Call for Papers: Special Issue on Multimodal Learning

Guest Editors

• Michael Ying Yang, University of Twente
• Paolo Rota, University of Trento
• Massimiliano Mancini,  University of Tübingen
• Zeynep Akata, University of Tübingen
• Bodo Rosenhahn, Leibniz Universität Hannover
• Vittorio Murino, University of Verona

The exploitation of the power of big data in the last few years led to a big step forward in many applications of Computer Vision. However, most of the tasks tackled so far are involving visual modality only, mainly due to the unbalanced number of labelled samples available among modalities (e.g., there are many huge labelled datasets for images while not as many for audio or IMU based classification), resulting in a huge gap in performance when algorithms are trained separately.

Recently, a few works have started to exploit the synchronization of multimodal streams (e.g., audio/video, RGB/depth, RGB/Lidar, visual/text, text/audio) to transfer semantic information from one modality to another reaching surprising results. Interesting applications are also proposed in a self-supervised fashion, where multiple modalities are learned correspondences without need of manual labelling, resulting in a more powerful set of features compared to those learned processing the two modalities separately. Other works have also shown that particular training paradigms allow neural networks to perform well when one of the modalities is missing due to sensor failure or unfavourable environmental conditions. These topics are gaining lots of interest in the computer vision community in recent years.

The information fusion from multiple sensors is a topic of major interest also in industry, the exponential growth of companies working on automotive, drone vision, surveillance or robotics are just a few examples. Many companies are trying to automate processes, by using a large variety of control signals from different sources.

The creation of the “Multimodal Learning and Applications” workshop series has been motivated to generate momentum around this topic of growing interest, and to encourage interdisciplinary interaction and collaboration between computer vision, multimedia, remote sensing, and robotics communities that will serve as a forum for research groups from academia and industry.

This “Multimodal Learning” IJCV special issue shall serve as a publication venue for improving our understanding of the multimodal learning problems and also provide a place to discuss approaches to apply and refine academic results to achieve working real-world solutions. Participants of the workshop and scientists only interested in this journal are both invited to submit their papers.

Aims & Scope

We expect contributions involving, but not limited to, image, video, audio, depth, IR, IMU, laser, text, drawings, synthetic, etc. Position papers with feasibility studies and cross-modality issues with highly applicative flair are also encouraged. Topics of interest are, but not limited to,

● Multimodal learning

● Cross-modal learning

● Self-supervised learning for multimodal data

● Multimodal data generation and sensors

● Unsupervised learning on multimodal data

● Cross-modal adaptation

● Multimodal data fusion and data representation

● Multimodal transfer learning

● Multimodal scene understanding

● Vision and language

● Vision and sound

● Multimodal applications

Important Dates

• Full paper submission deadline: extended to April 3rd 2023
• Review deadline: June 30th, 2023
• Author response deadline: July 24th, 2023
• Final notification: August 24th, 2023
• Final manuscript submission: September 24th, 2023

Submission guidelines

Papers must be prepared in accordance with the Journal guidelines: www.springer.com/11263

Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process. Manuscripts will be subject to a peer reviewing process and must conform to the author guide lines available on the IJCV website at: https://www.springer.com/11263 

Author Resources

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.  

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