  Fine-grained object classification is a challenging task due to the subtle
inter-class difference and large intra-class variation. Recently, visual
attention models have been applied to automatically localize the discriminative
regions of an image for better capturing critical difference and demonstrated
promising performance. However, without consideration of the diversity in
attention process, most of existing attention models perform poorly in
classifying fine-grained objects. In this paper, we propose a diversified
visual attention network (DVAN) to address the problems of fine-grained object
classification, which substan- tially relieves the dependency on
strongly-supervised information for learning to localize discriminative regions
compared with attentionless models. More importantly, DVAN explicitly pursues
the diversity of attention and is able to gather discriminative information to
the maximal extent. Multiple attention canvases are generated to extract
convolutional features for attention. An LSTM recurrent unit is employed to
learn the attentiveness and discrimination of attention canvases. The proposed
DVAN has the ability to attend the object from coarse to fine granularity, and
a dynamic internal representation for classification is built up by
incrementally combining the information from different locations and scales of
the image. Extensive experiments con- ducted on CUB-2011, Stanford Dogs and
Stanford Cars datasets have demonstrated that the proposed diversified visual
attention networks achieve competitive performance compared to the state-
of-the-art approaches, without using any prior knowledge, user interaction or
external resource in training or testing.
