Researchers have been developing Hand Gesture Recognition (HGR) systems to enhance
natural, efficient, and authentic human-computer interaction, especially benefiting those who …

Despite the recent success of deep learning in continuous sign language recognition
(CSLR), deep models typically focus on the most discriminative features, ignoring other …

Abstract Zero-Shot Learning (ZSL) has rapidly advanced in recent years. Towards
overcoming the annotation bottleneck in the Dynamic Hand Gesture Recognition (DHGR) …

Although many network traffic protection methods have been developed to protect user
privacy, encrypted traffic can still reveal sensitive user information with sophisticated …

Landmark-based human action recognition in videos is a challenging task in computer
vision. One key step is to design a generic approach that generates discriminative features …

Sign Language Recognition (SLR) represents a revolutionary technology aiming to
establish communication between hearing impaired and non-hearing impaired …

This paper contributes to the challenge of learning a function on streamed multimodal data
through evaluation. The core of the result of our paper is the combination of two quite …

Automatic hand gesture recognition in video sequences has widespread applications,
ranging from home automation to sign language interpretation and clinical operations. The …

Different from traditional video retrieval, sign language retrieval is more biased towards
understanding the semantic information of human actions contained in video clips. Previous …

The development of machine learning algorithms in the cyber security domain has been
impeded by the complex, hierarchical, sequential and multimodal nature of the data …