  We propose Fuse Local and Global Semantics in Representation Learning (FLAGS)
to generate richer representations. FLAGS aims at extract both global and local
semantics from images to benefit various downstream tasks. It shows promising
results under common linear evaluation protocol. We also conduct detection and
segmentation on PASCAL VOC and COCO to show the representations extracted by
FLAGS are transferable.
