Call for Papers

Special Issue on the Visual Computer

Title: Multi-Modality Feature Learning for Visual Understanding (MMFL)

Extract robust multi-modality feature is an important task for visual understanding. There has been significant progress in recent years thanks to the rapid progress in machine learning tools and computational resources as well as the availability of large amount of data. Not only is its application performance greatly improved, but also its application fields significantly widened. However, how to learn and combine multi-modality features still facing many challenges in solving real world problems. Lots of real world applications, such as human behavior analysis, robot tracking, object (pedestrian) detection, medical disease diagnosis, often require multiple modalities including RGB cue, depth cue, navigation cue, spatio-temporal structure cue, etc. The goal of this special issue is to bring together researchers working on different modalities to exchange their work and discuss how to leverage and use multi-modality cues to improve visual understanding.
This special issue addressing the problems of visual understanding, such as some visual tasks of human behavior analysis and recognition, robot tracking, object (pedestrian) detection, medical image processing, and their applications with multiple modalities are invited. Both the theoretical and practical progresses are encouraged. The topics of interest for this workshop include but are not limited to the following areas:

Scope and Topics:


Paper submission and review:

Authors need to submit full papers online through the Visual Computer site at:

Selecting the choice “MMFL” that indicates this special issue.
Peer reviewing will follow the standard review process. Full length manuscripts are expected to follow the Visual Computer guidelines in:

Tentative working title: MMFL

The number of expected papers to be published in this Special Issue: 12


Submission portal open: Jun. 20st, 2021
Submission Deadline: Aug. 15st, 2021

Guest Editors

Dr. Zhigang Tu

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote sensing Wuhan University, Wuhan, China E-mail:

Dr. Yang Cong

State Key Laboratory of Robotics, SIA, Chinese Academy of Sciences (CAS), China E-mail:

Dr. Jun Liu

Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore E-mail:

Dr. Lei Zhang

School of Microelectronics and Communication Engineering, Chongqing University, China Email:

Dr. Zhou Ren

Wormpex AI Research, USA E-mail: