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 active learning cycle on VOC07+12 with 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%.
“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]
Section 4.2 says VOC07+12 starts with 1,000 labeled images and adds 1,000 per cycle, so a 7,000-image cycle would have 6,000—not 5,000—actively selected images. The VOC07+12 results table gives 0.7352 mAP for the authors’ method, 0.6927 for random sampling, and 0.7149 for LLAL at 7,000 images; those differences are 4.25 and 2.03 percentage points, not 9.1 and more than 2.8. The Comparison to SOTA section also identifies 10,000 images, rather than 7,000, as the last iteration. (LLM, conf=0.90)
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Experimental — accuracy not yet measuredOn MS-COCO first AL cycle, our method outperforms the random baseline by 5.8%, the best-performing AL method by 2.7%, the semi-supervised method by 5.4%, and the ensembles by 5%.
“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]
Table 5 (MS-COCO comparison; page unavailable) reports this paper’s active-learning benchmark, including 29.28% mAP for Oursgmm at 6,000 labeled images. The citation is attached to the comparison with that method, but this source does not report the citing authors’ result, so it cannot establish their claimed 2.7-point margin. (LLM, conf=0.84)
Experimental — accuracy not yet measuredOn MS-COCO last active learning cycle, our method outperforms the semi-supervised method, multi-model methods, and the best AL method by 1.6%.
“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]
On MS-COCO second cycle, our approach outperforms all other methods, including PM, by almost 4% or more.
“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]
Compared to state-of-the-art methods, our approach yields up to 7.7% relative mAP improvement for PASCAL-VOC and up to 7% for MS-COCO.
“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]
The random sampling strategy is mostly used for comparison purposes, based on the principle that AL-based methods have the potential to perform better.
“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]
Classifiers used for uncertainty estimation ignore the location information.
“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]
White methods, based on multi-models, have a high computing cost, while black methods may not need many parameters.
“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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BackgroundMethodInfluentialStream-Based Active Distillation for Scalable Model Deployment
Manjah, D., Cacciarelli, D., Benkedadra, M., Standaert, B., de Hertaing, G. R., Macq, B. M. M., Galland, S., & Vleeschouwer, C. D. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00528 · Cited by 14 · see citing works
“Query strategies based on localization tightness and stability [21], mixture density networks [12], and a unified box regression and classification metric [39] have also been proposed.”
MethodActive Pointly-Supervised Instance Segmentation
Tang, C., Xie, L., Zhang, G., Zhang, X., Tian, Q. C., & Hu, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19815-1_35 · Cited by 13 · see citing works
Optical Flow Training Under Limited Label Budget via Active Learning
Yuan, S., Sun, X., Kim, H. H., Yu, S., & Tomasi, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20047-2_24 · Cited by 12 · see citing works
Heterogeneous Diversity Driven Active Learning for Multi-Object Tracking
Li, R., Zhang, B., Liu, J., Liu, W., Zhao, J. C., & Teng, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00911 · Cited by 12 · see citing works
“[1, 8, 39, 20], object detection [45, 44, 10], semantic segmentation [7, 28, 33], and so on.”
BackgroundBayesian Active Learning for Sim-to-Real Robotic Perception
Feng, J., Lee, J., Durner, M., & Triebel, R. (2021) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros47612.2022.9982175 · Cited by 16 · see citing works
“Meanwhile, uncertainty based approaches [19], [25], [31] are also able to achieve competitive performances in the field of object detection.”
BackgroundPlausible Uncertainties for Human Pose Regression
Bramlage, L., Karg, M. E., & Curio, C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01389 · Cited by 11 · see citing works
Active Domain Adaptation with False Negative Prediction for Object Detection
Nakamura, Y., Ishii, Y., & Yamashita, T. (2024) · 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/cvpr52733.2024.02719 · Cited by 11 · see citing works
“Active Learning (AL) for Object Detection [5, 10, 22, 24, 26, 30, 42, 45, 46, 53, 56, 58] methods aim to select effective samples for training within a limited labeling budget.”
