Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of
sensors have encouraged the development of smart environments, such as smart homes …

Deep neural networks have become prevalent in human analysis, boosting the performance
of applications, such as biometric recognition, action recognition, as well as person re …

Recognizing interactive action plays an important role in human-robot interaction and
collaboration. Previous methods use late fusion and co-attention mechanism to capture …

The world's elderly population continues to grow at an unprecedented rate, creating a need
to monitor the safety of an aging population. One of the current problems is accurately …

While recognition accuracies of video classification models trained on conventional
benchmarks are gradually saturating, recent studies raise alarm about the learned …

Video self-supervised learning (VSSL) has made significant progress in recent years.
However, the exact behavior and dynamics of these models under different forms of …

The ability to anticipate possible future human actions is essential for a wide range of
applications, including autonomous driving and human-robot interaction. Consequently …

Recognition of human actions using machine learning requires extensive datasets to
develop robust models. Nevertheless, obtaining real-world data presents challenges due to …

Modality selection is an important step when designing multimodal systems, especially in
the case of cross-domain activity recognition as certain modalities are more robust to …

Supervised deep learning methods using image data as input have shown promising results
in the context of vehicle control. However, these supervised methods have two main …