论文阅读：Self-Supervised Video Representation Learning With Odd-One-Out Networks

目录

Contributions

Method

1、Model

2、Three sampling strategies.

3、Video frame encoding.

Results

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论文名称：Self-Supervised Video Representation Learning With Odd-One-Out Networks（2017 CVPR）

论文作者：Basura Fernando, Hakan Bilen, Efstratios Gavves, Stephen Gould

下载地址：https://openaccess.thecvf.com/content_cvpr_2017/html/Fernando_Self-Supervised_Video_Representation_CVPR_2017_paper.html

 

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Contributions

We propose a new self-supervised CNN pre-training technique based on a novel auxiliary task called odd-one-out learning. In this task, we sample subsequences from videos and ask the network to learn to predict the odd video subsequence. The odd video subsequence is sampled such that it has wrong temporal order of frames while the even ones have the correct temporal order. Our learning machine is implemented as multi-stream convolutional neural network, which is learned end-to-end. Using odd-one-out networks, we learn temporal representations for videos that generalizes to other related tasks such as action recognition.

 

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Method

1、Model

O3N is composed of (N+1) input branches, each contains five Convolutional layers and weight