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In this paper, we use Long Short-Term Memory networks (LSTMs) to model the temporal context in audio-video features of movies. We present continuous emotion ...
This paper uses Long Short-Term Memory networks (LSTMs) to model the temporal context in audio-video features of movies and presents continuous emotion ...
This paper presents our automatic prediction of dimensional emotional state for Audio-Visual Emotion Challenge (AVEC 2017), which uses multi-features and fusion ...
Missing: Movies | Show results with:Movies
Nov 21, 2018 · Bibliographic details on Multimodal Continuous Prediction of Emotions in Movies using Long Short-Term Memory Networks.
Sep 28, 2021 · This paper focuses on experienced emotion prediction and evaluates the proposed method on the extended COGNIMUSE dataset [4, 15]. First, the ...
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This paper presents our automatic prediction of dimensional emotional state for Audio-Visual Emotion Challenge (AVEC 2017), which uses multi-features and fusion ...
Missing: Movies | Show results with:Movies
This paper presents the automatic prediction of dimensional emotional state for Audio-Visual Emotion Challenge (AVEC 2017), which uses multi- features and ...
Missing: Movies | Show results with:Movies
The goal of this study is to develop and analyze multimodal models for predicting experienced affective responses of viewers watching movie clips.
Missing: Continuous | Show results with:Continuous
These studies reveal that LSTM manage to capture the temporal information of the emotional dimension significantly, outperforming SVR. Later work by Huang et al ...
Missing: Movies | Show results with:Movies
We propose novel multimodal models for predicting movie induced emotions. These models incorporate perceived emotion annotations in the hierarchical.