Cross Modal Distillation for Supervision Transfer

被引：352

作者：

Gupta, Saurabh ^{[1
]}

Hoffman, Judy ^{[1
]}

Malik, Jitendra ^{[1
]}

机构：

[1] Univ Calif Berkeley, Berkeley, CA 94720 USA

来源：

2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) | 2016年

基金：

美国国家科学基金会;

关键词：

D O I：

10.1109/CVPR.2016.309

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this work we propose a technique that transfers supervision between images from different modalities. We use learned representations from a large labeled modality as supervisory signal for training representations for a new unlabeled paired modality. Our method enables learning of rich representations for unlabeled modalities and can be used as a pre-training procedure for new modalities with limited labeled data. We transfer supervision from labeled RGB images to unlabeled depth and optical flow images and demonstrate large improvements for both these cross modal supervision transfers.

引用

页码：2827 / 2836

页数：10