Optimal Partition Assignment for Universal Object Detection

The label assignment problem is a core task in object detection, which mainly focuses on how to define the $positive/negative$ samples during the training phase. Recent works have proved that label assignment is significant for performance improvement of the detector. In this article, we propose an exquisite strategy that can dynamically assign labels according samples' joint scores (classification and location). Moreover, our strategy can apply to both 2D and 3D monocular detectors. In our strategy, we formulate label assignment as an optimization problem. Concretely, we first calculate the classification and location costs of each sample, which are treated as points in a 2-D coordinate system. Then an optimal divider line that minimizes the sum of point-to-line distances is designed to separate the $positive/negative$ samples. An iterative Genetic Algorithm is employed in acquiring the optimal solution. Furthermore, a GIoU auxiliary branch is devised to keep sample selection consistent during the training and testing phase. Benefitting from the non-maximum suppression (NMS) that utilizes the joint scores of classification and location, excellent detection performance is achieved. Extensive experiments conducted on MS COCO, PASCAL VOC (2D object detection), and KITTI (3D object detection) verify the effectiveness and universality of our proposed Optimal Partition Assignment (OPA).