[visionlist] CfP: IEEE T-CSVT SI on "Advanced Machine Learning Methodologies for Large-Scale Video Object Segmentation and Detection"
zhangdingwen2006yyy at gmail.com
Wed Sep 30 23:46:06 -04 2020
One-month deadline for the upcoming special issue on IEEE T-CSVT.
More detailed dates and scope of the special issue are listed below.
Looking forward to your contribution!
*IMPORTANT DATES: *
Manuscript submission: 1st November 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
- 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
Dingwen Zhang, Xidian University
Hamid Rezatofighi, Monash University
Junwei Han, Northwestern Polytechnical University
Nicu Sebe, University of Trento
Carnegie Mellon University
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