Fine-grained visual classification is inherently challenging because of its inter-class similarity and intra-class variance. However, by contrasting the images with same/different labels, a human can instinctively notice that the key clues lie in certain objects while other objects are ignorable. Inspired by this, we propose Contrastively-reinforced Attention Convolutional Neural Network (CRA-CNN), which reinforces the attention awareness of deep activations. CRA-CNN mainly contains two parts:

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... he classification stream and attention regularization stream. The former classifies the input image and simultaneously proposes to divide the input visual information into attention and redundancy. The latter evaluates the attention/redundancy proposal by classifying the attention and contrasting the attention/redundancy of various inputs. The evaluation information is backpropagated and forces the classification stream to improve its awareness of visual attention, which helps classification. Experimental results on CUB-Birds and Stanford Cars show that CRA-CNN distinctly outperforms the baselines and is comparable with state-of-art studies despite its simplicity.