CamoFA: A Learnable Fourier-Based Augmentation for Camouflage Segmentation

Minh-Quan Le, Minh-Triet Tran, Trung-Nghia Le, Tam V. Nguyen, Thanh-Toan Do; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 3427-3436

Camouflaged object detection (COD) and camouflaged instance segmentation (CIS) aim to recognize and segment objects that are blended into their surroundings respectively. While several deep neural network models have been proposed to tackle those tasks augmentation methods for COD and CIS have not been thoroughly explored. Augmentation strategies can help improve models' performance by increasing the size and diversity of the training data and exposing the model to a wider range of variations in the data. Besides we aim to automatically learn transformations that help to reveal the underlying structure of camouflaged objects and allow the model to learn to identify better and segment camouflaged objects. To achieve this we propose a learnable augmentation method in the frequency domain for COD and CIS via the Fourier transform approach dubbed CamoFA. Our method leverages a conditional generative adversarial network and cross-attention mechanism to generate a reference image and an adaptive hybrid swapping with parameters to mix the low-frequency component of the reference image and the high-frequency component of the input image. This approach aims to make camouflaged objects more visible for detection and segmentation models. Without bells and whistles our proposed augmentation method boosts the performance of camouflaged object detectors and instance segmenters by large margins.