Weakly supervised learning with only coarse labels can obtain visual explanations of deep neural network such as attention maps by back-propagating gradients. These attention maps are then available as priors for tasks such as object localization and semantic segmentation. In one common framework we address three shortcomings of previous approaches in modeling such attention maps: We (1) first time make attention maps an explicit and natural component of the end-to-end training, (2) provide self-guidance directly on these maps by exploring supervision form the network itself to improve them, and (3) seamlessly bridge the gap between using weak and extra supervision if available. Despite its simplicity, experiments on the semantic segmentation task demonstrate the effectiveness of our methods. We clearly surpass the state-of-the-art on Pascal VOC 2012 val. and test set. Besides, the proposed framework provides a way not only explaining the focus of the learner but also feeding back with direct guidance towards specific tasks. Under mild assumptions our method can also be understood as a plug-in to existing weakly supervised learners to improve their generalization performance.
翻译:微弱监督的学习,只有粗糙的标签,才能获得深神经网络的直观解释,例如以反射梯度绘制的注意图。这些注意图随后作为物体定位和语义分割等任务的前题提供。在一个共同框架内,我们解决了先前在制作注意图方面方法的三个缺点:我们(1) 首次将注意图作为端对端培训的明确和自然组成部分,(2) 通过探索监督网络本身来改进这些地图,直接提供自我指导,(3) 填补网络本身在使用薄弱和额外监督(如果有的话)之间的差距。尽管它很简单,但是关于语义分割的实验显示了我们的方法的有效性。我们显然超越了Pascal VOC 2012val. 和测试集的状态。此外,拟议框架不仅解释了学习者的重点,而且还为具体任务提供了直接指导。根据温和的假设,我们的方法也可以被理解为对现有的低监管学习者的一种插座,以提高其一般性能。