How do humans recognize an object in a piece of video? Due to the
deteriorated quality of single frame, it may be hard for people to identify an
occluded object in this frame by just utilizing information within one image.
We argue that there are two important cues for humans to recognize objects in
videos: the global semantic information and the local localization information.
Recently, plenty of methods adopt the self-attention mechanisms to enhance the
features in key frame with either global semantic information or local
localization information. In this paper we introduce memory enhanced
global-local aggregation (MEGA) network, which is among the first trials that
takes full consideration of both global and local information. Furthermore,
empowered by a novel and carefully-designed Long Range Memory (LRM) module, our
proposed MEGA could enable the key frame to get access to much more content
than any previous methods. Enhanced by these two sources of information, our
method achieves state-of-the-art performance on ImageNet VID dataset. Code is
available at https://github.com/Scalsol/mega.pytorch.

本文提出一种基于记忆增强的全局局部聚合（MEGA）网络，该网络融合了全局语义信息和局部定位信息，并通过新设计的长期记忆模块让关键帧可以获得比之前任何方法更多的内容，从而在 ImageNet VID 数据集上取得了最先进的性能。