Unveiling Camouflage: A Learnable Fourier-based Augmentation for
  Camouflaged Object Detection and Instance Segmentation
Minh-Quan Le Minh-Triet Tran Trung-Nghia Le Tam V. Nguyen Thanh-Toan Do
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 the
performance of models 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 better
identify and segment camouflaged objects. To achieve this, we propose a
learnable augmentation method in the frequency domain for COD and CIS via
Fourier transform approach, dubbed CamoFourier. 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 camouflaged instance segmenters by large margins.