计算机科学
调制(音乐)
稀缺
质量(理念)
语义学(计算机科学)
红外线的
领域(数学)
编码(集合论)
人工智能
哲学
物理
光学
经济
微观经济学
集合(抽象数据类型)
认识论
程序设计语言
纯数学
美学
数学
作者
Yawen Dai,Yiquan Wu,Fei Zhou,Kobus Barnard
出处
期刊:Cornell University - arXiv
日期:2020-09-30
被引量:1
标识
DOI:10.48550/arxiv.2009.14530
摘要
Single-frame infrared small target detection remains a challenge not only due to the scarcity of intrinsic target characteristics but also because of lacking a public dataset. In this paper, we first contribute an open dataset with high-quality annotations to advance the research in this field. We also propose an asymmetric contextual modulation module specially designed for detecting infrared small targets. To better highlight small targets, besides a top-down global contextual feedback, we supplement a bottom-up modulation pathway based on point-wise channel attention for exchanging high-level semantics and subtle low-level details. We report ablation studies and comparisons to state-of-the-art methods, where we find that our approach performs significantly better. Our dataset and code are available online.
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