Traditional frame-based cameras, despite their effectiveness and usage in computer vision,
exhibit limitations such as high latency, low dynamic range, high power consumption, and …

Neuromorphic sensors, specifically event cameras, revolutionize visual data acquisition by
capturing pixel intensity changes with exceptional dynamic range, minimal latency, and …

Event cameras have recently been shown beneficial for practical vision tasks such as action
recognition thanks to their high temporal resolution power efficiency and reduced privacy …

Human action recognition (HAR) plays a key role in various applications such as video
analysis, surveillance, autonomous driving, robotics, and healthcare. Most HAR algorithms …

Few-shot fine-grained human (FGH) action recognition is crucial in the context of human-
robot interaction within open-set real-world environments. Existing works mainly focus on …

In the field of action recognition, event cameras have marked a breakthrough by capturing
motion dynamics beyond the capability of traditional cameras, thanks to their high temporal …

Event cameras produce asynchronous and sparse event streams capturing changes in light
intensity. Overcoming limitations of conventional frame-based cameras, such as low …

Action recognition from an egocentric viewpoint is a crucial perception task in robotics and
enables a wide range of human-robot interactions. While most computer vision approaches …

Event-based Action Recognition (EAR) possesses the advantages of high-temporal
resolution capturing and privacy preservation compared with traditional action recognition …

Human action recognition is crucial for intelligent robots, especially in the realm of human-
robot collaboration research. Recent advancements in human pose estimation algorithms …