shuangshuangguo / eco-efficient-video-understanding Goto Github PK

By Mohammadreza Zolfaghari, Kamaljeet Singh, Thomas Brox

• 2018.4.17: Repository for ECO.

This repository will contains all the required models and scripts for the paper ECO: Efficient Convolutional Network for Online Video Understanding.

In this work, 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. Together with a sampling strategy, which exploits that neighboring frames are largely redundant, this yields high-quality action classification and video captioning at up to 230 videos per second, where each video can consist of a few hundred frames. The approach achieves competitive performance across all datasets while being 10x to 80x faster than state-of-the-art methods.

Action Recognition on UCF101 and HMDB51	Video Captioning on MSVD dataset

Model trained on UCF101 dataset	Model trained on Something-Something dataset

1. Code and Models
2. Data
3. Tables and Results
4. Demo

Mohammadreza Zolfaghari

Questions can also be left as issues in the repository. We will be happy to answer them.