The Power of Ensembles for Active Learning in Image Classification

Deep Residual Learning for Image Recognition

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ImageNet classification with deep convolutional neural networks

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A Mathematical Theory of Communication

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Densely Connected Convolutional Networks

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ImageNet Large Scale Visual Recognition Challenge

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Rethinking the Inception Architecture for Computer Vision

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Learning Multiple Layers of Features from Tiny Images

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A survey on deep learning in medical image analysis

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Ensemble methods in machine learning

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PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference

Okada, M., Kosaka, N., & Taniguchi, T. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340873 · Cited by 37

An Informative Path Planning Framework for Active Learning in UAV-Based Semantic Mapping

Rückin, J., Magistri, F., Stachniss, C., & Popović, M. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3313811 · Cited by 39

Training Data Subset Search With Ensemble Active Learning

Chitta, K., Álvarez, J. M., Haußmann, E., & Farabet, C. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3133268 · Cited by 45

Improving Semi-Supervised Domain Adaptation Using Effective Target Selection and Semantics

Singh, A. K., Doraiswamy, N., Takamuku, S., Bhalerao, M., Dutta, T., Biswas, S., Chepuri, A., Vengatesan, B., & Natori, N. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00305 · Cited by 33

Bio-inspired Active Learning method in spiking neural network

Zhan, Q., Liu, G., Xie, X., Zhang, M., & Sun, G. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110193 · Cited by 32

Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm

Xie, Y., Lü, H., Yan, J., Yang, X., Tomizuka, M., & Zhan, W. (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/cvpr52729.2023.02271 · Cited by 32

Knowledge-Aware Federated Active Learning with Non-IID Data

Cao, Y.-T., Ye, S., Yu, B., Wang, J., & Tao, D. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02036 · Cited by 31

An Adaptive Query Approach for Extracting Medical Images for Disease Detection Applications

Migdady, A., Khamayseh, Y., AlZoubi, O., & Yassein, M. B. (2024) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-024-09152-w · Cited by 31

Federated Active Learning (F-AL): An Efficient Annotation Strategy for Federated Learning

Ahn, J.-H., Ma, Y., Park, S., & You, C. (2024) · IEEE Access · doi:10.1109/access.2024.3376746 · Cited by 30

How useful is active learning for image‐based plant phenotyping?

Nagasubramanian, K., Jubery, T., Ardakani, F. F., Mirnezami, S., Singh, A. K., Singh, A., Sarkar, S., & Ganapathysubramanian, B. (2020) · The Plant Phenome Journal · doi:10.1002/ppj2.20020 · Cited by 24

TAAL: Test-Time Augmentation for Active Learning in Medical Image Segmentation

Gaillochet, M., Desrosiers, C., & Lombaert, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17027-0_5 · Cited by 30

Advanced Active Learning Strategies for Object Detection

Schmidt, S., Rao, Q., Tatsch, J., & Knoll, A. (2020) · 2020 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/iv47402.2020.9304565 · Cited by 29

Deep indexed active learning for matching heterogeneous entity representations

Jain, A., Sarawagi, S., & Sen, P. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3485450.3485455 · Cited by 39

Large-Scale Visual Active Learning with Deep Probabilistic EnsemblesPreprint

Chitta, K., Álvarez, J. M., & Lesnikowski, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.03575 · Cited by 38

Box-Level Active Detection

Lyu, M., Zhou, J., Chen, H., Huang, Y., Yu, D., Li, Y., Guo, Y., Guo, Y., Xiang, L., & Ding, G. (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/cvpr52729.2023.02276 · Cited by 29

A Survey on Confidence Calibration of Deep Learning-Based Classification Models Under Class Imbalance Data

Dong, J., Jiang, Z., Pan, D., Chen, Z., Guan, Q., Zhang, H., Gui, G., & Gui, W. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3565159 · Cited by 38

Visual Sentiment Analysis With Active Learning

Chen, J., Mao, Q., & Xue, L. (2020) · IEEE Access · doi:10.1109/access.2020.3024948 · Cited by 28

Object Classification Based on Enhanced Evidence Theory: Radar–Vision Fusion Approach for Roadside Application

Liu, P., Yu, G., Wang, Z., Zhou, B., & Chen, P. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3154001 · Cited by 28

Exploring Diversity-Based Active Learning for 3D Object Detection in Autonomous Driving

Lin, J.-P., Liang, Z., Deng, S., Cai, L.-L., Jiang, T., Li, T.-R., Jia, K., & Xu, X. (2022) · IEEE transactions on intelligent transportation systems (Print) · doi:10.1109/tits.2024.3463801 · Cited by 37

