Pose variation and subtle differences in appearance are key challenges to
fine-grained classification. While deep networks have markedly improved general
recognition, many approaches to fine-grained recognition rely on anchoring
networks to parts for better accuracy. Identifying parts to find correspondence
discounts pose variation so that features can be tuned to appearance. To this
end previous methods have examined how to find parts and extract
pose-normalized features. These methods have generally separated fine-grained
recognition into stages which first localize parts using hand-engineered and
coarsely-localized proposal features, and then separately learn deep
descriptors centered on inferred part positions. We unify these steps in an
end-to-end trainable network supervised by keypoint locations and class labels
that localizes parts by a fully convolutional network to focus the learning of
feature representations for the fine-grained classification task. Experiments
on the popular CUB200 dataset show that our method is state-of-the-art and
suggest a continuing role for strong supervision.

本文提出了一种全卷积网络的方法，通过部位定位和关键点标注来实现表观的转化，以有效解决姿态变化和外观上的微妙差异在微细分类中的挑战。在 CUB200 测试集中表现出最新的性能，为强监督发挥了重要作用。