lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Existing models for multimodal sensor data are inefficient and require extensive annotation.UbiComp '22COCOA: Cross Modality Contrastive Learning for Sensor Data
Visualization, Analytics, and Data Understanding / Time Series Semantic Retrieval and Trend Analysis
Existing models for multimodal sensor data are inefficient and require extensive annotation.
Similar questions
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Welding engineers struggle to efficiently analyze multidimensional time series data, hindering quality monitoring.lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Decision-makers and the public struggle to identify misleading charts in time-series data.lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Non-technical experts struggle to participate in time-series model design, limiting practical application.lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Users struggle to precisely find needed trend features when analyzing time series data with tools.lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Doctors and users struggle to understand the rationale behind time-series classification results.UbiComp '24Time for an Explanation: A Mini-Review of Explainable Physio-Behavioural Time-Series Classification
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Wearable devices have insufficient accuracy and robustness for complex activity recognition.UbiComp '24rTsfNet: A DNN Model with Multi-head 3D Rotation and Time Series Feature Extraction for IMU-based Human Activity Recognition
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