In this work, we first tackle the problem of simultaneous pixel-level
localization and image-level classification with only image-level labels for
fully convolutional network training. We investigate the global pooling method
which plays a vital role in this task. Classical global max pooling and average
pooling methods are hard to indicate the precise regions of objects. Therefore,
we revisit the global weighted average pooling (GWAP) method for this task and
propose the class-agnostic GWAP module and the class-specific GWAP module in
this paper. We evaluate the classification and pixel-level localization ability
on the ILSVRC benchmark dataset. Experimental results show that the proposed
GWAP module can better capture the regions of the foreground objects. We
further explore the knowledge transfer between the image classification task
and the region-based object detection task. We propose a multi-task framework
that combines our class-specific GWAP module with R-FCN. The framework is
trained with few ground truth bounding boxes and large-scale image-level
labels. We evaluate this framework on PASCAL VOC dataset. Experimental results
show that this framework can use the data with only image-level labels to
improve the generalization of the object detection model.

本文提出一种用于同时实现像素级别本地化和仅具有图像级别标签的图像级别分类的全卷积网络训练的全局加权平均池化方法并探讨了其在对象识别中的应用，提出了类特定和类不可知的 GWAP 模块构成的多任务框架，结果表明此框架可以使用仅具有图像级别标签的数据来改进对象检测模型的推广能力。

全局加权平均池化：连接像素级定位和图像级分类

Global Weighted Average Pooling Bridges Pixel-level Localization and  Image-level Classification

Weakly supervised learning of object detection is an important problem in
image understanding that still does not have a satisfactory solution. In this
paper, we address this problem by exploiting the power of deep convolutional
neural networks pre-trained on large-scale image-level classification tasks. We
propose a weakly supervised deep detection architecture that modifies one such
network to operate at the level of image regions, performing simultaneously
region selection and classification. Trained as an image classifier, the
architecture implicitly learns object detectors that are better than
alternative weakly supervised detection systems on the PASCAL VOC data. The
model, which is a simple and elegant end-to-end architecture, outperforms
standard data augmentation and fine-tuning techniques for the task of
image-level classification as well.

本文探讨探索深度卷积神经网络的力量，提出了一种弱监督的深度检测架构，该架构可以同时进行区域选择和分类，并使用图像分类器训练，从而比实现图像级分类任务的标准数据增强和微调技术更好地学习目标探测器。