Xavier Giro-i-Nieto

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Cross-Modal Machine Translation

The advances on neural machine translation across natural language have opened new venues in the field of cross-modal analysis. Given the unified the machine learning framework broadly adopted by the language and vision communities, novel opportunities have arisen by using deep learning framework to transform across modalities. This talk will provide an overview of the state of the art on cross-modal translation and (eg. lipreading, facial animation, sign language) and present our work in speaker visualization from speech.

Xavier Giro-i-Nieto is a learning enthusiast working as an associate professor at the Universitat Politecnica de Catalunya (UPC), in Barcelona, and a certified instructor at the NVIDIA Deep Learning Institute. He has been a visiting scholar at Columbia University and works regularly with Dublin City University, the Barcelona Supercomputing Center and Vilynx. His research interests focus on deep learning for computer vision, speech and natural language processing. His current service includes associate editor of the IEEE Transactions in Multimedia. Xavier Giro-i-Nieto is a learning enthusiast working as an associate professor at the Universitat Politecnica de Catalunya (UPC), in Barcelona, and a certified instructor at the NVIDIA Deep Learning Institute. He has been a visiting scholar at Columbia University and works regularly with Dublin City University, the Barcelona Supercomputing Center and Vilynx. His research interests focus on deep learning for computer vision and natural language processing applied to large scale image retrieval, affective computing, lifelogging from wearables and visual saliency prediction. His current service includes associate editor of the IEEE Transactions in Multimedia and ACM SIGMM Records.

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