Abstract
Weakly supervised object detection aims at learning precise object detectors, given image category labels. In recent prevailing works, this problem is generally formulated as a multiple instance learning module guided by an image classification loss. The object bounding box is assumed to be the one contributing most to the classification among all proposals. However, the region contributing most is also likely to be a crucial part or the supporting context of an object. To obtain a more accurate detector, in this work we propose a novel end-to-end weakly supervised detection approach, where a newly introduced generative adversarial segmentation module interacts with the conventional detection module in a collaborative loop. The collaboration mechanism takes full advantages of the complementary interpretations of the weakly supervised localization task, namely detection and segmentation tasks, forming a more comprehensive solution. Consequently, our method obtains more precise object bounding boxes, rather than parts or irrelevant surroundings. Expectedly, the proposed method achieves an accuracy of 51.0% on the PASCAL VOC 2007 dataset, outperforming the state-of-the-arts and demonstrating its superiority for weakly supervised object detection.
Abstract (translated)
弱监督目标检测的目的是学习精确的目标探测器，给定的图像类别标签。在最近的研究中，这个问题通常被描述为一个由图像分类损失引导的多实例学习模块。假设对象边界框是对所有建议中的分类贡献最大的框。然而，贡献最大的区域也可能是一个重要的部分或一个对象的支持背景。为了获得更精确的检测方法，本文提出了一种新的端到端弱监督检测方法，即新引入的生成对抗分割模块与传统检测模块在协同循环中相互作用。协作机制充分利用弱监督定位任务的互补解释，即检测和分割任务，形成更全面的解决方案。因此，我们的方法得到更精确的对象边界框，而不是零件或无关的环境。预计该方法在PascalVOC 2007数据集上的检测精度达到51.0%，优于现有技术水平，证明了其在弱监控目标检测中的优越性。
URL
https://arxiv.org/abs/1904.00551
