论文标题
注意引导的上下文具有用于对象检测的金字塔网络
Attention-guided Context Feature Pyramid Network for Object Detection
论文作者
论文摘要
对于对象检测，如何在高分辨率输入上解决特征映射分辨率和接受场之间的矛盾要求仍然是一个悬而未决的问题。在本文中，为了解决这个问题，我们构建了一种新颖的体系结构，称为“注意引导上下文”具有金字塔网络（AC-FPN），该网络通过整合注意力指导的多路径特征来利用来自各种大型接受领域的歧视性信息。该模型包含两个模块。第一个是上下文提取模块（CEM），该模块探讨了来自多个接受场的大量上下文信息。由于冗余上下文关系可能会误导定位和识别，因此我们还设计了名为“注意引导”模块（AM）的第二个模块，该模块可以通过使用注意机制自适应地捕获对象的显着依赖性。 AM由两个子模块组成，即上下文注意模块（CXAM）和内容注意模块（CNAM），它们分别侧重于捕获歧视性语义和定位精确位置。最重要的是，我们的AC-FPN可以很容易地插入现有的基于FPN的模型中。关于对象检测和实例分割的广泛实验表明，我们提出的CEM的现有模型在没有它们的情况下会大大超过其对应物，并且我们的模型成功获得了最新的结果。我们已经在https://github.com/caojunxu/ac-fpn上发布了源代码。
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.
