Name
Abstract | ||
|---|---|---|
Regression modeling is a significant aspect of multi-modal continuous dimensional emotion recognition. Despite the developments of this domain, one of the limitations that severely impede the application of emotion recognition is that most methods utilized for regression modeling merely capture the temporal information. Motivated by this, we propose a new branch feature fusion framework BF-Net, whose core idea is to deeply combine local features captured by convolutional neural networks and temporal dependencies captured by long-short-term memory recurrent neural networks. The framework consists of two main components: two-branch structure and fusion of branches. The former captures local features and temporal dependencies respectively in an effective way. Besides, the latter utilizes an attention mechanism to fuse the features on a deep level. In this way, the framework achieves full utilization of the local features and temporal dependencies. The experiments on ULM-TSST dataset show that the proposed method is competitive or superior to the state-of-the-art works. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LSP.2022.3160373 | IEEE SIGNAL PROCESSING LETTERS |
Keywords | DocType | Volume |
Emotion recognition, Feature extraction, Convolution, Fuses, Convolutional neural networks, Data models, Context modeling, Multimodal emotion recognition, regression modeling, feature fusion | Journal | 29 |
ISSN | Citations | PageRank |
1070-9908 | 0 | 0.34 |
References | Authors | |
0 | 3 | |