论文标题
弥合分类和本地化之间的差距，以进行弱监督的对象本地化
Bridging the Gap between Classification and Localization for Weakly Supervised Object Localization
论文作者
论文摘要
弱监督的对象本地化旨在在给定图像中找到一个目标对象区域，仅具有弱监督，例如图像级标签。大多数现有方法使用类激活图（CAM）生成本地化图；但是，凸轮仅标识目标对象的最歧视部分，而不是整个对象区域。在这项工作中，我们发现分类和本地化之间的差距是根据输入特征和特定于类特异性重量之间的方向的未对准的。我们证明，未对准会抑制CAM在不太歧视但属于目标对象的区域的激活。为了弥合差距，我们提出了一种与特定体重的特征说明对齐的方法。所提出的方法在CUB-200-2011和Imagenet-1K基准测试中实现了最先进的定位性能。
Weakly supervised object localization aims to find a target object region in a given image with only weak supervision, such as image-level labels. Most existing methods use a class activation map (CAM) to generate a localization map; however, a CAM identifies only the most discriminative parts of a target object rather than the entire object region. In this work, we find the gap between classification and localization in terms of the misalignment of the directions between an input feature and a class-specific weight. We demonstrate that the misalignment suppresses the activation of CAM in areas that are less discriminative but belong to the target object. To bridge the gap, we propose a method to align feature directions with a class-specific weight. The proposed method achieves a state-of-the-art localization performance on the CUB-200-2011 and ImageNet-1K benchmarks.
