Recently, object detection has been a vital aspect in the vision community, while scale variation of objects in images or videos usually brings challenge for performance improvement. To combat this problem, conventional paradigms generally adopt image pyramid or Feature Pyramid Network (FPN) to process objects at different scales. However, existing multi-scale deep convolution neural networks mostly set different scales in a heuristic way, which may introduce inconsistency between the region of interest and the semantic scope. In this paper, we propose an innovative paradigm called Consistent Scale Normalization (CSN) to weaken the influence of scale variation for object detection. The proposed CSN can realize a consistent compression for the scale space of objects, in both training and testing phases. Extensive experimental testing is performed on COCO object detection benchmark in comparison with several state-of-the-art methods. In addition to object detection, experiments on instance segmentation and multi-task human pose estimation are also conducted. Furthermore, the CSN paradigm is beneficial to reduce the difficulty of network learning. The results verify the effectiveness and superiority of the CSN paradigm.