The field of object detection has made great
progress in recent years. Most of these improvements are derived
from using a more sophisticated convolutional neural network.
However, in the case of humans, the attention mechanism,
global structure information, and local details of objects all
play an important role for detecting an object. In this paper,
we propose a novel fully convolutional network, named as Attention CoupleNet, to incorporate the attention-related information
and global and local information of objects to improve the
detection performance. Specifically, we first design a cascade
attention structure to perceive the global scene of the image and
generate class-agnostic attention maps. Then the attention maps
are encoded into the network to acquire object-aware features.
Next, we propose a unique fully convolutional coupling structure
to couple global structure and local parts of the object to
further formulate a discriminative feature representation. To fully
explore the global and local properties, we also design different
coupling strategies and normalization ways to make full use
of the complementary advantages between the global and local
information. Extensive experiments demonstrate the effectiveness
of our approach. We achieve state-of-the-art results on all three
challenging data sets, i.e., a mAP of 85.7% on VOC07, 84.3%
on VOC12, and 35.4% on COCO. Codes are publicly available
at https://github.com/tshizys/CoupleNet.