Real-time sign language translation systems, that convert continuous sign sequences to
text/speech, will facilitate communication between the deaf-mute community and the normal …

With the advancement of computer vision, human action recognition (HAR) has shown its
broad research worth and application prospects in a wide range of fields such as intelligent …

Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …

Multi-view sequential learning is a fundamental problem in machine learning dealing with
multi-view sequences. In a multi-view sequence, there exists two forms of interactions …

Multimodal sentiment analysis is a core research area that studies speaker sentiment
expressed from the language, visual, and acoustic modalities. The central challenge in …

Human speech is often accompanied by hand and arm gestures. We present a method for
cross-modal translation from" in-the-wild" monologue speech of a single speaker to their …

Learning multimodal representations is a fundamentally complex research problem due to
the presence of multiple heterogeneous sources of information. Although the presence of …

Human face-to-face communication is a complex multimodal signal. We use words
(language modality), gestures (vision modality) and changes in tone (acoustic modality) to …

Millions of hearing impaired people around the world routinely use some variants of sign
languages to communicate, thus the automatic translation of a sign language is meaningful …

Computational modeling of human multimodal language is an emerging research area in
natural language processing spanning the language, visual and acoustic modalities …