Attention mechanisms have been found to be effective for human gaze estimation. To address the problem that traditional attention has limited ability to extract higher-order contextual information in gaze estimation tasks, an ECA attention mechanism-based gaze estimation network is proposed, which aims to effectively exploit the channel relations of features through a global average pooling layer without dimensionality reduction, suppress some facial regions that do not contribute to gaze estimation, and activate subtle facial features that can improve gaze estimation. The model can take full advantage of the user's appearance, which helps to improve the accuracy of the gaze estimation model. In this paper, experiments are conducted on the MPIIGaze dataset, and the results show that the network based on the channel attention mechanism can reduce the estimation error, and the model proposed in this paper can achieve more accurate gaze estimation.
Depression is one of the most common mental illnesses in the world today. Unlike anxiety in daily life, depression is often accompanied by prolonged low mood, slow thinking, unresponsiveness and difficulty in self-regulation. In severe cases, it can affect life and even lead to death. In this paper, a multimodal depression classification model based on BiGRU and BiLSTM is proposed in the publicly available Chinese dataset EATD-Corpus. Audio and text features are extracted using the vggish model and elmo respectively. The features are not fused. After the audio and text features are trained separately for detection, BiGRU and BiLSTM are adaptively weighted and fused to detect depression. The method has a precision value of 0.66, an F1-score value of 0.77 and a recall value of 0.97. The experimental results show that the performance of the method has been improved.
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