  For object detection, how to address the contradictory requirement between
feature map resolution and receptive field on high-resolution inputs still
remains an open question. In this paper, to tackle this issue, we build a novel
architecture, called Attention-guided Context Feature Pyramid Network (AC-FPN),
that exploits discriminative information from various large receptive fields
via integrating attention-guided multi-path features. The model contains two
modules. The first one is Context Extraction Module (CEM) that explores large
contextual information from multiple receptive fields. As redundant contextual
relations may mislead localization and recognition, we also design the second
module named Attention-guided Module (AM), which can adaptively capture the
salient dependencies over objects by using the attention mechanism. AM consists
of two sub-modules, i.e., Context Attention Module (CxAM) and Content Attention
Module (CnAM), which focus on capturing discriminative semantics and locating
precise positions, respectively. Most importantly, our AC-FPN can be readily
plugged into existing FPN-based models. Extensive experiments on object
detection and instance segmentation show that existing models with our proposed
CEM and AM significantly surpass their counterparts without them, and our model
successfully obtains state-of-the-art results. We have released the source code
at https://github.com/Caojunxu/AC-FPN.
