Recent trends in Wearable Human Activity Recognition (WHAR) have led to an
unprecedented 42.9% increase in scholarly articles in 2022, underscoring the urgency for a …

Research has shown the complementarity of camera-and inertial-based data for modeling
human activities, yet datasets with both egocentric video and inertial-based sensor data …

Continual learning (CL) aims to empower machine learning models to learn continually from
new data, while building upon previously acquired knowledge without forgetting. As …

Continual egocentric activity recognition aims to understand diverse first-person activities
from the multimodal data of a wearable device captured in streaming environments, which is …

Grounding 3D scene affordance aims to locate interactive regions in 3D environments,
which is crucial for embodied agents to interact intelligently with their surroundings. Most …

Egocentric non-intrusive sensing of human activities of daily living (ADL) in free-living
environments represents a holy grail in ubiquitous computing. Existing approaches, such as …

Deep neural networks, despite their exceptional performance on individual tasks, face the
challenge of catastrophic forgetting when incrementally learning new scenarios. Existing …

In this paper, we explore a novel Text-supervised Egocentic Semantic Segmentation (TESS)
task that aims to assign pixel-level categories to egocentric images weakly supervised by …

Generative artificial intelligence (AI) systems based on large-scale pretrained foundation
models (PFMs) such as vision-language models, large language models (LLMs), diffusion …

The rapid advancement of wearable sensors has significantly facilitated data collection in
our daily lives. Human activity recognition (HAR), a prominent research area in wearable …