  In our daily life, the scenes around us are always with multiple labels
especially in a smart city, i.e., recognizing the information of city operation
to response and control. Great efforts have been made by using Deep Neural
Networks to recognize multi-label images. Since multi-label image
classification is very complicated, people seek to use the attention mechanism
to guide the classification process. However, conventional attention-based
methods always analyzed images directly and aggressively. It is difficult for
them to well understand complicated scenes. In this paper, we propose a
global/local attention method that can recognize an image from coarse to fine
by mimicking how human-beings observe images. Specifically, our global/local
attention method first concentrates on the whole image, and then focuses on
local specific objects in the image. We also propose a joint max-margin
objective function, which enforces that the minimum score of positive labels
should be larger than the maximum score of negative labels horizontally and
vertically. This function can further improve our multi-label image
classification method. We evaluate the effectiveness of our method on two
popular multi-label image datasets (i.e., Pascal VOC and MS-COCO). Our
experimental results show that our method outperforms state-of-the-art methods.
