Asymmetric Contextual Modulation for Infrared Small Target Detection

By Yimian Dai, Yiquan Wu, Fei Zhou, Kobus Barnard. IEEE Workshop/Winter Conference on Applications of Computer Vision, 2020

723 citations recorded by the source corpus.

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 1.

Subfields: Aerospace Engineering, Mechanics of Materials, Media Technology

View Paper: https://www.semanticscholar.org/paper/91c21dc7fd0e3b45c0bb1d83a946931e4eab1d51

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Zhenming Peng
2. Siying Cao
3. Tianfang Zhang
4. Lei Li
5. Tian Pu
6. Xin Wu
7. Jocelyn Chanussot
8. Yulan Guo

In Mechanics of Materials

1. Zhenming Peng
2. Lei Li

In Media Technology

1. Danfeng Hong
2. Jocelyn Chanussot
3. Xin Wu
4. Yulan Guo
5. Longguang Wang
6. Zhenming Peng
7. Wei An
8. Yingqian Wang

Citations in the corpus

• Dense Nested Attention Network for Infrared Small Target Detection (2021, methodology): “[24] built the first real SIRST dataset with high-quality images and labels.”
• Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background (2023, methodology): “In data-driven approaches, we use feature pyramid network (FPN) [38], ACM [19], ALCNet [21], local similarity pyramid module (LSPM) [39] and dense nested attention network (DNANet) [40] as comparison methods.”
• UIU-Net: U-Net in U-Net for Infrared Small Object Detection (2022, methodology): “[30], [31] built a bottom-up attention mechanism module and embedded it into U-Net or FPN network structure.”