About |
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This workshop calls for scientific works that illustrate the most recent progress on multi-modal deep learning. In particular, multi-modal data capture, integration, modelling, understanding and analysis, and how to leverage them to derive accurate and robust AI models in many applications. It is a timely topic following the rapid development of deep learning technologies and their remarkable applications to many fields. It will serve as a forum to bring together active researchers and practitioners to share their recent advances in this exciting area. In particular, we solicit original and high-quality contributions in: (1) presenting state-of-the-art theories and novel application scenarios related to multi-modal deep learning; (2) surveying the recent progress in this area; and (3) developing benchmark datasets and evaluations. We welcome contributions coming from various communities (i.e., visual computing, machine learning, multimedia analysis, distributed and cloud computing, etc.) to submit their novel results. |
Call for Papers |
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The list of topics includes, but not limited to:
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Summary |
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MMDLCA 2020 : International Workshop on Multi-Modal Deep Learning: Challenges and Applications will take place in Milan, Italy. It’s a 1 day event starting on Jan 11, 2021 (Monday) and will be winded up on Jan 11, 2021 (Monday). MMDLCA 2020 falls under the following areas: DEEP LEARNING, COMPUTER VISION, MACHINE LEARNING, ARTIFICIAL INTELLIGENCE, etc. Submissions for this Workshop can be made by Oct 17, 2020. Authors can expect the result of submission by Nov 10, 2020. Upon acceptance, authors should submit the final version of the manuscript on or before Nov 15, 2020 to the official website of the Workshop. Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines. Other Details of the MMDLCA 2020
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Credits and Sources |
| [1] MMDLCA 2020 : International Workshop on Multi-Modal Deep Learning: Challenges and Applications |
