques, inter-class similarity may also affect feature learning and reduce classification performance. In this work, we address this problem using a novel optimization procedure for the end-to-end neural network training on FGVC tasks. Our procedure, called Pairwise Confusion (PC) reduces overfitting by intentionally introducing confusion in the activations. With PC regularization, we obtain state-ofthe-art performance on six of the most widely-used FGVC datasets and demonstrate improved localization ability. PC is easy to implement, does not need excessive hyperparameter tuning during training, and does not add significant overhead during test time.
REF ) constructs a Siamese neural network, trained with a loss function including pairwise confusion (PC).
Pairwise Confusion for Fine-Grained Visual Classification
ECCV
Computer Science