Advanced Machine Learning Methodologies for
Large-Scale Video Object Segmentation and Detection Call for Papers
CfP: IEEE T-CSVT SI on
Advanced Machine Learning Methodologies for
Large-Scale Video Object Segmentation and Detection
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
SCOPE:
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
networks
- Video object segmentation/detection based on capsule networks
- Video object segmentation/detection based on deep reinforcement
learning
- Video object segmentation/detection based on generative adversarial
learning
- 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
segmentation/detection
- Saliency detection and its applications in video object
segmentation/detection
- Representation learning for video object segmentation/detection
- Tracking and other video understanding systems based on video object
segmentation/detection
GUEST EDITORS:
Dingwen Zhang, Xidian University
Hamid Rezatofighi, University of Adelaide
Junwei Han, Northwestern Polytechnical University
Nicu Sebe, University of Trento