  Infrared small target detection (ISTD) has a wide range of applications in
early warning, rescue, and guidance. However, CNN based deep learning methods
are not effective at segmenting infrared small target (IRST) that it lack of
clear contour and texture features, and transformer based methods also struggle
to achieve significant results due to the absence of convolution induction
bias. To address these issues, we propose a new model called attention with
bilinear correlation (ABC), which is based on the transformer architecture and
includes a convolution linear fusion transformer (CLFT) module with a novel
attention mechanism for feature extraction and fusion, which effectively
enhances target features and suppresses noise. Additionally, our model includes
a u-shaped convolution-dilated convolution (UCDC) module located deeper layers
of the network, which takes advantage of the smaller resolution of deeper
features to obtain finer semantic information. Experimental results on public
datasets demonstrate that our approach achieves state-of-the-art performance.
Code is available at https://github.com/PANPEIWEN/ABC