BackgroundMethodEfficient Active Learning Strategies for Monocular 3D Object Detection
Hekimoglu, A., Schmidt, M., Marcos-Ramiro, A., & Rigoll, G. (2022) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv51971.2022.9827454 · Cited by 11 · see citing works
One Step Closer to Unbiased Aleatoric Uncertainty Estimation
Wang, Z., Martin, Z., Das, S., Weng, T.-W., Megretski, A., Daniel, L., & Nguyen, L. M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i15.29627 · Cited by 11 · see citing works
Nani, P., Das, S., & Dey, S. (2025) · Evolutionary Intelligence · doi:10.1007/s12065-025-01061-7 · Cited by 10 · see citing works
“However, probabilistic models [28] indicate these discrepancies are generally minor, offering useful hints for identification.”
Im, Y.-J., Chang, T., Park, S., & Kim, H. (2025) · Waste Management · doi:10.1016/j.wasman.2025.01.028 · Cited by 9 · see citing works
Active Object Detection with Epistemic Uncertainty and Hierarchical Information Aggregation
Park, Y.-H., Kim, S., Choi, W., Han, D.-J., & Moon, J. J. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00306 · Cited by 9 · see citing works
Development of robust detector using the weather deep generative model for outdoor monitoring system
Jin, K., Kang, K.-S., Shin, B.-K., Kwon, J., Jang, S., Kim, Y., & Ryu, H.-G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120984 · Cited by 9 · see citing works
Introspective Perception for Long-Term Aerial Telemanipulation With Virtual Reality
Lee, J., Balachandran, R., Kondak, K., Coelho, A., Stefano, M. D., Humt, M., Feng, J., Asfour, T., & Triebel, R. (2024) · IEEE Transactions on Field Robotics · doi:10.55417/fr.2023010 · Cited by 8 · see citing works
“Defining the combination function comb ( · ) as a weighted sum or max operation (Choi et al., 2021), and also the aggregation function agg ( · ) as a sum or average operation (Roy et al., 2018), it is chosen;:”
UTrack: Multi-object Tracking with Uncertain Detections
Solano‐Carrillo, E., Sattler, F., Alex, A., Klein, A., Costa, B. P., Rodríguez, Á. B., & Stoppe, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91585-7_14 · Cited by 11 · see citing works
Pixel Is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection
Wu, Z., Wang, L., Wang, W., Xia, Q., Chen, C., Hao, A., & Li, S. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25390 · Cited by 8 · see citing works
Probabilistic Model-Based Active Learning with Attention Mechanism for Fish Species Recognition
Nabi, M. M., Shah, C., Alaba, S. Y., Prior, J. H., Campbell, M. D., Wallace, F., Moorhead, R. J., II, & Ball, J. E. (2023) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.23919/oceans52994.2023.10337403 · Cited by 8 · see citing works
“The model depends on the Gaussian Mixture Model (GMM) to estimate the probability distribution [12], [13].”
MethodInfluentialMulti-Task Consistency for Active Learning
Hekimoglu, A., Friedrich, P., Zimmer, W., Schmidt, M., Marcos-Ramiro, A., & Knoll, A. C. (2023) · 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw60793.2023.00366 · Cited by 17 · see citing works
“AL methods for object detection measure uncertainty of a detected box through either classification or localization uncertainty [8], [16], [21]–[24].”
MethodVLMine: Long-Tail Data Mining with Vision Language Models
Ye, M., Meyer, G. P., Zhang, Z., Park, D., Mustikovela, S. K., Chai, Y., & Wolff, E. M. (2025) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw65960.2025.00120 · Cited by 7 · see citing works
“For that reason, the long-tail signal provided by our approach tends to be complementary to the signals obtained from traditional model-based mining algorithms (4; 8; 27).”
