Gradient is All You Need: Gradient-Based Attention Fusion for Infrared Small Target Detection
<|reference_start|>Gradient is All You Need: Gradient-Based Attention Fusion for Infrared Small Target Detection: Infrared small target detection (IRSTD) is widely used in civilian and military applications. However, IRSTD encounters several challenges, including the tendency for small and dim targets to be obscured by complex backgrounds. To address this issue, we propose the Gradient Network (GaNet), which aims to extract and preserve edge and gradient information of small targets. GaNet employs the Gradient Transformer (GradFormer) module, simulating central difference convolutions (CDC) to extract and integrate gradient features with deeper features. Furthermore, we propose a global feature extraction model (GFEM) that offers a comprehensive perspective to prevent the network from focusing solely on details while neglecting the background information. We compare the network with state-of-the-art (SOTA) approaches, and the results demonstrate that our method performs effectively. Our source code is available at https://github.com/greekinRoma/Gradient-Transformer.<|reference_end|>
@article{hu2024gradient,
    title={Gradient is All You Need: Gradient-Based Attention Fusion for Infrared
  Small Target Detection},
    author={Chen Hu, Yian Huang, Kexuan Li, Luping Zhang, Yiming Zhu, Yufei Peng,
  Tian Pu, and Zhenming Peng},
    journal={arXiv preprint arXiv:2409.19599},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19599},
    primaryClass={cs.CV}
}