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Summary of the Cooking Activity Recognition Challenge
Activity Recognition from Skeleton and Acceleration Data Using CNN and GCN
Let's Not Make It Complicated-Using Only LightGBM and Naive Bayes for Macro- and Micro-Activity Recognition from a Small Dataset
Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data
SCAR-Net: Scalable ConvNet for Activity Recognition with Multimodal Sensor Data
Multi-sampling Classifiers for the Cooking Activity Recognition Challenge
Multi-class Multi-label Classification for Cooking Activity Recognition
Cooking Activity Recognition with Convolutional LSTM Using Multi-label Loss Function and Majority Vote
Identification of Cooking Preparation Using Motion Capture Data: A Submission to the Cooking Activity Recognition Challenge
Cooking Activity Recognition with Varying Sampling Rates Using Deep Convolutional GRU Framework.
Activity Recognition from Skeleton and Acceleration Data Using CNN and GCN
Let's Not Make It Complicated-Using Only LightGBM and Naive Bayes for Macro- and Micro-Activity Recognition from a Small Dataset
Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data
SCAR-Net: Scalable ConvNet for Activity Recognition with Multimodal Sensor Data
Multi-sampling Classifiers for the Cooking Activity Recognition Challenge
Multi-class Multi-label Classification for Cooking Activity Recognition
Cooking Activity Recognition with Convolutional LSTM Using Multi-label Loss Function and Majority Vote
Identification of Cooking Preparation Using Motion Capture Data: A Submission to the Cooking Activity Recognition Challenge
Cooking Activity Recognition with Varying Sampling Rates Using Deep Convolutional GRU Framework.