Second-order Attention Guided Convolutional Activations for Visual Recognition

• Published in: 2020 25th International Conference on Pattern Recognition (ICPR) pp. 3071 - 3076
• Main Authors: Chen, Shannan, Wang, Qian, Sun, Qiule, Liu, Bin, Zhang, Jianxin, Zhang, Qiang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 10.01.2021
• Subjects: channel attention; Computational modeling; deep convolutional networks; Image representation; Limiting; Network topology; Pattern recognition; second-order statistics; Tensors; visual recognition; Visualization
• DOI: 10.1109/ICPR48806.2021.9412350

Recently, modeling deep convolutional activations by the global second-order pooling has shown great advance on visual recognition tasks. However, most of the existing deep second-order statistical models mainly compute second-order statistics of activations of the last convolutional layer as image representations, and they seldom introduce second-order statistics into earlier layers to better fit network topology, thus limiting the representational ability to a certain extent. Motivated by the flexibility of attention blocks that are commonly plugged into intermediate layers of deep convolutional networks (ConvNets), this work makes an attempt to combine deep second-order statistics with attention mechanisms in ConvNets, and further proposes a novel Second-order Attention Guided Network (SoAG-Net) for visual recognition. More specifically, SoAG-Net involves several SoAG modules seemingly inserted into intermediate layers of the network, in which SoAG collects second-order statistics of convolutional activations by polynomial kernel approximation to predict channel-wise attention maps utilized for guiding the learning of convolutional activations through tensor scaling along channel dimension. SoAG improves the nonlinearity of ConvNets and enables ConvNets to fit more complicated distribution of convolutional activations. Experiment results on three commonly used datasets illuminate that SoAG-Net outperforms its counterparts and achieves competitive performance with state-of-the-art models under the same backbone.