Dual-branch interactive cross-frequency attention network for deep feature learning

As random noises contained in the high-frequency data could interfere with the feature learning of deep networks, low-pass filtering or wavelet transform have been integrated with deep networks to exclude the high-frequency component of input image. However, useful image details like contour and texture are also lost in such a process. In this paper, we propose Dual-branch interactive Cross-frequency attention Network (DiCaN) to separately process low-frequency and high-frequency components of input image, such that useful information is extracted from high-frequency data and included in deep learning. Our DiCaN first decomposes input image into low-frequency and high-frequency components using wavelet decomposition, and then applies two parallel residual-style branches to extract features from the two components. We further design an interactive cross-frequency attention mechanism, to highlight the useful information in the high-frequency data and interactively fuse them with the features in low-frequency branch. The features learned by our framework are then applied for both image classification and object detection and tested using ImageNet-1K and COCO datasets. The results suggest that DiCaN achieves better classification performance than different ResNet variants. Both one-stage and two-stage detectors with our DiCaN backbone also achieve better detection performance than that with ResNet backbone. The code of DiCaN will be released.