Bio-Inspired Multi-Scale Contourlet Attention Networks

Inspired by the sparse and hierarchical features representation in the ventral stream of the human visual system, the biologically inspired multi-scale contourlet attention network (BMCAnet) is proposed to extract robust discriminative features. First, we constructed the multi-scale contourlet filter banks as a population of neurons in the primary visual cortex (V1), and extracted sparse features in a multi-scale and multi-direction way. It simulated a simple cell in V1 that responds to stimuli in a specific direction. Second, in order to refine contourlet features adaptively, the Shannon block attention module (SBAM) is introduced by integrating Shannon entropy as the third branch of the channel attention module (CAM), thus the weights of contourlet coefficients can be learned adaptively. Third, the responses of the spatial and spectral features are pooled by the proposed contourlet pooling layer to obtain the invariant structure features with the specified rules, which roughly stimulate the pooling process of complex cells in the V1 area. Last, the combination of global average pooling (GAP) and full connection (FC) is used for classification. The competitive results on eight databases demonstrate that the BMCAnet can effectively extract sparse and effective features for the classification tasks.