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MIC-TJU in MediaEval 2017 Emotional Impact of Movies Task

Yun Yi, Hanli Wang, Jiangchuan Wei
2017 MediaEval Benchmarking Initiative for Multimedia Evaluation  
To predict the emotional impact and fear of movies, we propose a framework which employs four audio-visual features.  ...  In particular, we utilize the features extracted by the methods of motion keypoint trajectory and convolutional neural networks to depict the visual information, and extract a global and a local audio  ...  INTRODUCTION The 2017 emotional impact of movies task is a challenging task, which contains two subtasks (i.e., valence-arousal prediction and fear prediction).  ... 
dblp:conf/mediaeval/YiWW17 fatcat:7ig4a3ko7jasnjmzh6e2uhkjui

Imbalance Learning-based Framework for Fear Recognition in the MediaEval Emotional Impact of Movies Task

Xiaotong Zhang, Xingliang Cheng, Mingxing Xu, Thomas Fang Zheng
2018 Interspeech 2018  
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]

Jie Zhang, Yin Zhao, Longjun Cai, Chaoping Tu, Wu Wei
2019 arXiv   pre-print
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

Affective analysis of videos [article]

Esra Acar Celik, Technische Universität Berlin, Technische Universität Berlin, Sahin Albayrak
2017
As emotions play an important role for multimedia content selection and consumption in peoples' daily life, analyzing the emotional content (i.e., affective content) of videos in order to structure mostly  ...  In the first part of the thesis, we first address the issue of feature engineering in the field of video affective content analysis.  ...  Table 6 .9 provides a comparison of our best performing FSP method with the best run of participating teams (in terms of MAP) in the Media-Eval 2015 VSD task (i.e., Affective Impact of Movies -including  ... 
doi:10.14279/depositonce-5824 fatcat:wmjex7tq5nco7dhdnzil52yaoq