Object detection is commonly formulated as a multi-task learning problem in deep learning methods. Due to the di-vergence between classification and regression tasks, modern one-stage detectors typically utilize two parallel branches as the detection head, which might be sub-optimal. In this paper, we propose a new Gating Head (G-Head) to enhance the in-teraction between different tasks and promote the multi-task learning process. By introducing Multi-Scale Aggregation (MSA), Multi-Aspect Learning (MAL), and Gating Selec-tor (GS), our method can significantly boost the performance of existing one-stage frameworks with fewer parameters and computational costs. To validate the efficiency, effectiveness, and generalization of our G- Head, extensive experiments are conducted on the challenging MS COCO dataset. Without bells and whistles, we achieve a new state-of-the-art 48.7 AP under single-model and single-scale test.