Dynamic Feature-Memory Transformer Network for RGBT Tracking-Reference-Cited by-同舟云学术

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Dynamic Feature-Memory Transformer Network for RGBT Tracking

Published:2023-09-01 Issue:17 Volume:23 Page:19692-19703
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Li Muyang¹^ORCID,Zhang Ping¹^ORCID,Yan Miao¹,Chen Hu²^ORCID,Wu Changke³

Affiliation:

1. School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

2. School of Computer Science, Sichuan University, Chengdu, China

3. National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10235271/10188610.pdf?arnumber=10188610

Reference65 articles.

1. Challenge-Aware RGBT Tracking

2. Dynamic Fusion Network for RGBT Tracking

3. RGBT Tracking via Multi-Adapter Network with Hierarchical Divergence Loss

4. Differential Reinforcement and Global Collaboration Network for RGBT Tracking

5. Deep Adaptive Fusion Network for High Performance RGBT Tracking

Cited by 8 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. RGBT tracking: A comprehensive review;Information Fusion;2024-10

2. AMNet: Learning to Align Multi-Modality for RGB-T Tracking;IEEE Transactions on Circuits and Systems for Video Technology;2024-08

3. A review on infrared and visible image fusion algorithms based on neural networks;Journal of Visual Communication and Image Representation;2024-05

4. Visible–Infrared Dual-Sensor Tracking Based on Transformer via Progressive Feature Enhancement and Fusion;IEEE Sensors Journal;2024-05-01

5. A Comprehensive Review of RGBT Tracking;IEEE Transactions on Instrumentation and Measurement;2024

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