ECO: Efficient Convolutional Network for Online Video Understanding

Understand

The state of the art in video understanding suffers from two problems: (1) The major part of reasoning is performed locally in the video, therefore, it misses important relationships within actions that span several seconds.

• (2) While there are local methods with fast per-frame processing, the processing of the whole video is not efficient and hampers fast video retrieval or online classification of long-term activities.
• In this paper, we introduce a network architecture that takes long-term content into account and enables fast per-video processing at the same time.
• The architecture is based on merging long-term content already in the network rather than in a post-hoc fusion.