论文标题
针对弱监督语义细分和对象本地化的反对对抗操纵归因
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization
论文作者
论文摘要
从类标签中获得准确的像素级定位是弱监督语义分割和对象定位的关键过程。来自训练有素的分类器的归因图被广泛用于提供像素级定位，但是它们的重点往往仅限于目标对象的一个小歧视区域。 AdvCAM是图像的归因图，该图被操纵以增加在最终软性层或Sigmoid层之前由分类器产生的分类评分。这种操作以反对性方式实现，因此原始图像沿像素梯度在与对抗性攻击中使用的图像相反的方向上受到干扰。此过程增强了非歧视性但相关的相关特征，这对先前的归因图做出了不足的贡献，因此所得的AdvCAM确定了目标对象的更多区域。此外，我们引入了一种新的正则化程序，该程序抑制了与目标对象无关的区域的不正确归因，以及在目标对象的小区域上属于归因的过度浓度。我们的方法在Pascal VOC 2012和MS Coco 2014数据集中，在弱和半监督的语义细分中实现了新的最新性能。在弱监督的对象本地化中，它在CUB-2011和Imagenet-1K数据集上实现了新的最新性能。
Obtaining accurate pixel-level localization from class labels is a crucial process in weakly supervised semantic segmentation and object localization. Attribution maps from a trained classifier are widely used to provide pixel-level localization, but their focus tends to be restricted to a small discriminative region of the target object. An AdvCAM is an attribution map of an image that is manipulated to increase the classification score produced by a classifier before the final softmax or sigmoid layer. This manipulation is realized in an anti-adversarial manner, so that the original image is perturbed along pixel gradients in directions opposite to those used in an adversarial attack. This process enhances non-discriminative yet class-relevant features, which make an insufficient contribution to previous attribution maps, so that the resulting AdvCAM identifies more regions of the target object. In addition, we introduce a new regularization procedure that inhibits the incorrect attribution of regions unrelated to the target object and the excessive concentration of attributions on a small region of the target object. Our method achieves a new state-of-the-art performance in weakly and semi-supervised semantic segmentation, on both the PASCAL VOC 2012 and MS COCO 2014 datasets. In weakly supervised object localization, it achieves a new state-of-the-art performance on the CUB-200-2011 and ImageNet-1K datasets.