Semi-Automated Framework for Digitalizing Multi-Product Warehouses with Large Scale Camera Arrays
Higashiura, K., Yokoyama, K., Asai, Y., Shimosato, H., Kano, K., Katayama, S., Urano, K., Yonezawa, T., & Kawaguchi, N. (2024) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom59722.2024.10494498 · Cited by 7 · see citing works
Employing feature mixture for active learning of object detection
Zhang, L., Lam, S.-K., Luo, D., & Wu, X. H. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127883 · Cited by 6 · see citing works
Wu, Z.-Y., Wang, W., Wang, L., Li, Y., Lv, F.-M., Xia, Q., Chen, C., Hao, A.-M., & Li, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3476683 · Cited by 7 · see citing works
Ada-iD: Active Domain Adaptation for Intrusion Detection
Han, F., Ye, P., Duan, S., & Wang, L. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681272 · Cited by 6 · see citing works
“Currently, ADA achieves encouraging performance in multiple multimedia tasks, e.g. , image classification [36], semantic segmentation [23, 29, 37, 40], object detection [1, 5, 15, 25, 28, 41], image captioning [43], refractive error detection [8] and autonomous driving [12].”
Active Supervised Cross-Modal Retrieval
Zhang, H., Yang, Y., Qi, F., Qian, S., & Xu, C. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3550526 · Cited by 6 · see citing works
“The predictive model is obtained after the iteration stops. classification [17], [18], [19], [20], [21], object detection [22], [23],[24],semanticsegmentation[25],[26],[27],[28],andother non-visualareas[29],[30],[31],[32],[33],[34],[35],[36].”
Assessing domain gap for continual domain adaptation in object detection
Doan, A.-D., Nguyen, B. L., Gupta, S., Reid, I. D., Wagner, M., & Chin, T.-J. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103885 · Cited by 6 · see citing works
MD3D: Mixture-Density-Based 3D Object Detection in Point Clouds
Choi, J., Song, Y., Kim, Y., Yoo, J., & Kwak, N. (2022) · IEEE Access · doi:10.1109/access.2022.3210108 · Cited by 5 · see citing works
“The MDN was also utilized to model the multi-modal nature of object detection and human pose estimation [35], and to address active learning for object detection [36].”
MethodSelf-citationWang, Y., Yu, Z., Liu, S., Zhou, Z., & Guo, B. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3580815 · Cited by 5 · see citing works
“Here we utilize an estimation function [10] to predict the annotation difficulty of H-CNN.”
MethodSemi-Path: An interactive semi-supervised learning framework for gigapixel pathology image analysis
Lai, Z., Chauhan, J., Chen, D., Dugger, B. N., Cheung, S.-C. S., & Chuah, C. (2024) · Smart Health · doi:10.1016/j.smhl.2024.100474 · Cited by 5 · see citing works
ALStereo: Active learning for stereo matching
Zhang, J., Li, J., Gu, M., Yu, X., Zheng, J. G., Bai, X. P., & Hancock, E. R. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111480 · Cited by 4 · see citing works
Efficient Remote Sensing in Agriculture via Active Learning and Opt-HRDNet
Chen, D., Xiao, S., Xi, M., Dai, L., & Yang, J. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3369189 · Cited by 4 · see citing works
“There is no doubt that remote sensing detection relying on large-scale datasets has made great progress in recent years [25], [26].”
BackgroundPrediction Accuracy & Reliability: Classification and Object Localization Under Distribution Shift
Diet, F., Sbeyti, M. K., & Karg, M. E. (2024) · Studies in big data · doi:10.1007/978-3-031-66842-5_9 · Cited by 4 · see citing works
“Within the field of perception for autonomous driving, uncertainty quantification has been applied to 1) classification tasks such as semantic segmentation [1], 2) regression tasks such as depth [7, 8] and optical flow estimation [6], and 3) combined tasks such as object detection [2, 3, 9, 10].”