Uncertainty Based Detection and Relabeling of Noisy Image LabelsPreprint

Köhler, J. M., Autenrieth, M., & Beluch, W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.11876 · Cited by 27

A novel anomaly detection approach based on ensemble semi-supervised active learning (ADESSA)

Niu, Z., Guo, W., Xue, J., Wang, Y., Kong, Z., & Huang, L. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103190 · Cited by 27

Parting with Illusions about Deep Active LearningPreprint

Mittal, S., Tatarchenko, M., Çiçek, Ö., & Brox, T. (2019) · arXiv.org · doi:10.48550/arxiv.1912.05361 · Cited by 75

Inductive State-Relabeling Adversarial Active Learning With Heuristic Clique Rescaling

Zhang, B., Li, L., Wang, S., Cai, S., Zha, Z.-J., Tian, Q., & Huang, Q. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3432099 · Cited by 29

Towards Robust Learning with Different Label Noise Distributions

Ortego, D., Arazo, E., Albert, P., O'Connor, N. E., & McGuinness, K. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412747 · Cited by 26

Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification

Liu, L., Lei, W., Wan, X., Liu, L., Luo, Y., & Feng, C. (2020) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai50040.2020.00191 · Cited by 26

Deep Bayesian Active Learning for Learning to Rank: A Case Study in Answer Selection

Wang, Q., Wu, W., Qi, Y., & Zhao, Y.-C. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3056894 · Cited by 23

Deep Active Cross-Modal Visuo-Tactile Transfer Learning for Robotic Object Recognition

Murali, P. K., Wang, C., Lee, D., Dahiya, R., & Kaboli, M. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3191408 · Cited by 26

High frequency root dynamics: sampling and interpretation using replicated robotic minirhizotrons

Nair, R. K. F., Strube, M., Hertel, M., Kolle, O., Rolo, V., & Migliavacca, M. (2022) · Journal of Experimental Botany · doi:10.1093/jxb/erac427 · Cited by 27

Deep Active Learning for Computer Vision: Past and Future

Takezoe, R., Liu, X., Mao, S., Chen, M. T., Feng, Z., Zhang, S., & Wang, X. (2023) · APSIPA Transactions on Signal and Information Processing · doi:10.1561/116.00000057 · Cited by 26

Advancing molecular machine learning representations with stereoelectronics-infused molecular graphs

Boiko, D. A., Reschützegger, T., Sánchez-Lengeling, B., Blau, S. M., & Gomes, G. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01031-9 · Cited by 31

Similarity Search for Efficient Active Learning and Search of Rare Concepts

Coleman, C., Chou, E., Katz-Samuels, J., Culatana, S. C., Bailis, P. D., Berg, A. C., Nowak, R. D., Sumbaly, R., Zaharia, M., & Yalnız, İ. Z. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i6.20591 · Cited by 25

A semi-supervised self-training method to develop assistive intelligence for segmenting multiclass bridge elements from inspection videos

Karim, M. M., Qin, R., Chen, G., & Yin, Z. (2021) · Structural Health Monitoring · doi:10.1177/14759217211010422 · Cited by 25

Machine learning for multiscale modeling in computational molecular design

Alshehri, A. S., & You, F. (2021) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2021.100752 · Cited by 27

Warm Start Active Learning with Proxy Labels and Selection via Semi-supervised Fine-Tuning

Nath, V., Yang, D., Roth, H. R., & Xu, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16452-1_29 · Cited by 25

MUS-CDB: Mixed Uncertainty Sampling With Class Distribution Balancing for Active Annotation in Aerial Object Detection

Liang, D., Zhang, J., Tang, Y.-P., & Huang, S.-J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3285443 · Cited by 28

Geometry in active learning for binary and multi-class image segmentation

Konyushkova, K., Sznitman, R., & Fua, P. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.01.007 · Cited by 24

Gone Fishing: Neural Active Learning with Fisher EmbeddingsPreprint

Ash, J. T., Goel, S., Krishnamurthy, A., & Kakade, S. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09675 · Cited by 25

An active learning model based on image similarity for skin lesion segmentation

Shu, X., Li, Z., Tian, C., Chang, X., & Yuan, D. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129690 · Cited by 26

LETS: A Label-Efficient Training Scheme for Aspect-Based Sentiment Analysis by Using a Pre-Trained Language Model

Shim, H., Lowet, D., Luca, S., & Vanrumste, B. (2021) · IEEE Access · doi:10.1109/access.2021.3101867 · Cited by 30

