A Multi-Task Neural Approach for Emotion Attribution, Classification, and Summarization
A Multi-Task Neural Approach for Emotion Attribution, Classification, and Summarization
复制标题
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Xiangyang Xue
中科院分区：
文献类型：
--
作者：
Guoyun Tu;Yanwei Fu;Boyang Li;Jiarui Gao;Yu-Gang Jiang;Xiangyang Xue
Emotional content is a crucial ingredient in usergenerated videos. However, the sparsity of emotional expressions in the videos poses an obstacle to visual emotion analysis. In this paper, we propose a new neural approach, Bi-stream Emotion Attribution-Classification Network (BEAC-Net), to solve three related emotion analysis tasks: emotion recognition, emotion attribution, and emotion-oriented summarization, in a single integrated framework. BEAC-Net has two major constituents, an attribution network and a classification network. The attribution network extracts the main emotional segment that classification should focus on in order to mitigate the sparsity issue. The classification network utilizes both the extracted segment and the original video in a bi-stream architecture. We contribute a new dataset for the emotion attribution task with human-annotated ground-truth labels for emotion segments. Experiments on two video datasets demonstrate superior performance of the proposed frameworkandthecomplementarynatureofthedualclassification streams.