BackgroundMethodUncertainty-driven active developmental learning
Hu, Q., Ji, L., Wang, Y., Zhao, S., & Lin, Z. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110384 · Cited by 4 · see citing works
Hierarchical Active Learning for Low-Altitude Drone-View Object Detection
Hu, H., Han, T., Wang, Y., Zhong, W., Yue, J., & Zan, P. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02228-y · Cited by 4 · see citing works
“Choi et al. (2021) employs mixture density networks to estimate both aleatoric and epistemic uncertainty in a single forward pass.”
Kano, K., Mori, Y., Higashiura, K., Hossain, T., Katayama, S., Urano, K., Yonezawa, T., & Kawaguchi, N. (2024) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3675094.3678447 · Cited by 4 · see citing works
“Choi et al. used Mixture Density Network (MDN) for object detection to estimate aleatoric and epistemic uncertainty on localization and classification in a single forward pass [3].”
Optimizing Object Detection via Metric-driven Training Data Selection
Zhou, C., Guo, Y., Lv, Q., & Ji, Y. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00730 · Cited by 4 · see citing works
“Parallel to domain adaptation, active machine learning has emerged as a paradigm to optimize the training process by selectively querying the most informative data points [2, 16].”
Joint Semi-Supervised and Active Learning via 3D Consistency for 3D Object Detection
Hwang, S., Kim, S., Kim, Y., & Kum, D. (2023) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra48891.2023.10160433 · Cited by 7 · see citing works
“The uncertainty-based approaches [10]−[16], on the other hand, focus on selecting samples with the highest uncertainty in the model prediction by measuring the entropy [34], [11] or probability of class [10].”
BackgroundClassification Committee for Active Deep Object Detection
Zhao, L., Li, B., Jiang, J.-X., & Wei, X. (2023) · IEEE transactions on multimedia · doi:10.1109/tmm.2024.3521778 · Cited by 4 · see citing works
“The proposed method is consistent with mainstream active learning meth-ods [38], [50], [51].”
InfluentialDeep active learning: A reality check
Gashi, E., Deng, J., & Elezi, I. (2025) · Pattern Recognition Letters · doi:10.1016/j.patrec.2025.07.017 · Cited by 3 · see citing works
Self-citationMIME: Minority Inclusion for Majority Group Enhancement of AI Performance
Chari, P., Ba, Y., Athreya, S., & Kadambi, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19778-9_19 · Cited by 3 · see citing works
Uncertainty Quantification in Table Structure Recognition
Ajayi, K., Zhang, L., He, Y., & Wu, J. (2024) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri62200.2024.00014 · Cited by 3 · see citing works
“We also compare our method against an active learning model proposed by Choi et al. [22].”
ResultLiu, P., Huang, B., Jin, T., & Long, H. (2025) · Remote Sensing · doi:10.3390/rs17071155 · Cited by 3 · see citing works
Yang, J., Wu, J., Xiao, S., Wen, J., Meng, Q., Lu, W. B., & Gao, X. (2025) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2025.3584808 · Cited by 3 · see citing works
“Additionally, considering the prevalent adoption of dual-branch architectures (consisting of classification and regression modules) in detection frameworks [26], achieving information alignment between these complementary branches continues to pose substantial technical challenges.”
Wang, J., Chen, L. F., & Wang, X. (2023) · Wireless networks · doi:10.1007/978-3-031-32397-3_15 · Cited by 3 · see citing works
Select Informative Samples for Night-Time Vehicle Detection Benchmark in Urban Scenes
Wang, X., Tu, X., Al-Hassani, B., Lin, C., & Xu, X. (2023) · Remote Sensing · doi:10.3390/rs15174310 · Cited by 3 · see citing works
Schmidt, S., Stappen, L., Schwinn, L., & Günnemann, S. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3444670 · Cited by 7 · see citing works
“Latter research directions employ Gaussian mixture models to asses uncertainty [13], [14].”