Active Learning Strategies for Weakly-Supervised Object Detection

Vo, H. V., Siméoni, O., Gidaris, S., Bursuc, A., Pérez, P., & Ponce, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20056-4_13 · Cited by 25

Active learning and novel model calibration measurements for automated visual inspection in manufacturing

Rožanec, J. M., Bizjak, L., Trajkova, E., Zajec, P., Keizer, J., Fortuna, B., & Mladenić, D. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02098-0 · Cited by 29

Predicting the Performance of Retail Market Firms: Regression and Machine Learning Methods

Vukovic, D. B., Spitsina, L., Gribanova, E. B., Spitsin, V., & Лызин, И. А. (2023) · Mathematics · doi:10.3390/math11081916 · Cited by 29

Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagery

Lee, H., & Li, W.-W. (2024) · Remote Sensing of Environment · doi:10.1016/j.rse.2024.114213 · Cited by 28

Deep Active Learning for Joint Classification & Segmentation with Weak Annotator

Belharbi, S., Ayed, I. B., McCaffrey, L. M., & Granger, É. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00338 · Cited by 31

DEAL: Deep Evidential Active Learning for Image Classification

Hemmer, P., Kühl, N., & Schöffer, J. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-3357-7_7 · Cited by 25

Nearest Neighbor Classifier Embedded Network for Active Learning

Wan, F., Yuan, T., Fu, M., Ji, X., Huang, Q., & Ye, Q. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i11.17205 · Cited by 23

Ensemble-based active learning using fuzzy-rough approach for cancer sample classification

Kumar, A., & Halder, A. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103591 · Cited by 23

Interactive Weak Supervision: Learning Useful Heuristics for Data LabelingPreprint

Boecking, B., Neiswanger, W., Xing, E. P., & Dubrawski, A. W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.06046 · Cited by 47

Layer Ensembles: A Single-Pass Uncertainty Estimation in Deep Learning for Segmentation

Kushibar, K., Campello, V. M., Garrucho, L., Linardos, A., Radeva, P. I., & Lekadir, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16452-1_49 · Cited by 28

A recurrent network based on active learning for the assessment of fish feeding status

Kong, Q., Du, R., Duan, Q., Zhang, Y., Chen, Y., Li, D., Xu, C., Li, W., & Liu, C. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.106979 · Cited by 22

AcneGrader: An ensemble pruning of the deep learning base models to grade acne

Liu, S., Fan, Y., Duan, M., Wang, Y., Su, G., Ren, Y., Huang, L., & Zhou, F. (2022) · Skin Research and Technology · doi:10.1111/srt.13166 · Cited by 22

Discrepancy-Based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images

Bai, F., Xing, X., Shen, Y., Ma, H., & Meng, M. Q. ‐. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16452-1_3 · Cited by 22

Sequential Image-Based Attention Network for Inferring Force Estimation Without Haptic Sensor

Shin, H., Cho, H., Kim, D., Ko, D.-K., Lim, S., & Hwang, W. (2019) · IEEE Access · doi:10.1109/access.2019.2947090 · Cited by 22

Diversity-Sensitive Generative Adversarial Network for Terrain Mapping Under Limited Human Intervention

Li, J., Chen, Z., Chen, J., & Lin, Q. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2962086 · Cited by 23

Multimodal Surprise Adequacy Analysis of Inputs for Natural Language Processing DNN Models

Kim, S., & Yoo, S. (2021) · International Conference/Workshop on Automation of Software Test · doi:10.1109/ast52587.2021.00017 · Cited by 21

Improving the Intra-class Long-Tail in 3D Detection via Rare Example Mining

Jiang, C. M., Najibi, M., Qi, C. R., Zhou, Y., & Anguelov, D. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20080-9_10 · Cited by 21

Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning

Rückin, J., Magistri, F., Stachniss, C., & Popović, M. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3359970 · Cited by 20

A Survey of Deep Active Learning for Foundation Models

Wan, T., Xu, K., Yu, T., Wang, X., Feng, D., Ding, B. Y., & Wang, H. (2023) · Intelligent Computing · doi:10.34133/icomputing.0058 · Cited by 26

TiDAL: Learning Training Dynamics for Active Learning

Kye, S. M., Choi, K., Byun, H., & Chang, B. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02041 · Cited by 23

Active Learning for Single-Stage Object Detection in UAV Images

Yamani, A. Z., Alyami, A., Luqman, H., Ghanem, B., & Giancola, S. (2024) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00187 · Cited by 20

Sequential Optimal Experimental Design of Perturbation Screens Guided by Multi-modal Priors

