TF-SOD: a novel transformer framework for salient object detection-Reference-Cited by-同舟云学术

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TF-SOD: a novel transformer framework for salient object detection

Published:2022-03-14 Issue:14 Volume:34 Page:11789-11806
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Wang Zhenyu,Zhang Yunzhou,Liu Yan,Wang Zhuo,Coleman Sonya,Kerr Dermot

Funder

National Natural Science Foundation of China

Open Research Projects of Zhejiang Lab

national natural science foundation of china

Major Science and Technology Projects of Liaoning Province

Fundation of Key Laboratory of Aerospace System Simulation

Fundation of Key Laboratory of Equipment Reliability

fundamental research funds for the central universities

china postdoctoral science foundation

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-022-07069-9.pdf

Reference76 articles.

1. Achanta R, Hemami SS, Estrada FJ, Susstrunk S (2009) Frequency-tuned salient region detection. In: IEEE conference on computer vision and pattern recognition (CVPR), pp. 1597–1604 https://doi.org/10.1109/CVPR.2009.5206596

2. Achanta R, Süsstrunk S (2010) Saliency detection using maximum symmetric surround. In: IEEE international conference on image processing, pp. 2653–2656. https://doi.org/10.1109/ICIP.2010.5652636

3. Baek D, Kang H, Ryoo J (2020) Sali360: design and implementation of saliency based video compression for 360$$^\circ$$ video streaming. In: Proceedings of the 11th ACM multimedia systems conference (MMSYS), pp. 141–152 https://doi.org/10.1145/3339825.3391866

4. Cane T, Ferryman J (2016) Saliency-based detection for maritime object tracking. In: IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp. 1257–1264 https://doi.org/10.1109/CVPRW.2016.159

5. Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European conference on computer vision (ECCV), pp. 213–229 https://doi.org/10.1007/978-3-030-58452-8_13

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