MethodIntrospective Perception for Long-Term Aerial Telemanipulation With Virtual Reality
Lee, J., Balachandran, R., Kondak, K., Coelho, A., de Stefano, M., Humt, M., Feng, J., Asfour, T., & Triebel, R. (2024) · IEEE Transactions on Field Robotics · doi:10.1109/tfr.2024.3494719 · Cited by 3 · see citing works
“Recent works can also be found on active learning for DNN based object detectors [54], [55], where the focus is on adaptations of active learning to existing object detection frameworks.”
Inconsistency-Based Active Learning for LiDAR Object Detection
Rivera, E., Stratil, L., & Lienkamp, M. (2025) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv64158.2025.11097777 · Cited by 2 · see citing works
“For example, Choi[14] proposes using mixture density networks to estimate a probabilistic distribution for each localization and classification head’s output.”
Rarity-Aware Stratified Active Learning for Class-Imbalanced Industrial Object Detection
Benhafid, Z., & Selouani, S. (2026) · Applied Sciences · doi:10.3390/app16031236 · Cited by 2 · see citing works
“Probabilistic modeling approaches explicitly estimate uncertainty for bounding box regression and class prediction within a single model [32].”
Active learning for industrial defect detection: a study on hybrid sampling strategies
Gonzalez, D. G., Nascimento, R. C., Rocha, C. D., Silva, M. F., Filipe, V. M. D. J., Rocha, L. F., Magalhães, L., & Cunha, A. M. T. D. S. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-17378-7 · Cited by 2 · see citing works
Feature Augmentation Based on Pixel-Wise Attention for Rail Defect Detection
Li, H., Li, H., Hou, Z., Song, H., Liu, J., & Dai, P. (2022) · Applied Sciences · doi:10.3390/app12168006 · Cited by 2 · see citing works
“As a comparison, AL-MDN and DyHead also outperform traditional one-stage and two-stage baselines, and are competitive with our proposed method.”
MethodResultVideo logo detection by Deep-Transfer Active Learning
Su, H., Qiu, G., & Guangdong Key Lab for Intelligent Information Processing, C. (2022) · Discrete and Continuous Dynamical Systems - S · doi:10.3934/dcdss.2022181 · Cited by 2 · see citing works
Na, J., & De-Silva, V. (2023) · 2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET) · doi:10.1109/globconet56651.2023.10149954
“However, [19], [20] has shown that the data informativeness for the localization component is also crucial to the performance of an active learning algorithm.”
BackgroundMethodInconsistency-based active learning with adaptive pseudo-labeling for fish species identification
Nabi, M. M., Shah, C., Alaba, S. Y., Caillouet, R., Prior, J. H., Campbell, M. D., Wallace, F., Ball, J. E., & Moorhead, R. J., II (2024) · SPIE Ocean Sensing and Monitoring · doi:10.1117/12.3013336 · Cited by 2 · see citing works
“32 In Table 1, we conduct a total of five cycles, with cycle 0 representing the initial supervised learning model, starting with 3,000 samples.”
MethodObject Recognition Consistency in Regression for Active Detection
Jing, M., Ou, Z., Wang, H.-X., Li, J., & Zhao, Z. (2024) · Machine Vision and Applications · doi:10.1007/s00138-024-01604-5 · Cited by 3 · see citing works
“The second type of methods attempts to calculate the localization uncertainty of the objects, such as AL-MDN [21].”
InfluentialTSceneJAL: Joint Active Learning of Traffic Scenes for 3D Object Detection
Lei, C., Peng, W.-Y., Zhou, G., Zhang, M.-Y., Hao, Q., Ji, C., & Xu, C.-Z. (2024) · IEEE transactions on intelligent transportation systems (Print) · doi:10.1109/tits.2025.3553170 · Cited by 2 · see citing works
“While several metrics, like statistical complexity [20], [21] and perception uncertainty [10], [22], [23], have been proposed to gauge scene complexity.”
HiCAL: Hierarchical Consistency-Based Active Learning for Drone-View Object Detection
Zhang, X., Liu, Y., Li, A., Han, K., Zhao, Q., & Wu, J. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3576980 · Cited by 3 · see citing works
“Common uncertainty measurement methods include Monte Carlo uncertainty sampling [12], entropy [13], similarity sampling [14], and some other training-embedded approaches [39], [40], [41].”
