Adversarial Active Learning Method for Enhanced Object Detection Guided by Uncertainty

Shaiya Wang; Hongzhong Tang; Haifan Luo; Shang Gao; Yuebing Xu

doi:10.3788/LOP240632

Laser & Optoelectronics Progress, Volume. 61, Issue 22, 2237002(2024)

Adversarial Active Learning Method for Enhanced Object Detection Guided by Uncertainty

Shaiya Wang¹, Hongzhong Tang^1,2、*, Haifan Luo¹, Shang Gao¹, and Yuebing Xu³

Author Affiliations

¹College of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, Hunan , China

²The Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, Hunan , China

³College of Physics and Electronic Engineering, Hengyang Normal University, Hengyang 421002, Hunan , China

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References(34)

[1] Li X, He M, Luo H B. Occluded pedestrian detection algorithm based on improved YOLOv3[J]. Acta Optica Sinica, 42, 1415003(2022).

[2] Xu Z C, Xue J P, Sun P F et al. Robot grasp detection method based on stable lightweight network[J]. Chinese Journal of Lasers, 50, 1304003(2023).

[3] Zhang Y, Zhu G Y, Shi T J et al. Small object detection in remote sensing images based on feature fusion and attention[J]. Acta Optica Sinica, 42, 2415001(2022).

[4] Chen B B, Chen W J, Yang S C et al. Label matching semi-supervised object detection[C], 14361-14370(2022).

[5] Chen B H, Li P Y, Chen X et al. Dense learning based semi-supervised object detection[C], 4805-4814(2022).

[6] Zhou H Y, Ge Z, Liu S T et al. Dense teacher: dense pseudo-labels for semi-supervised object detection[M]. Computer vision-ECCV 2022, 13669, 35-50(2022).

[7] Xu M H, Wang H, Ni B B et al. Cross-domain detection via graph-induced prototype alignment[C], 12352-12361(2020).

[8] Li W Y, Liu X Y, Yuan Y X. SIGMA: semantic-complete graph matching for domain adaptive object detection[C], 5281-5290(2022).

[9] Yan P X, Wu Z Y, Liu M M et al. Unsupervised domain adaptive salient object detection through uncertainty-aware pseudo-label learning[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 3000-3008(2022).

[10] Li G, Zhao L, Gu J W. An active learning algorithm for image classification based on difficulty and competence[J]. IEEE Access, 11, 60398-60406(2023).

[11] Sener O, Savarese S. Active learning for convolutional neural networks: a core-set approach[EB/OL]. https://arxiv.org/abs/1708.00489

[12] Zhang B C, Li L, Yang S J et al. State-relabeling adversarial active learning[C], 8753-8762(2020).

[13] Kim K, Park D, Kim K I et al. Task-aware variational adversarial active learning[C], 8162-8171(2021).

[14] Haussmann E, Fenzi M, Chitta K et al. Scalable active learning for object detection[C], 1430-1435(2020).

[15] Aghdam H H, Gonzalez-Garcia A, Lopez A et al. Active learning for deep detection neural networks[C], 3671-3679(2019).

[16] Roy S, Unmesh A, Namboodiri V P. Deep active learning for object detection[C], 91(2018).

[17] Kao C C, Lee T Y, Sen P et al. Localization-aware active learning for object detection[M]. Computer vision-ACCV 2018, 11366, 506-522(2019).

[18] Choi J, Elezi I, Lee H J et al. Active learning for deep object detection via probabilistic modeling[C], 10244-10253(2021).

[19] Yoo D, Kweon I S. Learning loss for active learning[C], 93-102(2019).

[20] Sinha S, Ebrahimi S, Darrell T. Variational adversarial active learning[C], 5971-5980(2019).

[21] Agarwal S, Arora H, Anand S et al. Contextual diversity for active learning[M]. Computer vision-ECCV 2020, 12361, 137-153(2020).

[22] Yu W P, Zhu S J, Yang T J N et al. Consistency-based active learning for object detection[C], 3950-3959(2022).

[23] Yang C, Huang L C, Crowley E J. Plug and play active learning for object detection[EB/OL]. https://arxiv.org/abs/2211.11612

[24] Wu J X, Chen J X, Huang D. Entropy-based active learning for object detection with progressive diversity constraint[C], 9387-9396(2022).

[25] Beluch W H, Genewein T, Nürnberger A et al. The power of ensembles for active learning in image classification[C], 9368-9377(2018).

[26] Zhu J J, Bento J. Generative adversarial active learning[EB/OL]. http://arxiv.org/abs/1702.07956v5

[27] Lin T Y, Maire M, Belongie S et al. Microsoft COCO: common objects in context[M]. Computer vision-ECCV 2014, 8693, 740-755(2014).

[28] Everingham M, Van Gool L, Williams C K I et al. The pascal visual object classes (VOC) challenge[J]. International Journal of Computer Vision, 88, 303-338(2010).

[29] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. https://arxiv.org/abs/1409.1556

[30] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. Computer vision-ECCV 2016, 9905, 21-37(2016).

[31] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[32] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[C], 2999-3007(2017).

[33] Girshick R. Fast R-CNN[C], 1440-1448(2015).

[34] Chen P, YanL Q, DangY J et al. Redundancy guided masked feature reconstruction for few-shot action recognition[J/OL]. Journal of Chinese Computer Systems, 1-9. https://kns.cnki.net/kcms/detail/21.1106.TP.20230809. 1156.008.html

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Shaiya Wang, Hongzhong Tang, Haifan Luo, Shang Gao, Yuebing Xu. Adversarial Active Learning Method for Enhanced Object Detection Guided by Uncertainty[J]. Laser & Optoelectronics Progress, 2024, 61(22): 2237002

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Paper Information

Category: Digital Image Processing

Received: Jan. 29, 2024

Accepted: Mar. 12, 2024

Published Online: Nov. 19, 2024

The Author Email: Hongzhong Tang (HongzhongTang@xtu.edu.cn)

DOI:10.3788/LOP240632

CSTR:32186.14.LOP240632

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology