cjlal,github

activityrecognition

Resources about activity recognition-行为识别资料

daic_woz_nlp

NLP/ Linguistic Approach to modelling PHQ8 scores.

depression-analysis

Analysis of depression using Emotion recognition from facial image and text classication

depression-detect

Predicting depression from acoustic features of speech using a Convolutional Neural Network.

depression-detection

Depression detection using multi-modal fusion framework composed of deep convolutional neural network (DCNN) and deep neural network (DNN) models.

depression-detection-through-multi-modal-data

Conventionally depression detection was done through extensive clinical interviews, wherein the subject’s re- sponses are studied by the psychologist to determine his/her mental state. In our model, we try to imbibe this approach by fusing the 3 modalities i.e. word context, audio, and video and predict an output regarding the mental health of the patient. The output is divided into a binary yes/no denoting whether the patient has symptoms of depression. We’ve built a deep learning model that fuses these 3 modalities, assigning them appropriate weights, and thus gives an output.

depressiondectection

depressiondetect

Detecting depression using DAIC WOZ dataset

distracted-driving-detection

分心驾驶检测，检测驾驶员的驾驶状态，对分心驾驶的驾驶员做出提醒

docformer

Implementation of DocFormer: End-to-End Transformer for Document Understanding, a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU)