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Multi-modal Depression Estimation based on Sub-attentional Fusion. (arXiv:2207.06180v2 [cs.CV] UPDATED)
Aug. 19, 2022, 1:12 a.m. | Ping-Cheng Wei, Kunyu Peng, Alina Roitberg, Kailun Yang, Jiaming Zhang, Rainer Stiefelhagen
cs.CV updates on arXiv.org arxiv.org
Failure to timely diagnose and effectively treat depression leads to over 280
million people suffering from this psychological disorder worldwide. The
information cues of depression can be harvested from diverse heterogeneous
resources, e.g., audio, visual, and textual data, raising demand for new
effective multi-modal fusion approaches for automatic estimation. In this work,
we tackle the task of automatically identifying depression from multi-modal
data and introduce a sub-attention mechanism for linking heterogeneous
information while leveraging Convolutional Bidirectional LSTM as our backbone. …
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