OAPR: An Offset-Aware Progressive Regression Object Detector

Object detection generally involves two main components: classification and regression. Despite the impressive performance achieved by recent refinement localization works, there is still room for improvement due to the limitations of current multistep regression strategies and task misalignment. To overcome these challenges, we propose a novel offset-aware progressive regression detector (OAPR) comprising an offset-aware head and a progressive regression predictor. Initially, we develop a head network incorporating our innovative plug-and-play offset-aware module. By utilizing the offset from one task to guide feature learning in another task, we intuitively achieve task alignment to address feature misalignment. We subsequently employ a progressive regression predictor to locate objects. In first-step regression, the aim is to identify a region within the object rather than the object itself. This is followed by second-step regression to locate the object precisely. Extensive experiments conducted on MS COCO datasets demonstrate the superior performance of our OAPR compared with recent state-of-the-art detectors with various backbones, including ATSS (~3.0 AP), GFL (~2.0 AP), BorderDet (~2.0 AP), and VFNet (~1.0 AP). Our code will be released.