论文标题
代表学习中的本地和全球语义融合
Fuse Local and Global Semantics in Representation Learning
论文作者
论文摘要
我们在表示学习（FLAGS）中提出了保险丝本地和全球语义，以生成更丰富的表示。标志旨在从图像中提取全球语义和本地语义，以使各种下游任务受益。它在常见的线性评估协议下显示了有希望的结果。我们还对Pascal VOC和可可进行检测和分割，以显示标志提取的表示形式是可转移的。
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.
