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Deep Attentive Tracking via Reciprocative Learning [article]

Shi Pu, Yibing Song, Chao Ma, Honggang Zhang, Ming-Hsuan Yang
2018 arXiv   pre-print
In this paper, we propose a reciprocative learning algorithm to exploit visual attention for training deep classifiers.  ...  The deep classifier learns to attend to the regions of target objects robust to appearance changes.  ...  Our deep classifier with reciprocative learning in itself can attend to temporal robust features to improve the tracking accuracy.  ... 
arXiv:1810.03851v2 fatcat:li7ehndhfzg3xihzizj6gd3o2u

Learning Target-oriented Dual Attention for Robust RGB-T Tracking [article]

Rui Yang, Yabin Zhu, Xiao Wang, Chenglong Li, Jin Tang
2019 arXiv   pre-print
Specifically, the local attention is implemented by exploiting the common visual attention of RGB and thermal data to train deep classifiers.  ...  In this paper, we propose two visual attention mechanisms for robust RGB-T object tracking.  ...  Based on Eq. 4, we can conduct the reciprocative learning [8] via standard backward propagation and chain rule.  ... 
arXiv:1908.04441v1 fatcat:qx22gd4yjnfpfhbit72x65667y

K-reciprocal Harmonious Attention Network for Video-based Person Re-identification

Xinxing Su, Xiaoye Qu, Zhikang Zou, Pan Zhou, Wei Wei, Shiping Wen, Menglan Hu
2019 IEEE Access  
In this paper, we propose a k-reciprocal harmonious attention network (KHAN) to jointly learn discriminative spatiotemporal features and the similarity metrics.  ...  INDEX TERMS Video-based person re-identification, attention, k-reciprocal, temporal information.  ...  Most of deep learning based methods perform much better than traditional methods, mainly because the hierarchical convolution layers in deep models can extract features more robustly.  ... 
doi:10.1109/access.2019.2898269 fatcat:mxo4e4xbbbcipn7ytqqg32rtpm

An Analytical Review on Some Recent Advances in Deep Learning Object Tracking Approaches

K. Nani Kumar, M. James Stephen, P. V. G. D. Prasad Reddy, Andhra University
2020 International Journal of Engineering Research and  
This paper presents a detailed review on some of the recent advances in Deep Learning Based Visual Object Tracking Approaches from a wide variety of algorithms often cited in research literature.  ...  Visual Object tracking in real world, real time application scenarios is a complex problem, therefore, it remains a most active area of research in computer vision.  ...  Song., 2018] [10] Described and implemented a reciprocate learning algorithm to exploit visual attention within the tracking by detection framework.  ... 
doi:10.17577/ijertv9is010309 fatcat:e7wny2gl35cuvfxrcfec3zxn7y

Table of Contents

2022 IEEE Transactions on Systems, Man & Cybernetics. Systems  
Meng 2174 Attention-Based Sequence-to-Sequence Learning for Online Structural Response Forecasting Under Seismic Excitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Luo 2348 A Reset Algorithm Solving Coordination With Antagonistic Reciprocity . . . . . . . . . . . . . . . . . . . . . . . . Y. Zhang, Y. Liu, X. Yang, and J.  ... 
doi:10.1109/tsmc.2022.3155205 fatcat:s755bh7nlnagxl5sqydsywkj5e

On Machine Learning and Structure for Mobile Robots [article]

Markus Wulfmeier
2018 arXiv   pre-print
Nonetheless, learning gains relevance in these modules when data collection and curation become easier than manual rule design.  ...  Due to recent advances - compute, data, models - the role of learning in autonomous systems has expanded significantly, rendering new applications possible for the first time.  ...  and in particular deep learning.  ... 
arXiv:1806.06003v1 fatcat:mkekvhmkibdxhdjii4jzu7nkyu

2020 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 31

2020 IEEE Transactions on Neural Networks and Learning Systems  
., +, TNNLS Jan. 2020 246-258 Pay Attention to Them: Deep Reinforcement Learning-Based Cascade Object Detection.  ...  Liao, L., +, TNNLS Nov. 2020 4600-4609 Pay Attention to Them: Deep Reinforcement Learning-Based Cascade Object Detection.  ... 
doi:10.1109/tnnls.2020.3045307 fatcat:34qoykdtarewhdscxqj5jvovqy

The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection [article]

Zhikang Zou, Xiaoqing Ye, Liang Du, Xianhui Cheng, Xiao Tan, Li Zhang, Jianfeng Feng, Xiangyang Xue, Errui Ding
2021 arXiv   pre-print
features; (ii) the Dynamic Intra-Trading module (DIT) that adaptively realigns the training processes of various sub-tasks via a self-learning manner.  ...  In this paper, we dig into the 3D object detection task and reformulate it as the sub-tasks of object localization and appearance perception, which benefits to a deep excavation of reciprocal information  ...  other via reciprocal feature reflecting.  ... 
arXiv:2112.14023v1 fatcat:ma2zg5vb5rb4tjrd5ds6yrvybe

