Saliency detection by conditional generative adversarial network-猫眼课题宝

DOI：

10.1117/12.2306421

发表时间：

2018-04

期刊：

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其他

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作者：

Xiaoxu Cai;Hui Yu

图像中显著目标的检测一直是计算机视觉中的一个基本问题。近年来，深度学习在处理各种视觉任务方面表现出令人印象深刻的性能。在本文中，我们提出了一种新的方法来检测显着的目标，使用条件生成对抗网络（GAN）。这种类型的网络不仅学习从RGB图像到显著区域的映射，而且还学习用于训练映射的损失函数。据我们所知，这是第一次将条件GAN用于显著对象检测。我们评估我们的显着性检测方法在2个大型公开数据集与像素精确的注释。实验结果表明，在一个具有挑战性的数据集上，与最先进的方法相比，该方法具有显著和一致的改进，并且测试速度要快得多。

Detecting salient objects in images has been a fundamental problem in computer vision. In recent years, deep learning has shown its impressive performance in dealing with many kinds of vision tasks. In this paper, we propose a new method to detect salient objects by using Conditional Generative Adversarial Network (GAN). This type of network not only learns the mapping from RGB images to salient regions, but also learns a loss function for training the mapping. To the best of our knowledge, this is the first time that Conditional GAN has been used in salient object detection. We evaluate our saliency detection method on 2 large publicly available datasets with pixel accurate annotations. The experimental results have shown the significant and consistent improvements over the state-of-the-art method on a challenging dataset, and the testing speed is much faster.

Saliency detection by conditional generative adversarial network

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