Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep Learning-Reference-Cited by-同舟云学术

Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep Learning

Published:2021-11-01 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE 24th International Conference on Information Fusion (FUSION)
language:
Short-container-title:

Author:

Pinto Juliano,Hess Georg,Ljungbergh William,Xia Yuxuan,Svensson Lennart,Wymeersch Henk

Publisher

IEEE

Link

Reference29 articles.

1. Attention is all you need;vaswani;Advances in neural information processing systems,2017

2. Advances in statistical multisource-multitarget information fusion;mahler;Artech House,2014

3. Layer normalization;ba;Neural Information Processing Systems Deep Learning Symposium,2016

5. The evolved transformer;so;ICML,2019

Cited by 11 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

4. Multi-Target Tracking with Transferable Convolutional Neural Networks;2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP);2023-12-10

5. Deep Learning for Model-Based Multiobject Tracking;IEEE Transactions on Aerospace and Electronic Systems;2023-12