Learning meta-adversarial features via multi-stage adaptation network for robust visual object tracking-Reference-Cited by-同舟云学术

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Learning meta-adversarial features via multi-stage adaptation network for robust visual object tracking

Published:2022-06 Issue: Volume:491 Page:365-381
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Chen Si,Wang Libo,Wang Zhen,Yan Yan,Wang Da-Han,Zhu Shunzhi

Funder

Fujian Provincial Natural Science Foundation

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference59 articles.

1. Fully-convolutional siamese networks for object tracking;Bertinetto,2016

2. High performance visual tracking with siamese region proposal network;Li,2018

3. Distractor-aware siamese networks for visual object tracking;Zhu,2018

4. Deeper and wider siamese networks for real-time visual tracking;Zhang,2019

5. Siamrpn++: Evolution of siamese visual tracking with very deep networks;Li,2019

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. GCAT: graph calibration attention transformer for robust object tracking;Neural Computing and Applications;2024-05-06

2. Sparse mixed attention aggregation network for multimodal images fusion tracking;Engineering Applications of Artificial Intelligence;2024-01

3. Graph Attention Transformer Network for Robust Visual Tracking;Communications in Computer and Information Science;2023

4. Online Continual Learning with Contrastive Vision Transformer;Lecture Notes in Computer Science;2022

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