Language-Driven Motion Prior Knowledge Learning for Moving Infrared Small Target Detection

Different from traditional object detection, pure vision is often not enough to infrared small target detection (ISTD), due to the small target size and weak background contrast. For promoting performance, more target representations are often needed. Currently, motion representations have been proved to be one of the most potential feature patterns for infrared small targets. Besides vision features, existing methods have an obvious weakness that they could only capture coarse motion representations from the temporal domain. By vision features, fine motion representations could often be more effective to enhance detection performance. To overcome this weakness, and inspired by prevalent vision-language models (VLMs), the first vision-language framework with motion prior knowledge learning (MoPKL) was proposed in our previous work. To further extend it, we repropose an improved version, i.e., iMoPKL. Breaking through traditional pure-vision modality, it utilizes the homogeneous language descriptions, specially formatted for moving targets, to directionally guide vision channels to learn the motion prior knowledge of targets. In detail, it learns the distribution of target motion reconstruction corresponding to the language description as a type of prior knowledge. With the facilitation of language-driven motion alignment, the motion of infrared small targets could be further refined by motion-relation learning, to generate more fine motion representations. The extensive experiments on ITSDT-15K, DAUB-R, and IRDST-H show that our improvement version is effective. It could often obviously outperform the other methods, including our original MoPKL. Our source codes are available at https://github.com/UESTC-nnLab/MoPKL