论文阅读笔记：Learning Deep Features for Discriminative Localization

Introduction

Task

基于弱监督学习的图像分类和定位(检测)
相关工作:

• 弱监督目标定位
• 可视化CNN

Method

Class Activation Mapping(CAM)

CAM技术详细且简洁地展示了如何用CNN进行目标定位(检测)以及可视化，原理很简单，主要基于global average pooling(GAP)

• Firstly, get the last convolutional layer feature maps $f_k(x,y)$，is the $kth$ channel feature map, channel num is $n$

• Sencondly，use global average pooling to get $F_k$
  $$F_k=\sum _{x,y}{f}_k(x,y)$$

• Thirdly，use a FC layer，get class score $S_c$，it can be used to compute softmax cross entropy loss and then to train
  $$S_c=\sum _k{w}_k^c\ast F_k$$

• Finally，we can get class activation map by the weights $w_k^c$ for every class $c$，the resolution of $M_c(x,y)$ and $f_k(x,y)$ is same, and we can upsample it to get final map(size is same with oral image)
  $$M_c(x,y)=\sum _k{w}_k^c\ast f_k(x,y)$$

Experiments

classification result

Compared with original network(VGG、GoogleLeNet et al)，use GAP there is a small drop of 1%-2%.

Localization

Compared with fully-supervised methods，use CAM there is a large difference，at last this method not use bounding box.

Conclusion

• It is important that we can use classification-trained CNNs to learn to localization，without using any bounding box.
• The class activation mapping method is easy to transfer to other task for example captioning、VAQ et al.