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THUHCSI in MediaEval 2017 Emotional Impact of Movies Task
2017
MediaEval Benchmarking Initiative for Multimedia Evaluation
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In this paper we describe our team's approach to MediaEval 2017 Challenge Emotional Impact of Movies. ...
We also aim at the continuous flow of emotion, where using time-sequential models such as LSTM will be useful and effective. Fusion methods are also considered and discussed in this paper. ...
CONCLUSION AND DISCUSSION In this paper, we illustrate our approach to the MediaEval 2017 Challenge "Emotional Impact of Movies" task. ...
dblp:conf/mediaeval/JinYMX17
fatcat:doxbpqsx55fpfi363fk6pjc23u
Imbalance Learning-based Framework for Fear Recognition in the MediaEval Emotional Impact of Movies Task
2018
Interspeech 2018
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Experiments are conducted on the MediaEval 2017 Emotional Impact of Movies Task. ...
Fear recognition, which aims at predicting whether a movie segment can induce fear or not, is a promising area in movie emotion recognition. Research in this area, however, has reached a bottleneck. ...
The official metrics used in the MediaEval 2017 Emotional Impact of Movies Task are all calculated at the movie-level. ...
doi:10.21437/interspeech.2018-1744
dblp:conf/interspeech/ZhangCXZ18
fatcat:kr43fkxiszb6dcjkxgsi7zdtce
Video Affective Effects Prediction with Multi-modal Fusion and Shot-Long Temporal Context
[article]
2019
arXiv
pre-print
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Predicting the emotional impact of videos using machine learning is a challenging task considering the varieties of modalities, the complicated temporal contex of the video as well as the time dependency ...
Feature extraction, multi-modal fusion and temporal context fusion are crucial stages for predicting valence and arousal values in the emotional impact, but have not been successfully exploited. ...
Cases Analysis As shown in Fig. 5 and Fig. 6 , our emotion prediction pipelines can precisely predict the trend of movies' emotional impact for valence and arousal tasks. ...
arXiv:1909.01763v1
fatcat:v2764xs3hfaztm5dbmmtbgs3da