ABC: Attention with Bilinear Correlation for Infrared Small Target Detection
This addresses infrared small target detection for applications like early warning and rescue, representing an incremental improvement in a domain-specific task.
The paper tackles the problem of infrared small target detection, where existing CNN and transformer methods struggle due to lack of clear features and induction bias, by proposing the ABC model with novel modules, achieving state-of-the-art performance on public datasets.
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