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AFAT: Adaptive Failure-Aware Tracker for Robust Visual Object Tracking
[article]
2020
arXiv
pre-print
Preserved Fulltext
In addition, we further provide an Adaptive Failure-Aware Tracker (AFAT) by combing the state-of-the-art Siamese trackers with our system. ...
Siamese approaches have achieved promising performance in visual object tracking recently. ...
Adaptive failure-aware tracker We propose the Adaptive Failure-Aware Tracker (AFAT) based on the Siame-seRPN++ tracker [26] and the proposed QPN in Section 3. ...
arXiv:2005.13708v1
fatcat:zo55gjj5krcdth6gbbagdpksbq
RFN-Nest: An end-to-end residual fusion network for infrared and visible images
[article]
2021
pre-print
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The most difficult part of the design is to choose an appropriate strategy to generate the fused image for a specific task in hand. ...
To address this problem, a novel end-to-end fusion network architecture (RFN-Nest) is developed for infrared and visible image fusion. ...
In AFAT, a failure-aware system, realized by a Quality Prediction Network (QPN), based on convolutional and LSTM modules was proposed and obtained better tracking performance in many datasets. ...
doi:10.1016/j.inffus.2021.02.023
arXiv:2103.04286v1
fatcat:akla65qmunfrnpkbxglsbenq2m
Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
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Finally, we discuss the challenges and give deep thinking of promising directions for future research. ...
Although there have been some works to summarize the different approaches, it still remains challenging for researchers to have an in-depth view of how these approaches work. ...
: 0.95), AP M for medium objects, AP L for large objects, AR 0.5 , AR 0.75 , AR, AR M for medium objects, AR L for large objects. ...
arXiv:2104.11536v1
fatcat:tdag2jq2vjdrjekwukm5nu7l6a