Huang, K., Lopez, R., Hütter, J.-C., Kudo, T., Rios, A., & Regev, A. (2024) · Lecture notes in computer science · doi:10.1007/978-1-0716-3989-4_2 · Cited by 24

Efficient Conditional Pre-training for Transfer Learning

Chakraborty, S., Uzkent, B., Ayush, K., Tanmay, K., Sheehan, E., & Ermon, S. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00469 · Cited by 24

Active Learning for Bayesian 3D Hand Pose Estimation

Caramalau, R., Bhattarai, B., & Kim, T. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00346 · Cited by 20

Discovering General-Purpose Active Learning StrategiesPreprint

Konyushkova, K., Sznitman, R., & Fua, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.04114 · Cited by 20

Realistic Evaluation of Deep Active Learning for Image Classification and Semantic Segmentation

Mittal, S., Niemeijer, J., Çiçek, Ö., Tatarchenko, M., Ehrhardt, J., Schäfer, J. P., Heinz Handels, A., & Brox, T. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02372-z · Cited by 22

Towards Exploring the Limitations of Active Learning: An Empirical Study

Hu, Q., Guo, Y., Cordy, M., Xie, X., Ma, W., Papadakis, M., & Traon, Y. L. (2021) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase51524.2021.9678672 · Cited by 20

Active deep learning on entity resolution by risk sampling

Nafa, Y., Chen, Q., Chen, Z., Lu, X., He, H., Duan, T., & Li, Z. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107729 · Cited by 20

A Secure Framework toward IoMT‐Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

Islam, M. K., Kaushal, C., Amin, M. A., Algarni, A. D., Alturki, N. M., Soliman, N. F., & Mansour, R. F. (2022) · Contrast Media & Molecular Imaging · doi:10.1155/2022/6805460 · Cited by 20

Effective Active Learning Method for Spiking Neural Networks

Xie, X., Yu, B., Liu, G., Zhan, Q., & Tang, H. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3257333 · Cited by 22

Multiclass autoencoder-based active learning for sensor-based human activity recognition

Park, H., Lee, G. H., Han, J., & Choi, J. K. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.09.029 · Cited by 22

Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning

Tsymbalov, E., Makarychev, S., Shapeev, A. V., & Panov, M. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/499 · Cited by 20

Learning to Rank for Active Learning: A Listwise Approach

Li, M., Liu, X.-L., van de Weijer, J., & Raducanu, B. (2020) · International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412680 · Cited by 24

CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation

Qiao, Y., Zhu, J., Long, C., Zhang, Z., Wang, Y., Du, Z., & Yang, X. S. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20107 · Cited by 18

Deep Active Learning Framework for Lymph Node Metastasis Prediction in Medical Support System

Zhuang, Q., Dai, Z., & Wu, J. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4601696 · Cited by 18

Self-supervised Assisted Active Learning for Skin Lesion Segmentation

Zhao, Z., Lü, W., Zeng, Z., Xu, K., Veeravalli, B., & Guan, C. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871734 · Cited by 19

Graph Node Based Interpretability Guided Sample Selection for Active Learning

Mahapatra, D., Poellinger, A., & Reyes, M. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3215017 · Cited by 18

From Known to Unknown

Ren, J., Jiang, L., Peng, H. M., Cao, Y., Wu, J. X., Yu, P. S., & He, L. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557329 · Cited by 18

The devil is in the details: a small-lesion sensitive weakly supervised learning framework for prostate cancer detection and grading

Yang, Z., Wang, X., Xiang, J., Zhang, J., Yang, S., Wang, X., Yang, W., Li, Z., Han, X., & Liu, Y. (2023) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-023-03502-z · Cited by 18

BDAL: Balanced Distribution Active Learning for MRI Cardiac Multistructures Segmentation

Shu, X., Yang, Y., Liu, J., Chang, X., & Wu, B. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3342442 · Cited by 18

Active Learning for Entity Alignment

Berrendorf, M., Faerman, E., & Tresp, V. (2020) · European Conference on Information Retrieval · doi:10.1007/978-3-030-72113-8_4 · Cited by 20

Multi-Modal Active Learning For Automatic Liver Fibrosis Diagnosis Based On Ultrasound Shear Wave Elastography

Gao, L., Zhou, R., Dong, C., Feng, C., Li, Z., Wan, X., & Liu, L. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434170 · Cited by 18

A Robust Computerized Adaptive Testing Approach in Educational Question Retrieval

Zhuang, Y., Liu, Q., Huang, Z., Li, Z., Jin, B., Bi, H., Chen, E., & Wang, S. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531928 · Cited by 18

