Camouflaged objects attempt to conceal their texture into the background and
discriminating them from the background is hard even for human beings. The main
objective of this paper is to explore the camouflaged object segmentation
problem, namely, segmenting the camouflaged object(s) for a given image. This
problem has not been well studied in spite of a wide range of potential
applications including the preservation of wild animals and the discovery of
new species, surveillance systems, search-and-rescue missions in the event of
natural disasters such as earthquakes, floods or hurricanes. This paper
addresses a new challenging problem of camouflaged object segmentation. To
address this problem, we provide a new image dataset of camouflaged objects for
benchmarking purposes. In addition, we propose a general end-to-end network,
called the Anabranch Network, that leverages both classification and
segmentation tasks. Different from existing networks for segmentation, our
proposed network possesses the second branch for classification to predict the
probability of containing camouflaged object(s) in an image, which is then
fused into the main branch for segmentation to boost up the segmentation
accuracy. Extensive experiments conducted on the newly built dataset
demonstrate the effectiveness of our network using various fully convolutional
networks. https://sites.google.com/view/ltnghia/research/camo

本文主要探索追踪伪装物体的图像分割问题，提供一个新的伪装物体数据集，提供了一种通用的端到端网络 ——Anabranch 网络，融合了分类和分割功能，采用多种全卷积网络评估其在新构建的数据集上的效果。