MICPL: Motion-Inspired Cross-Pattern Learning for Small-Object Detection in Satellite Videos
Shengjia Chen, Luping Ji, Sicheng Zhu, Mao Ye
Abstract
For small-object detection, vision patterns can only provide limited support to feature learning. Most prior schemes mainly depend on a single vision pattern to learn object features, seldom considering more latent motion patterns. In the real world, humans often efficiently perceive small objects through multipattern signals. Inspired by this observation, this article attempts to address small-object detection from a new prospective of latent pattern learning. To fulfill this purpose, it regards a real-world moving object as the spatiotemporal sequences of a static object to capture latent motion patterns. In view of this, we propose a motion-inspired cross-pattern learning (MICPL) scheme to capture the motion patterns for moving small-object scenarios. This scheme mainly consists of two crucial parts: motion pattern mining (MPM) and motion-vision adaption. The former is designed to effectively mine the motion pattern from time-dependent representation space. The latter is devised to correlate between motion patterns and vision semantics. In the meanwhile, we explore their cross-pattern interactions to guide MICPL to capture motion patterns effectively. Comparison experiments verify that, cooperated by motion pattern, even a simple detector could often refresh state-of-the-art (SOTA) results on moving small-object detection. Moreover, the experiments on two small-object-related tasks further prove the adaptivity and advantages of our cross-pattern feature learning scheme. Our source codes are available at https://github.com/UESTC-nnLab/MICPL.