Shin, J. W., & Kim, H. (2023) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic57457.2023.10049902 · Cited by 2 · see citing works
“However, the performance of each task is highly dependent on the training data, and the use of limited data hinders the performance improvement of vision tasks [5].”
BackgroundSiamese Network Representation for Active Learning
Li, J., Du, Y., & Du, L. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222798 · Cited by 2 · see citing works
“Active learning can be categorized in uncertainty-based methods[2, 3], query-by-committee methods[4, 5, 6], representation-based methods[7, 8, 9, 10], etc.”
BackgroundHierarchical Uncertainty Aggregation and Emphasis Loss for Active Learning in Object Detection
Nguyen, T. T., Nguyen, K. D., Nguyen, T., Nguyen, T. C., Nguyen, A., & Kim, K. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386534 · Cited by 2 · see citing works
“Diverging from these approaches, active learning [18], [19], [20], [21], [22] emerges as a promising solution.”
BackgroundBucketized Active Sampling for learning ACOPF
Klamkin, M., Tanneau, M., Mak, T. W. K., & Hentenryck, P. V. (2024) · Electric Power Systems Research · doi:10.1016/j.epsr.2024.110697 · Cited by 2 · see citing works
“Acquisition functions α (or scoring functions [23]) are commonly used in active sampling applications to select the best k samples from D for the next training cycle.”
BackgroundWang, M., Liu, G., & Liu, H. (2024) · IEEE Access · doi:10.1109/access.2024.3372595 · Cited by 1 · see citing works
“Despite the success of these object detection methods [8], [29], certain limitations persist.”
BackgroundMethodInfluentialMurel, J., & Smith, D. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70543-4_18 · Cited by 1 · see citing works
Hierarchical evidence aggregation in two dimensions for active water surface object detection
Zhong, W., Hu, H., Wang, Y., Li, L., Han, T., Li, C., & Zan, P. (2024) · The Visual Computer · doi:10.1007/s00371-024-03685-9 · Cited by 1 · see citing works
“From different perspectives, AL can be broadly categorized into diversity-based [21, 22], uncertainty-based [5, 23–26] and hybrid-based [27–29].”
SSE: Multimodal Semantic Data Selection and Enrichment for Industrial-scale Data Assimilation
Shen, M., Chang, N., Liu, S., & Álvarez, J. M. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709417 · Cited by 1 · see citing works
“Crucially, we quantitatively show that the now semantically expanded dataset is able to improve rare class performance with even fewer rare but semantically important objects, demonstrating the strong advantage semantics imbue.”
Kun, 唐. 昆. T., Zhao, 黄. H., Yongjian, 朱. Z., Hang, 张. Z., Gang, 曾. Z., Hongchao, 肖. X., Xiaojie, 周. Z., Weidong, 唐. T., Mingjun, 张. Z., & Cong, 毛. M. (2025) · Chinese Journal of Lasers · doi:10.3788/cjl241344 · Cited by 1 · see citing works
Enhanced uncertainty sampling with category information for improved active learning
Wang, X., Zhang, B., Wang, F., Bao, T., Lu, Z., & Bao, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0327694 · Cited by 1 · see citing works
“In [18], a deep active learning method designed for object detection is proposed.”
Table Detection with Active Learning
Gautam, S., Purohit, N., & Harit, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04624-6_8 · Cited by 1 · see citing works
“Although AL has been successfully applied to various object detection tasks [28], [11], [18], [13], [4], [40], [41], [5], [39], its application to particularly table detection remains unexplored.”
Efficient Iterative Semi-supervised Object Detection Training for Counting of Fruits and Vegetables
Bogomasov, K., & Conrad, S. (2023) · International Conference on Advances in Artificial Intelligence (ICAAI) · doi:10.1145/3633598.3633619 · Cited by 1 · see citing works
“On this basis, a couple of AL-publications for OD have been introduced [6], [7], [9], [5], [15].”
