High quality feature representation can boost performance for object detection and other computer vision tasks. Modern object detectors resort to versatile feature pyramids to enrich the representation power but neglect that different fusing operations should be used for pathways of different directions to meet their different needs of information flow. This study proposes separated spatial semantic fusion (SSSF) that uses a channel attention block (CAB) in top-down pathway to pass semantic information and a spatial attention block (SAB) with a bottleneck structure in the bottom-up pathway to pass precise location signals to the top level with fewer parameters and less computation (compared with plain spatial attention without dimension reduction). SSSF is effective and has a great generality ability: it improves AP over 1.3% for object detection, about 0.8% over plain addition for fusing operation of the top-down path for semantic segmentation, and boost the instance segmentation performance in all metrics for both bounding box AP and mask AP.