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Found 7 papers, 4 papers with code
A collection of the accepted papers for the Human-Centric Representation Learning workshop at AAAI 2024
This non-archival index is not complete, as some accepted papers chose to opt-out of inclusion.
CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking
Limited availability of labeled data for machine learning on multimodal time-series extensively hampers progress in the field.
Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study
In the context of mobile sensing environments, various sensors on mobile devices continually generate a vast amount of data.
COCOA: Cross Modality Contrastive Learning for Sensor Data
Contrastive Learning (CL) is one of the most well-known approaches in SSL that attempts to learn general, informative representations of data.
Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data
Unlike existing reviews of SSRL that have pre-dominately focused upon methods in the fields of CV or NLP for a single modality, we aim to provide the first comprehensive review of multimodal self-supervised learning methods for temporal data.
Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding
Change Point Detection (CPD) methods identify the times associated with changes in the trends and properties of time series data in order to describe the underlying behaviour of the system.
ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data
Extracting informative and meaningful temporal segments from high-dimensional wearable sensor data, smart devices, or IoT data is a vital preprocessing step in applications such as Human Activity Recognition (HAR), trajectory prediction, gesture recognition, and lifelogging.
Ranked #3 on
Change Point Detection
on TSSB
(Covering metric)