BackgroundEnhancing Pseudo-Labeling Performance in Object Detection Using Gaussian Mixture Modeled Uncertainty
Lee, S., & Kim, H. (2024) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic61013.2024.10457103 · Cited by 1 · see citing works
“GMM [9], [10] represents the network’s output y for an input image x with parameters consisting of mean( µ ), variance( Σ ), and weights( π ) as follows: where k is the number of model components used in GMM, and the extracted parameters are used to redefine the loss function.”
BackgroundMethodActive Batch Sampling for Multi-label Classification with Binary User Feedback
Goswami, D., & Chakraborty, S. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00252 · Cited by 1 · see citing works
“Batch mode AL has been successfully used in a variety of computer vision applications, such as image recognition [37], semantic segmentation [1], object detection [3] and image regression [19] among others.”
MethodActive Learning Method Based on Pseudo-labeling
Hou, X., & Wang, C. (2024) · International Conference on Robotics, Control and Automation · doi:10.1109/icrca60878.2024.10649264 · Cited by 2 · see citing works
“Choi et al. [13] proposed to use a mixed density network to estimate the probability distribution of the output of each positioning head and classification head, fully evaluate the arbitrary uncertainty and cognitive uncertainty, and eliminate the interference of noise.”
BackgroundOSA-PD: Open-Set Active Learning Exploiting Predictive Discrepancy
Chen, Z., Han, P., Jiang, F., Si, J., & Xiong, L. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3537398 · Cited by 1 · see citing works
“For instance, Entropy [13] utilizes information entropy to assess uncertainty, a concept further generalized in subsequent studies such as [14], [15].”
Enhancing weakly supervised 3D medical image segmentation through probabilistic-aware learning
Jiang, R., Fan, Z., Wu, J., Zhu, L., Huang, X., Wang, T., Huang, H., & Xu, M. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113973 · Cited by 1 · see citing works
“Label Generation”
Semi-Automatic Video Frame Annotation for Construction Equipment Automation Using Scale-Models
Borngrund, C., Hammarkvist, T., Bodin, U., & Sandin, F. (2021) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon48115.2021.9589255 · Cited by 1 · see citing works
“Active learning has also been used to improve the convergence of object detectors [38], [39], with promising results.”
BackgroundSource-Free Unsupervised Domain Adaptation in Imbalanced Datasets
Sun, W., Chen, Q., Wang, C., Ye, D., & Chen, C. (2022) · International Conference on Data Science and Information Technology (DSIT) · doi:10.1109/dsit55514.2022.9943839 · Cited by 1 · see citing works
“It is worth noting that although entropy can be used in active learning [3, 15, 16] to measure the uncertainty of the sample [22], it differs greatly from SFUDA and DEB.”
BackgroundMulti-granularity active learning based on the three-way decision
Wu, X., & Thitipong, T. (2023) · International Journal of Advances in Intelligent Informatics · doi:10.26555/ijain.v9i2.1036
“Active learning can reduce labeling costs by selecting the most informative samples of the dataset [14], [15].”
BackgroundQuery-based Hard-Image Retrieval for Object Detection at Test Time
Ayers, E. W., Sadeghi, J., Redford, J., Mueller, R., & Dokania, P. (2022) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26717 · Cited by 3 · see citing works
“…objective of these works was to either perform active learning mainly to reduce the labelling and training cost (Gal, Is-lam, and Ghahramani 2017; Choi et al. 2021; Roy, Unmesh, and Namboodiri 2018; Aghdam et al. 2019; Haussmann et al. 2020; Yu et al. 2022; Feng et al. 2019), or to improve the…”
BackgroundEpistemic uncertainty estimation for object detection in open-set conditions
Miller, D. (2021) · Queensland University of Technology · doi:10.5204/thesis.eprints.213588 · Cited by 1 · see citing works
- 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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