Sri International

Behjat Siddiquie

Ajay Divakaran

Harpreet Singh Sawhney

Elizabeth E Shriberg

Saad Masood Khan

Hui Ting Cheng

Jeffrey Lubin

Omar Javed

Mohamed R Amer

Timothy J Shields

Steven S Weiner

Jingen Liu

Colleen Richey

Ishani Chakraborty

David Chisolm

David Chisholm

Hapreet S Sawhney

A computing system for recognizing salient events depicted in a video utilizes learning algorithms to detect audio and visual features of the video. The computing system identifies one or more salient events depicted in the video based on the audio and visual features.

Recognizing salient video events through learning-based multimodal analysis of visual features and audio-based analytics

Technologies to detect persuasive multimedia content by using affective and semantic concepts extracted from the audio-visual content as well as the sentiment of associated comments are disclosed. The multimedia content is analyzed and compared with a persuasiveness model.

Exploiting multi-modal affect and semantics to assess the persuasiveness of a video

Technologies for analyzing temporal components of multimodal data to detect short-term multimodal events, determine relationships between short-term multimodal events, and recognize long-term multimodal events, using a deep learning architecture, are disclosed.

Dynamic hybrid models for multimodal analysis

A multi-modal interaction modeling system can model a number of different aspects of a human interaction across one or more temporal interaction sequences. Some versions of the system can generate assessments of the nature or quality of the interaction or portions thereof, which can be used to, among other things, provide assistance to one or more of the participants in the interaction.

Multi-modal modeling of temporal interaction sequences

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Plainsboro, NJ, United States of America

Behjat Siddiquie