Discriminative Active Learning for Robotic Grasping in Cluttered Scene

Wei, B., Ye, X., Long, C., Du, Z., Li, B., Yin, B., & Yang, X. S. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3243474 · Cited by 17

Multiple Instance Differentiation Learning for Active Object Detection

Wan, F., Ye, Q.-X., Yuan, T., Xu, S., Liu, J., Ji, X., & Huang, Q. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3277738 · Cited by 19

A Survey of Dataset Refinement for Problems in Computer Vision Datasets

Wan, Z., Wang, Z., Chung, C., & Wang, Z. (2023) · ACM Computing Surveys · doi:10.1145/3627157 · Cited by 20

Minority Class Oriented Active Learning for Imbalanced Datasets

Aggarwal, U., Popescu, A., & Hudelot, C. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412182 · Cited by 17

Effective active learning in digital pathology: A case study in tumor infiltrating lymphocytes

Meirelles, A. L., Kurc, T., Saltz, J., & Teodoro, G. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106828 · Cited by 17

Rethinking deep active learning: Using unlabeled data at model trainingPreprint

Siméoni, O., Budnik, M., Avrithis, Y., & Gravier, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.08177 · Cited by 22

When Active Learning Meets Implicit Semantic Data Augmentation

Chen, Z., Zhang, J., Wang, P., Chen, J., & Li, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19806-9_4 · Cited by 16

Active learning for left ventricle segmentation in echocardiography

Alajrami, E., Ng, T., Jevsikov, J., Naidoo, P., Fernandes, P., Azarmehr, N., Dinmohammadi, F., Shun-Shin, M. J., Serej, N. D., Francis, D. P., et al. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108111 · Cited by 16

Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning

Hekimoglu, A., Schmidt, M., & Marcos-Ramiro, A. (2023) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00234 · Cited by 22

Structural Semantic Adversarial Active Learning for Image Captioning

Zhang, B., Li, L., Su, L., Wang, S., Deng, J., Zha, Z.-J., & Huang, Q. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413885 · Cited by 16

Active learning to classify macromolecular structures in situ for less supervision in cryo-electron tomography

Du, X., Wang, H., Zhu, Z., Zeng, X., Chang, Y., Zhang, J., Xing, E. P., & Xu, M. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab123 · Cited by 16

Accelerating Neural Architecture Search via Proxy Data

Na, B., Mok, J., Choe, H., & Yoon, S. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/392 · Cited by 16

An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation

Gao, B., Zhao, X., & Zhao, H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3218403 · Cited by 17

Reducing Spatial Labeling Redundancy for Active Semi-Supervised Crowd Counting

Liu, Y., Ren, S., Chai, L., Wu, H., Xu, D., Qin, J., & He, S. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3232712 · Cited by 16

Minimax Active LearningPreprint

Ebrahimi, S., Gan, W., Chen, D., Biamby, G., Salahi, K., Laielli, M., Zhu, S., & Darrell, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.10467 · Cited by 30

Active Prompt Learning in Vision Language Models

Bang, J., Ahn, S., & Lee, J.-G. (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/cvpr52733.2024.02550 · Cited by 19

UniSAL: Unified Semi-supervised Active Learning for histopathological image classification

Zhong, L., Qian, K., Liao, X., Huang, Z., Liu, Y., Zhang, S., & Wang, G. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103542 · Cited by 16

Statistical Downscaling of SEVIRI Land Surface Temperature to WRF Near-Surface Air Temperature Using a Deep Learning Model

Afshari, A., Vogel, J., & Chockalingam, G. (2023) · Remote Sensing · doi:10.3390/rs15184447 · Cited by 15

Deep Ensemble Bayesian Active Learning : Addressing the Mode Collapse issue in Monte Carlo dropout via EnsemblesPreprint

Pop, R., & Fulop, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.03897 · Cited by 51

VisActive: Visual-concept-based Active Learning for Image Classification under Class Imbalance

Khaleel, M., Idris, A., Tavanapong, W., Pratt, J., Oh, J., & de Groen, P. C. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3617999 · Cited by 5

Similarity-Based Active Learning for Image Classification Under Class Imbalance

Zhang, C., Tavanapong, W., Kijkul, G., Wong, J., de Groen, P. C., & Oh, J. (2018) · Proceedings · doi:10.1109/icdm.2018.00196 · Cited by 32

Information Gain Sampling for Active Learning in Medical Image Classification

Mehta, R., Shui, C., Nichyporuk, B., & Arbel, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16749-2_13 · Cited by 4

Active Learning with Ensembles for Image Classification

(2008)