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- W4312986677 abstract "Automatic emotion recognition from video streams is an essential challenge for various applications including human behavior understanding, mental disease diagnosis, surveillance, or human-machine interaction. In this paper we introduce a novel, completely automatic, multimodal emotion recognition framework based on audio and visual fusion of information designed to leverage the mutually complementary nature of features while maintaining the modality-distinctive information. Specifically, we integrate the spatial, channel and temporal attention into the visual processing pipeline and the temporal self-attention into the audio branch. Then, a multimodal cross-attention fusion strategy is introduced that effectively exploits the relationship between the audio and video features. The experimental evaluation performed on RAVDESS, a publicly available database, validates the proposed approach with average accuracy scores superior to 87.85%. When compared with the state-of the art methods the proposed framework returns accuracy gains of more than 1.85%." @default.
- W4312986677 created "2023-01-05" @default.
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- W4312986677 date "2022-01-01" @default.
- W4312986677 modified "2023-10-14" @default.
- W4312986677 title "Emotion Recognition in Video Streams Using Intramodal and Intermodal Attention Mechanisms" @default.
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- W4312986677 doi "https://doi.org/10.1007/978-3-031-20716-7_23" @default.
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