  Fine-grained recognition is a challenging task due to the small
intra-category variances. Most of top-performing fine-grained recognition
methods leverage parts of objects for better performance. Therefore, part
annotations which are extremely computationally expensive are required. In this
paper, we propose a novel cascaded deep CNN detection framework for
fine-grained recognition which is trained to detect the whole object without
considering parts. Nevertheless, most of current top-performing detection
networks use the N+1 class (N object categories plus background) softmax loss,
and the background category with much more training samples dominates the
feature learning progress so that the features are not good for object
categories with fewer samples. To bridge this gap, we introduce a cascaded
structure to eliminate background and exploit a one-vs-rest loss to capture
more minute variances among different subordinate categories. Experiments show
that our proposed recognition framework achieves comparable performance with
state-of-the-art, part-free, fine-grained recognition methods on the
CUB-200-2011 Bird dataset. Moreover, our method even outperforms most of
part-based methods while does not need part annotations at the training stage
and is free from any annotations at test stage.
