Camouflaged Instance Segmentation In-the-Wild: Dataset, Method, and Benchmark Suite
Camouflaged Instance Segmentation In-the-Wild: Dataset, Method, and Benchmark Suite
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DOI：
10.1109/tip.2021.3130490
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发表时间：
2021-03
影响因子：
10.6
通讯作者：
Trung-Nghia Le;Yubo Cao;Tan-Cong Nguyen;Minh-Quan Le;Khanh-Duy Nguyen;Thanh-Toan Do;M. Tran;
中科院分区：
文献类型：
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作者：
Trung-Nghia Le;Yubo Cao;Tan-Cong Nguyen;Minh-Quan Le;Khanh-Duy Nguyen;Thanh-Toan Do;M. Tran;
本文将图像中的隐藏区域分解为有意义的成分，即隐藏实例。为了促进在野外图像的图像分割的新任务，我们引入了一个数据集，称为CAMO++，它在数量和多样性方面扩展了我们的初步CAMO数据集（图像对象分割）。新的数据集大大增加了具有分层像素地面实况的图像数量。我们还提供了一个基准测试套件的任务，封装的实例分割。特别是，我们在各种场景下对我们新构建的CAMO++数据集上的最先进的实例分割方法进行了广泛的评估。我们还提出了一个伪装融合学习（CFL）框架，用于伪装实例分割，以进一步提高最先进方法的性能。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。
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.