Le Trung-Nghia, Cao Yubo, Nguyen Tan-Cong, Le Minh-Quan, Nguyen Khanh-Duy, Do Thanh-Toan, Tran Minh-Triet, Nguyen Tam V
IEEE Trans Image Process. 2022;31:287-300. doi: 10.1109/TIP.2021.3130490. Epub 2021 Dec 9.
This paper pushes the envelope on decomposing camouflaged regions in an image into meaningful components, namely, camouflaged instances. To promote the new task of camouflaged instance segmentation of in-the-wild images, we introduce a dataset, dubbed CAMO++, that extends our preliminary CAMO dataset (camouflaged object segmentation) in terms of quantity and diversity. The new dataset substantially increases the number of images with hierarchical pixel-wise ground truths. We also provide a benchmark suite for the task of camouflaged instance segmentation. In particular, we present an extensive evaluation of state-of-the-art instance segmentation methods on our newly constructed CAMO++ dataset in various scenarios. We also present a camouflage fusion learning (CFL) framework for camouflaged instance segmentation to further improve the performance of state-of-the-art methods. The dataset, model, evaluation suite, and benchmark will be made publicly available on our project page.
本文突破了将图像中伪装区域分解为有意义组件（即伪装实例）的极限。为推动野外图像伪装实例分割这一新任务，我们引入了一个名为CAMO++的数据集，该数据集在数量和多样性方面扩展了我们初步的CAMO数据集（伪装物体分割）。新数据集大幅增加了具有分层逐像素真值的图像数量。我们还为伪装实例分割任务提供了一个基准测试套件。特别是，我们在各种场景下对新构建的CAMO++数据集上的当前最先进实例分割方法进行了广泛评估。我们还提出了一种用于伪装实例分割的伪装融合学习（CFL）框架，以进一步提高当前最先进方法的性能。该数据集、模型、评估套件和基准将在我们的项目页面上公开提供。
