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Camouflaged object detection (COD) and camouflaged instance segmentation(CIS) aim to recognize and segment objects that are blended into theirsurroundings, respectively. While several deep neural network models have beenproposed to tackle those tasks, augmentation methods for COD and CIS have notbeen thoroughly explored. Augmentation strategies can help improve theperformance of models by increasing the size and diversity of the training dataand exposing the model to a wider range of variations in the data. Besides, weaim to automatically learn transformations that help to reveal the underlyingstructure of camouflaged objects and allow the model to learn to betteridentify and segment camouflaged objects. To achieve this, we propose alearnable augmentation method in the frequency domain for COD and CIS viaFourier transform approach, dubbed CamoFourier. Our method leverages aconditional generative adversarial network and cross-attention mechanism togenerate a reference image and an adaptive hybrid swapping with parameters tomix the low-frequency component of the reference image and the high-frequencycomponent of the input image. This approach aims to make camouflaged objectsmore visible for detection and segmentation models. Without bells and whistles,our proposed augmentation method boosts the performance of camouflaged objectdetectors and camouflaged instance segmenters by large margins.

Unveiling Camouflage: A Learnable Fourier-based Augmentation for Camouflaged Object Detection and Instance Segmentation