  Multi-label image recognition is a practical and challenging task compared to
single-label image classification. However, previous works may be suboptimal
because of a great number of object proposals or complex attentional region
generation modules. In this paper, we propose a simple but efficient two-stream
framework to recognize multi-category objects from global image to local
regions, similar to how human beings perceive objects. To bridge the gap
between global and local streams, we propose a multi-class attentional region
module which aims to make the number of attentional regions as small as
possible and keep the diversity of these regions as high as possible. Our
method can efficiently and effectively recognize multi-class objects with an
affordable computation cost and a parameter-free region localization module.
Over three benchmarks on multi-label image classification, we create new
state-of-the-art results with a single model only using image semantics without
label dependency. In addition, the effectiveness of the proposed method is
extensively demonstrated under different factors such as global pooling
strategy, input size and network architecture. Code has been made available
at~\url{https://github.com/gaobb/MCAR}.
