A Survey on Deep Learning Technique for Video Segmentation-Reference-Cited by-同舟云学术

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A Survey on Deep Learning Technique for Video Segmentation

Published:2023-06-01 Issue:6 Volume:45 Page:7099-7122
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Zhou Tianfei¹^ORCID,Porikli Fatih²^ORCID,Crandall David J.³^ORCID,Van Gool Luc¹^ORCID,Wang Wenguan⁴^ORCID

Affiliation:

1. ETH Zurich, Zürich, Switzerland

2. School of Computer Science, Australian National University, Canberra, ACT, Australia

3. Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

4. ReLER Lab, Australian Artificial Intelligence Institute, University of Technology Sydney, Ultimo, NSW, Australia

Funder

Australian Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

http://xplorestaging.ieee.org/ielx7/34/10120646/09966836.pdf?arnumber=9966836

Reference266 articles.

1. Discriminative Segment Annotation in Weakly Labeled Video

2. Feature Space Optimization for Semantic Video Segmentation

3. The Shape-Time Random Field for Semantic Video Labeling

4. Semantic object classes in video: A high-definition ground truth database

5. Visual tracking: An experimental survey;smeulders;IEEE Trans Pattern Anal Mach Intell,2014

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3. Self-supervised spatial–temporal feature enhancement for one-shot video object detection;Neurocomputing;2024-10

4. DBTSF-VSOD: a decision-based two-stage framework for video salient object detection;International Journal of Multimedia Information Retrieval;2024-09-03

5. Scalable Video Object Segmentation With Identification Mechanism;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-09

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