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IEEE JSTSP 2020 : Special Issue on Deep Learning for Multi-modal Intelligence across Speech, Language, Vision, and Heterogeneous Signals

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Link: https://signalprocessingsociety.org/blog/ieee-jstsp-special-issue-deep-learning-multi-modal-intelligence-across-speech-language-vision
 
When N/A
Where N/A
Submission Deadline Sep 1, 2019
Notification Due Nov 1, 2019
Final Version Due Mar 15, 2020
Categories    gan   vision   deep learning
 

Call For Papers

IEEE Journal of Selected Topics in Signal Processing Deep Learning(IF: 6.688, 19/265)

Call for Papers --IE EE Journal of Selected Topics in Signal Processing Deep Learning for Multi-modal Intelligence across Speech, Language, Vision, and Heterogeneous Signals

In the past years, thanks to the disruptive advances in deep learning, significant progress has been made in speech processing, language processing, computer vision, and applications across multiple modalities. Despite the superior empirical results, however, there remain importantissues to be addressed. Both theoretical and empirical advancements are expected to drive further performance improvements, which in turn would generate new opportunities for indepth studies of emerging novel learning and modeling methodologies. Moreover, many
problems in artificial intelligence involve more than one modality, such as language, vision,speech and heterogeneous signals. Techniques developed for different modalities can often be successfully cross-fertilized. Therefore, it is of great interest to study multimodal modeling and learning approaches across more than one modality. The goal of this special issue is to bring together a diverse but complementary set of contributions on emerging deep learning methods for problems across multiple modalities. The topics of this special issue include but not limit to the following:

Topics of interest in this special issue include (but are not limited to):
• Fundamental problems and methods for processing multi-modality data including
language, speech, image, video, and heterogeneous signals.
• Pre-training, representation learning, multitask learning, low-shot learning, and
reinforcement learning of multimodal problems across natural language, speech, image,
and video
• Deep learning methods and applications for cross-modalities, such as image captioning,
visual question answering, visual story-telling, text-to-image synthesis, visionlanguage navigation, etc.
• Evaluation metrics of multimodal applications.

Important Dates
Submissions due: 01-Sept-2019
First review: 01-Nov-2019
Revised manuscript due: 15-Dec-2019
Second review: 1-Feb-2020
Final manuscripts due: 15-Mar-2020

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