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Found 5 papers, 2 papers with code
Explaining, Analyzing, and Probing Representations of Self-Supervised Learning Models for Sensor-based Human Activity Recognition
In recent years, self-supervised learning (SSL) frameworks have been extensively applied to sensor-based Human Activity Recognition (HAR) in order to learn deep representations without data annotations.
Unsupervised Interpretable Basis Extraction for Concept-Based Visual Explanations
An important line of research attempts to explain CNN image classifier predictions and intermediate layer representations in terms of human understandable concepts.
Temporal Feature Alignment in Contrastive Self-Supervised Learning for Human Activity Recognition
Automated Human Activity Recognition has long been a problem of great interest in human-centered and ubiquitous computing.
Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity Recognition
We extend a number of recent contrastive self-supervised approaches for the task of Human Activity Recognition, leveraging inertial and skeleton data.
Being the center of attention: A Person-Context CNN framework for Personality Recognition
Experiments on two public datasets demonstrate the effectiveness of jointly modeling the mutual Person-Context information, outperforming the state-of-the art-results for personality recognition in two different scenarios.
