Abstract End-to-end training of a deep CNN-Based model for salient object detection usually requires a huge number of training samples with pixel-level annotations, which are costly and time-consuming to obtain. In this paper, we propose an approach that can utilize large amounts of web data for learning a deep salient object detection model. With thousands of images collected from the Web, we first employ several bottom-up saliency detection techniques to generate salient object masks for all images, and then use a novel quality evaluation method to pick out a subset of images with reliable masks for training. After that, we develop a self-training approach to boost the performance of our initial network, which iterates between the network training process and the training set updating process. Importantly, different from existing webly-supervised or weakly-supervised methods, our approach is able to automatically select reliable images for network training without requiring any human intervention (e.g., dividing images into different difficulty levels). Results of extensive experiments on several widely-used benchmarks demonstrate that our method has achieved state-of-the-art performance. It significantly outperforms existing unsupervised and weakly-supervised salient object detection methods, and achieves competitive or even better performance than fully supervised approaches.

中文翻译：

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显着目标检测的网络监督学习

摘要 用于显着目标检测的基于 CNN 的深度模型的端到端训练通常需要大量具有像素级注释的训练样本，获取这些样本既昂贵又耗时。在本文中，我们提出了一种可以利用大量网络数据来学习深度显着对象检测模型的方法。通过从 Web 收集的数千张图像，我们首先采用几种自下而上的显着性检测技术为所有图像生成显着对象掩码，然后使用一种新颖的质量评估方法挑选出具有可靠掩码的图像子集进行训练。之后，我们开发了一种自训练方法来提高初始网络的性能，该方法在网络训练过程和训练集更新过程之间进行迭代。重要的，与现有的网络监督或弱监督方法不同，我们的方法能够自动选择可靠的图像进行网络训练，而无需任何人工干预（例如，将图像划分为不同的难度级别）。在几个广泛使用的基准测试上的大量实验结果表明，我们的方法已经达到了最先进的性能。它显着优于现有的无监督和弱监督的显着对象检测方法，并且比完全监督的方法具有竞争力甚至更好的性能。在几个广泛使用的基准测试上的大量实验结果表明，我们的方法已经达到了最先进的性能。它显着优于现有的无监督和弱监督的显着对象检测方法，并且比完全监督的方法具有竞争力甚至更好的性能。在几个广泛使用的基准测试上的大量实验结果表明，我们的方法已经达到了最先进的性能。它显着优于现有的无监督和弱监督的显着对象检测方法，并且比完全监督的方法具有竞争力甚至更好的性能。