INFORMATION ON MMDLCA
Deep learning is now recognized as one of the key software engines that drives the new industrial revolution. The majority of current deep learning research efforts have been dedicated to single-modal data processing. Pronounced manifestations are deep learning based visual recognition and speech recognition. Although significant progress made, single-modal data is often insufficient to derive accurate and robust deep models in many applications. Our digital world is by nature multi-modal, that combines different modalities of data such as text, audio, images, animations, videos and interactive content. Multi-modal is the most popular form for information representation and delivery. For example, posts for hot social events are typically composed of textual descriptions, images and videos. For medical diagnosis, the joint use of medical imaging and textual reports is also essential. Multi-modal data is common for human to make accurate perceptions and decisions. Multi-modal deep learning that is capable of learning from information presented in multiple modalities and consequently making predictions based on multi-modal input is much in demand.

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.

TOPICS
The list of topics includes, but not limited to:
• Multi-modal intelligent data acquisition and management
• Multi-modal benchmark datasets and evaluations
• Multi-modal representation learning and applications
• Multi-modal data driven visual analysis and understanding
• Multi-modal object detection, classification, recognition and segmentation
• Multi-modal information tracking, retrieval and identification
• Multi-modal social event analysis
• Multi-modal medical diagnosis
• Multi-modal machine learning from incomplete data
• Deep neural network architectures for multi-modal data processing
• Multi-modal big data analytics
• Emerging multi-modal deep learning applications

SUBMISSION GUIDELINES
Submission site: https://easychair.org/my/conference?conf=mmdlca2020#
Submissions must be formatted in accordance with the Springer's Computer Science Proceedings guidelines (https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines). Two types of contribution will be considered:
• Full papers (10-12 pages, including references)
• Short papers (6-8 pages, including references)
Accepted manuscripts will be included in the ICPR 2020 Workshop Proceedings Springer volume. Once accepted, at least one author is expected to attend the event and orally present the paper.

IMPORTANT DATES
• Workshop submission deadline: Oct. 15th, 2020
• Workshop author notification: Nov. 10th, 2020
• Camera-ready submission: Nov. 15th, 2020
• Finalized workshop program: Dec. 1st, 2020
• Workshop day: Jan. 11, 2021

CONTACTS
For any inquiry you may have, please send an email to:
Zhineng Chen at zhineng.chen@ia.ac.cn