The DOI matches its Crossref record; 47 of the 49 references it cites were found in a registry or an index.
Active Learning for Deep Object Detection via Probabilistic Modeling
Open accessAbstract
Active learning aims to reduce labeling costs by selecting only the most informative samples on a dataset. Few existing works have addressed active learning for object detection. Most of these methods are based on multiple models or are straightforward extensions of classification methods, hence estimate an image’s informativeness using only the classification head. In this paper, we propose a novel deep active learning approach for object detection. Our approach relies on mixture density networks that estimate a probabilistic distribution for each localization and classification head’s output. We explicitly estimate the aleatoric and epistemic uncertainty in a single forward pass of a single model. Our method uses a scoring function that aggregates these two types of uncertainties for both heads to obtain every image’s informativeness score. We demonstrate the efficacy of our approach in PASCAL VOC and MS-COCO datasets. Our approach outperforms single-model based methods and performs on par with multi-model based methods at a fraction of the computing cost. Code is available at https://github.com/NVlabs/AL-MDN.
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“In the last one, where we use 7 , 000 samples, 5 , 000 of which are actively labeled, our method outperforms the random baseline by 9 . 1% and the best existing active learning methods by more than 2 . 8% [5].”
From Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection (2021) · cites this paper as [5]
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“For MS-COCO, in Fig. 4a-b, we observe that in the first active learning cycle, our method outperforms the random baseline by 5 . 8% , the best-performing AL method by 2 . 7% [5], the semi-supervised method by 5 . 4% , and the ensembles by 5% .”
From Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection (2021) · cites this paper as [5]
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“We observe that this difference is maintained in the other cycles, including the last active learning cycle where our method outperforms the semisupervised method, multi-model methods [9, 12] and the best AL method [5] by 1 . 6% .”
From Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection (2021) · cites this paper as [5]
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“In the second cycle, our approach outperforms all the other methods, including PM [5], by almost 4% or more.”
From Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection (2021) · cites this paper as [5]
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“Compared to state-of-the-art methods [4-8], our approach yields up to a 7 . 7% and 7% relative mAP improvement for PASCAL-VOC and MS-COCO, respectively.”
From Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection (2021) · cites this paper as [5]
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“The random sampling strategy is mostly used for comparison purposes, grounded on to the principle that AL-based methods have the potential to perform better (e.g., [5,48,52,60]).”
From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [48]
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“The classifiers used for uncertainty estimation ignore the location information [48].”
From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [48]
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“White methods, based on multi-models (like classifier assemblies which increase the number of parameters [48]), have a high computing cost, while black methods may not need many parameters.”
From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [48]
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Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · cited 1×
“We compare the performance of SSD using different backbones such as Resnet-34 and Resnet-50 [17], and Faster-RCNN [32] detector trained using our actively sampled dataset.”
MethodVery Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148 · cited 1×
“Experimental settings: We employ Single Shot MultiBox Detector (SSD) [31], which is widely used in active learning studies [33, 40], with a VGG-16 backbone [37].”
MethodFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
A Mathematical Theory of Communication
Shannon, C. E. (1948) · The MIT Press eBooks · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 50,387
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
(2016) · Cited by 31,590
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939 · cited 3×
“21 Faster-RCNN [32] Resnet-50-FPN [29] 72.”
MethodInfluentialGirshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · cited 1×
“2, and obtain the class probability distribution for the k-th mixture by using the reparameterization trick [25] of applying Gaussian noise and variance Σp to μ k p [24]:”
MethodThe pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599 · cited 2×
“We demonstrate the benefits of our approach on PASCAL VOC [9] and MS-COCO [30] in a single-stage architecture such as SSD [31], and show generalized performance in a two-stage architecture such as Faster-RCNN [32].”
Method
Research works printed but not linked: 4
- 10.5555/3295222.3295309 · DOI
- 10.48550/arXiv.1405.0312 · DOI · Paper page
- 10.1007/978-3-319-46448-0_2 · DOI · Paper page
- 10.1145/584091.584093 · DOI
Cited by
Sorted by citations
Tiny object detection with context enhancement and feature purification
Xiao, J., Guo, H., Zhou, J. Y., Zhao, T., Yu, Q., Chen, Y., & Wang, Z. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118665 · Cited by 153
A comprehensive survey on deep active learning in medical image analysis
Wang, H., Jin, Q., Li, S., Liu, S., Wang, M., & Song, Z. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103201 · Cited by 157
Wang, K., Shen, C., Li, X., & Lǚ, J. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3532803 · Cited by 77
“There are inherently many unknown classes in the unknown world, but it can be reduced by active learning [62], [63] and other measures.”
Semantic Segmentation with Active Semi-Supervised Learning
Rangnekar, A., Kanan, C., & Hoffman, M. J. (2022) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00591 · Cited by 44
“Query-computing methods are further classified into applying Bayesian inference [37,28,45,24,48,76,32], ensemble learning [6,68,33,63,18], diversity learning [72,85,21,77,22,53,11] and representation learning [55,41,1,42,12,19,29,30,84,87,75,62,23].”
BackgroundMethodUnbiased Scene Graph Generation in Videos
Nag, S., Min, K., Tripathi, S., & Roy–Chowdhury, A. K. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.02184 · Cited by 45
“The class-specific aleatoric and epistemic uncertainty, for the sample z i are computed as follows: Therefore, by using a GMM head, we are modeling the inherent uncertainty associated with the data from a Bayesian perspective [8, 9, 28].”
BackgroundMethod
Metadata
- Authors
- Choi, J., Elezi, I., Lee, H.-J., Farabet, C., & Alvarez, J. M.
- Affiliations
- Seoul National University (KR)
- Ca' Foscari University of Venice (IT)
- NVIDIA
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 10244-10253
- OpenAlex
- W3145121851
- Cited by
- 135
- References
- 49
- Citations by year
- 2026: 15 · 2025: 27 · 2024: 36 · 2023: 36 · 2022: 17 · 2021: 4
- FWCI
- 10.74
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- active learning (machine learning), object detection, mixture density networks, uncertainty estimation, aleatoric uncertainty, epistemic uncertainty, annotation cost reduction
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