Hierarchical Active Tracking Control for UAVs via Deep Reinforcement Learning

Wenlong Zhao, Zhijun Meng, Kaipeng Wang, Jiahui Zhang, Shaoze Lu
2021 Applied Sciences  
Instead, we unify the perception and decision-making stages using a high-level controller and then leverage deep reinforcement learning to learn the mapping from raw images to the high-level action commands  ...  Perception methods based on deep neural networks are powerful but require considerable effort for manual ground truth labeling.  ...  More augmentation techniques should be introduced to achieve stable tracking for various dynamic targets in complex environments and mitigate the sim-to-real problem.  ... 
doi:10.3390/app112210595 fatcat:4yzj47nofzbrho72tlvrwn7qgm

Robust Tracking against Adversarial Attacks [article]

Shuai Jia, Chao Ma, Yibing Song, Xiaokang Yang
2020 arXiv   pre-print
We apply the proposed adversarial attack and defense approaches to state-of-the-art deep tracking algorithms.  ...  While deep convolutional neural networks (CNNs) are vulnerable to adversarial attacks, considerably few efforts have been paid to construct robust deep tracking algorithms against adversarial attacks.  ...  The representative deep tracking-by-detection methods include multidomain learning [27, 13] , ensemble learning [10] , adversarial learning [32] , reciprocative learning [28] and overlap maximization  ... 
arXiv:2007.09919v2 fatcat:rd6ctzhk5fejvcvgcgssekuxry

Non-rigid Object Tracking via Deep Multi-scale Spatial-temporal Discriminative Saliency Maps [article]

Pingping Zhang, Wei Liu, Dong Wang, Yinjie Lei, Hongyu Wang, Chunhua Shen, Huchuan Lu
2019 arXiv   pre-print
Subsequently, local saliency maps are fused via a weighted entropy method, resulting in a final discriminative saliency map.  ...  object tracking datasets.  ...  Index Terms-Deep learning, non-rigid object tracking, saliency detection, spatial-temporal consistency. I.  ... 
arXiv:1802.07957v2 fatcat:zy3rtafdefghplbjp6tmzvetma

Learning to Socially Navigate in Pedestrian-rich Environments with Interaction Capacity [article]

Quecheng Qiu, Shunyi Yao, Jing Wang, Jun Ma, Guangda Chen, Jianmin Ji
2022 arXiv   pre-print
navigation policy via multiple reinforcement learning algorithms.  ...  ., the beep action, in the context of reinforcement learning. Different from these methods, we intend to train the policy via both supervised learning and reinforcement learning.  ...  Interaction via sounds has been considered as an efficient way to attract the attention of pedestrians. Nishimura et al.  ... 
arXiv:2203.16154v1 fatcat:ubstgfb4efh5xh5nc5sqch6k64

2020 Index IEEE Transactions on Cybernetics Vol. 50

2020 IEEE Transactions on Cybernetics  
., +, TCYB May 2020 2002-2013 Visual Object Tracking by Hierarchical Attention Siamese Network.  ...  Li, L., +, TCYB May 2020 2097-2109 Visual Object Tracking by Hierarchical Attention Siamese Network.  ... 
doi:10.1109/tcyb.2020.3047216 fatcat:5giw32c2u5h23fu4drupnh644a

Audio-based Musical Version Identification: Elements and Challenges [article]

Furkan Yesiler, Guillaume Doras, Rachel M. Bittner, Christopher J. Tralie, Joan Serrà
2021 arXiv   pre-print
Although this trend positively influences the number of researchers and institutions working on VI, it may also result in obscuring the literature before the deep learning era.  ...  Recent years, however, have witnessed the rise of deep learning-based approaches that take a step toward bridging the accuracy-scalability gap, yielding systems that can realistically be deployed in industrial  ...  A popular attention technique in the machine learning community is self-attention, popularized by the Transformer architecture.  ... 
arXiv:2109.02472v1 fatcat:tbbd66yq2vcz3ahc4z5ymethgi

Robust Visual Tracking via Statistical Positive Sample Generation and Gradient Aware Learning

Lijian Lin, Haosheng Chen, Yanjie Liang, Yan Yan, Hanzi Wang
2019 Proceedings of the ACM Multimedia Asia on ZZZ  
In this paper, we propose a robust tracking method via Statistical Positive sample generation and Gradient Aware learning (SPGA) to address the above two limitations.  ...  DAT [18] proposes a reciprocative learning algorithm to exploit visual attention within the tracking framework.  ...  In addition to integrating deep learning features into the framework of correlation filters, some works tend to design deep neural networks for tracking.  ... 
doi:10.1145/3338533.3366556 dblp:conf/mmasia/LinCL0W19 fatcat:wdtzvxczx5cfbmwrjxqvekkoju
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