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 highfrequency component of input image. However, useful image details like contour and texture are also lost in such a process. In this paper, we propose D ual-branch i nteractive C ross-frequency a ttention N etwork (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 crossfrequency 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.