[visionlist] Deadline Extension!

Dingwen Zhang zhangdingwen2006yyy at gmail.com
Sun Nov 1 22:05:35 -04 2020

One-month deadline extension for the IEEE T-CSVT SI on "Advanced Machine
Learning Methodologies for Large-Scale Video Object Segmentation and
Detection".  We welcome all works that are related to the object-oriented video
understanding task, including algorithms for segmenting, detecting,
tracking, recognizing a certain type of object category, or general object
categories in video sequences. Techniques for improving video quality and
enhancing feature representation for benefiting the object-oriented video
understanding tasks are also within our scope.

More detailed dates and scope of the special issue are listed below.
Looking forward to your contribution!


Manuscript submission:           1st December 2020

Preliminary results:                  1st February 2021

Revisions due:                         15th March 2021

Notification:                             1st May 2021

Final manuscripts due:             1st June 2021

Anticipated publication:          November 2021


This special issue aims at promoting cutting-edge research for establishing
video object segmentation and detection frameworks based on the advanced
machine learning technologies and offers a timely collection of works to
benefit researchers and practitioners. We welcome high-quality original
submissions addressing both novel theoretical and practical aspects related
to this topic.

Topics of interests include, but are not limited to:

-       Video object segmentation/detection based on graph convolutional

-       Video object segmentation/detection based on capsule networks

-       Video object segmentation/detection based on deep reinforcement

-       Video object segmentation/detection based on generative adversarial

-       Weakly supervised video object segmentation/detection

-       Semi-supervised video object segmentation/detection

-       Zero/few-shot video object segmentation/detection

-       Unsupervised video object segmentation/detection

-       Active learning and cross-domain learning frameworks for video
object segmentation/detection

-       Self-taught learning-based frameworks for video object

-       Saliency detection and its applications in video object

-       Representation learning for video object segmentation/detection

-       Tracking and other video understanding systems based on video
object segmentation/detection


Dingwen Zhang, Xidian University

Hamid Rezatofighi, Monash University

Junwei Han, Northwestern Polytechnical University

Nicu Sebe, University of Trento

Dingwen Zhang
Xidian University
Carnegie Mellon University
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