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Found 7 papers, 3 papers with code
COMODO: Cross-Modal Video-to-IMU Distillation for Efficient Egocentric Human Activity Recognition
By distilling knowledge from video representations, our approach enables the IMU encoder to inherit rich semantic information from video while preserving its efficiency for real-world applications.
SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition
In this work, we bridge the gap between wearable sensor technology and personalized AI assistants by enabling Large Language Models (LLMs) to understand time-series tasks like human activity recognition (HAR).
Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned
Furthermore, our framework incorporates a stacking fusion module to combine diverse information from three types of curriculum learning, resulting in a strong and thorough learning process.
Urban Region Embedding via Multi-View Contrastive Prediction
Recently, learning urban region representations utilizing multi-modal data (information views) has become increasingly popular, for deep understanding of the distributions of various socioeconomic features in cities.
QLEVR: A Diagnostic Dataset for Quantificational Language and Elementary Visual Reasoning
Synthetic datasets have successfully been used to probe visual question-answering datasets for their reasoning abilities.
Ranked #1 on
Visual Question Answering (VQA)
on QLEVR
On the Generation of Medical Dialogs for COVID-19
Training complex dialog generation models on small datasets bears high risk of overfitting.
Self-supervised Regularization for Text Classification
The SSL task is unsupervised, which is defined purely on input texts without using any human-provided labels.
