Multi-branch Channel-wise Enhancement Network for Fine-grained Visual Recognition

The challenge in fine-grained visual classification (FGVC) is that the similarity within intra-class may be larger than inter-class, where the discriminative details require more attention than traditional classification tasks. To generate channel-wise complementary and discriminative features in beneficial details of FGVC, we propose a multi-branch channel-wise enhancement network (MCEN), which includes multi-pattern spatial disruption mechanism, inter-channel complementarity module(ICM), and novel soft target loss. The raw images are scrambled in multi-pattern and then the sub-images with different degrees of confusion are combined into three pairs as inputs, where the scrambled operation can force the channel to look for the discriminative details. And ICM can measure the complementarity between key features and overall features to restrain the redundancy of features. The soft target loss is designed for classification and the semantic relationship between the blocks is learned to judge the degree of the chaos of the image. Our designed multi-branched structure utilizes the shallow visual and deep semantic features to judge the outcome jointly, where the image pairs obtained by segmentation and rearrangement are input into the different branches to extract more complementary features from different patterns of the raw image. Our method is trained end-to-end with only class labels. Experimental results show that our model outperforms the state-of-the-art performance on three fine-grained benchmarks.