Learning Global Dynamic Query for Large-Motion Infrared Small Target Detection

Motion Infrared Small Target Detection (MIRSTD) leverages multi-frame temporal dependencies to improve detection robustness. However, existing methods have difficulty modeling global consistency and achieving precise alignment in complex motion and large displacement scenarios, leading to dispersed target representations and higher error rates. To address these challenges, we propose Dynamic Query Aligner (DQAligner), which introduces global random large-displacement augmentation and a cross-scale bidirectional shared attention mechanism to enhance inter-frame consistency. A dynamic receptive field pyramid deformable convolution decomposes complex multi-scale motions, enabling precise target alignment. Furthermore, class query memory serves as the generalized residual form of deformable convolution, which iteratively learns dynamic query representations to facilitate global target localization within each frame and maintain semantic consistency across frames. DQAligner achieves a paradigm shift from rigid alignment to flexible matching, and significantly boosts detection performance in large displacement and dynamic scenarios. Experiments on extensive stationary and moving platform datasets show that DQAligner outperforms existing methods, especially under complex motion and low signal-noise-rate conditions. Code will be available at https://github.com/dengfa02/DQAligner_MIRSTD.