  Infrared small target detection is an important problem in many fields such
as earth observation, military reconnaissance, disaster relief, and has
received widespread attention recently. This paper presents the
Attention-Guided Pyramid Context Network (AGPCNet) algorithm. Its main
components are an Attention-Guided Context Block (AGCB), a Context Pyramid
Module (CPM), and an Asymmetric Fusion Module (AFM). AGCB divides the feature
map into patches to compute local associations and uses Global Context
Attention (GCA) to compute global associations between semantics, CPM
integrates features from multi-scale AGCBs, and AFM integrates low-level and
deep-level semantics from a feature-fusion perspective to enhance the
utilization of features. The experimental results illustrate that AGPCNet has
achieved new state-of-the-art performance on two available infrared small
target datasets. The source codes are available at
https://github.com/Tianfang-Zhang/AGPCNet.
