Emotion is a key element in user-generated videos. However, it is difficult to understand emotions conveyed in such videos due to the complex and unstructured nature of user-generated content and the sparsity of video frames expressing emotion. In this paper, for the first time, we study the problem of transferring knowledge from heterogeneous external sources, including image and textual data, to facilitate three related tasks in understanding video emotion: emotion recognition, emotion attribution and emotion-oriented summarization. Specifically, our framework (1) learns a video encoding from an auxiliary emotional image dataset in order to improve supervised video emotion recognition, and (2) transfers knowledge from an auxiliary textual corpora for zero-shot recognition of emotion classes unseen during training. The proposed technique for knowledge transfer facilitates novel applications of emotion attribution and emotion-oriented summarization. A comprehensive set of experiments on multiple datasets demonstrate the effectiveness of our framework.
@article{arxiv.1511.04798,
title = {Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization},
author = {Baohan Xu and Yanwei Fu and Yu-Gang Jiang and Boyang Li and Leonid Sigal},
journal= {arXiv preprint arXiv:1511.04798},
year = {2018}
}
Comments
13 pages, 11 figures. Published at the IEEE Transactions on Affective Computing