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GTC ON-DEMAND

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Abstract:
We'll explore new techniques for TV show summarization using multimodal deep learning for saliency detection and fusion. For TV show summarization, the goal is to compact visual summary with informativeness and enjoyability to attract audience. In our work, we propose a multimodal summarization platform to integrate the multimodal saliences learned from video, audio, and text. Our work focuses on three aspects: 1) the saliency extraction for video, audio, and text using deep learning networks; 2) fusion framework design for multimodal information integration; 3) developing tools to speed up video processing. Using AI Vision, which is a public cloud-based AI service, we summarize a TV show with 11 hours duration in one minute.
We'll explore new techniques for TV show summarization using multimodal deep learning for saliency detection and fusion. For TV show summarization, the goal is to compact visual summary with informativeness and enjoyability to attract audience. In our work, we propose a multimodal summarization platform to integrate the multimodal saliences learned from video, audio, and text. Our work focuses on three aspects: 1) the saliency extraction for video, audio, and text using deep learning networks; 2) fusion framework design for multimodal information integration; 3) developing tools to speed up video processing. Using AI Vision, which is a public cloud-based AI service, we summarize a TV show with 11 hours duration in one minute.  Back
 
Topics:
Computer Vision, Intelligent Video Analytics, Video & Image Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8221
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Abstract:
We'll dive deep into VisionBrain, a deep learning platform for customized visual recognition in cloud. VisionBrain is developed by IBM Research, accessible on SuperVessel Cloud, and has been used in IBM commercial solutions. The platform aims to provide developer-customized model training and inference API services to support image/video object detection and classification. VisionBrain is based on container cloud and uses Marathon+Mesos for resource management. We'll focus on: (1) the architecture of VisionBrain, including user-defined data preprocessing, training, and inference with GPU-enabled container cloud, (2) novel deep learning technologies to enable customized model training with high accuracy and short training duration for visual recognition, and (3) how to do the intelligent GPU scheduling in container cloud for different workloads, and meet commercial SLA and high-availability requirements.
We'll dive deep into VisionBrain, a deep learning platform for customized visual recognition in cloud. VisionBrain is developed by IBM Research, accessible on SuperVessel Cloud, and has been used in IBM commercial solutions. The platform aims to provide developer-customized model training and inference API services to support image/video object detection and classification. VisionBrain is based on container cloud and uses Marathon+Mesos for resource management. We'll focus on: (1) the architecture of VisionBrain, including user-defined data preprocessing, training, and inference with GPU-enabled container cloud, (2) novel deep learning technologies to enable customized model training with high accuracy and short training duration for visual recognition, and (3) how to do the intelligent GPU scheduling in container cloud for different workloads, and meet commercial SLA and high-availability requirements.  Back
 
Topics:
Data Center & Cloud Infrastructure, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7226
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