A Deeper Look at Dataset Bias

Adaptive batch normalization for practical domain adaptation

Li, Y., Wang, N., Shi, J., Hou, X., & Liu, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.03.005 · Cited by 601 · see citing works

Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes

Zhang, Y., David, P., & Gong, B. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.223 · Cited by 504 · see citing works

Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations

Wang, T., Zhao, J., Yatskar, M., Chang, K.-W., & Ordóñez, V. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00541 · Cited by 353 · see citing works

Computer vision applications in construction: Current state, opportunities & challenges

Paneru, S., & Jeelani, I. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103940 · Cited by 309 · see citing works

Attention-Driven Dynamic Graph Convolutional Network for Multi-label Image Recognition

Ye, J., He, J., Peng, X., Wu, W., & Qiao, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58589-1_39 · Cited by 244 · see citing works

REPAIR: Removing Representation Bias by Dataset Resampling

Li, Y., & Vasconcelos, N. M. (2019) · 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/cvpr.2019.00980 · Cited by 231 · see citing works

Explainability of Deep Vision-Based Autonomous Driving Systems: Review and Challenges

Zablocki, É., Ben-Younes, H., Pérez, P., & Cord, M. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01657-x · Cited by 227 · see citing works

Cascaded Deep Monocular 3D Human Pose Estimation With Evolutionary Training Data

Li, S., Ke, L., Pratama, K., Tai, Y., Tang, C., & Cheng, K.-T. T. (2020) · 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/cvpr42600.2020.00621 · Cited by 191 · see citing works

A Comprehensive Survey of Databases and Deep Learning Methods for Cybersecurity and Intrusion Detection Systems

Gumusbas, D., Yıldırım, T., Genovese, A., & Scotti, F. (2020) · IEEE Systems Journal · doi:10.1109/jsyst.2020.2992966 · Cited by 183 · see citing works

Analyzing and Reducing the Damage of Dataset Bias to Face Recognition With Synthetic Data

Kortylewski, A., Egger, B., Schneider, A., Gerig, T., Morel-Forster, A., & Vetter, T. R. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00279 · Cited by 156 · see citing works

Training confounder-free deep learning models for medical applications

Zhao, Q., Adeli, E., & Pohl, K. M. (2020) · Nature Communications · doi:10.1038/s41467-020-19784-9 · Cited by 150 · see citing works

CNN-based features for retrieval and classification of food images

Ciocca, G., Napoletano, P., & Schettini, R. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.09.001 · Cited by 146 · see citing works

A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes

Zhang, Y., David, P., Foroosh, H., & Gong, B. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2903401 · Cited by 142 · see citing works

SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data

Tang, C. I., Perez-Pozuelo, I., Spathis, D., Brage, S., Wareham, N., & Mascolo, C. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3448112 · Cited by 140 · see citing works

A survey on bias in visual datasets

Fabbrizzi, S., Papadopoulos, S., Ntoutsi, E., & Kompatsiaris, I. Y. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103552 · Cited by 142 · see citing works

Human and artificial cognition

Siemens, G., Marmolejo-Ramos, F., Gabriel, F., Medeiros, K., Marrone, R., Joksimovic, S., & de Laat, M. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100107 · Cited by 133 · see citing works

Learning and the Unknown: Surveying Steps toward Open World Recognition

Boult, T. E., Cruz, S. S., Dhamija, A. R., Günther, M., Henrydoss, J., & Scheirer, W. J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019801 · Cited by 130 · see citing works

Synthetic data as an enabler for machine learning applications in medicine

Rajotte, J.-F., Bergen, R. V., Buckeridge, D. L., Emam, K. E., Ng, R. T., & Strome, E. (2022) · iScience · doi:10.1016/j.isci.2022.105331 · Cited by 128 · see citing works

Face Recognition: Too Bias, or Not Too Bias?

Robinson, J. P., Livitz, G., Henon, Y., Qin, C., Fu, Y., & Timoner, S. J. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00008 · Cited by 125 · see citing works

Understanding and Mitigating Annotation Bias in Facial Expression Recognition

Chen, Y., & Joo, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01471 · Cited by 120 · see citing works

Diversity in Faces.Preprint

Merler, M., Ratha, N., Feris, R., & Smith, J. R. (2019) · arXiv (Cornell University) · Cited by 111 · see citing works

Recent Advances in Object Detection in the Age of Deep Convolutional Neural NetworksPreprint

Agarwal, S., du Terrail, J. O., & Jurie, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.03193 · Cited by 133 · see citing works

A semisupervised autoencoder-based approach for anomaly detection in high performance computing systems

Borghesi, A., Bartolini, A., Lombardi, M., Milano, M., & Luca Benini, C. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.07.008 · Cited by 104 · see citing works

Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods

Gao, B., Pan, Y., Li, C., Geng, S., & Zhao, H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3076844 · Cited by 105 · see citing works

Does Object Recognition Work for Everyone?Preprint

DeVries, T., Misra, I., Wang, C., & van der Maaten, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02659 · Cited by 132 · see citing works

Deep Meta-Learning: Learning to Learn in the Concept SpacePreprint

Zhou, F., Wu, B., & Li, Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.03596 · Cited by 95 · see citing works

Open Set Domain Adaptation for Image and Action Recognition

Busto, P. P., Iqbal, A., & Gall, J. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2880750 · Cited by 97 · see citing works

Domain-Specific Suppression for Adaptive Object Detection

Wang, Y., Zhang, R., Zhang, S., Li, M., Xia, Y., Zhang, X., & Liu, S. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00948 · Cited by 93 · see citing works

Representation Learning with Statistical Independence to Mitigate Bias

Adeli, E., Zhao, Q., Pfefferbaum, A., Sullivan, E. V., Fei-Fei, L., Niebles, J. C., & Pohl, K. M. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00256 · Cited by 93 · see citing works

Rehumanized Crowdsourcing

Barbosa, N. M., & Chen, M. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300773 · Cited by 90 · see citing works

Online Feature Transformation Learning for Cross-Domain Object Category Recognition

Zhang, X., Zhuang, Y., Wang, W., & Pedrycz, W. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2705113 · Cited by 90 · see citing works

EnD: Entangling and Disentangling deep representations for bias correction

Tartaglione, E., Barbano, C. A., & Grangetto, M. (2021) · 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/cvpr46437.2021.01330 · Cited by 85 · see citing works

Don’t Judge an Object by Its Context: Learning to Overcome Contextual Bias

Singh, K. K., Mahajan, D. K., Grauman, K. L., Lee, Y. J., Feiszli, M., & Ghadiyaram, D. (2020) · 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/cvpr42600.2020.01108 · Cited by 80 · see citing works

On the Cross-dataset Generalization in License Plate Recognition

Laroca, R., Cardoso, E., Lucio, D. R., Estevam, V., & Menotti, D. (2022) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0010846800003124 · Cited by 80 · see citing works

Deep learning–driven multi-omics analysis: enhancing cancer diagnostics and therapeutics

Zhang, J., Che, Y., Liu, R., Wang, Z., & Liu, W. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf440 · Cited by 88 · see citing works

Deep image prior for undersampling high-speed photoacoustic microscopy

Vu, T., DiSpirito, A., Li, D., Wang, Z., Zhu, X., Chen, M., Jiang, L., Zhang, D., Luo, J., Zhang, Y. S., et al. (2021) · Photoacoustics · doi:10.1016/j.pacs.2021.100266 · Cited by 74 · see citing works

A Prototype-Oriented Framework for Unsupervised Domain AdaptationPreprint

Tanwisuth, K., Fan, X., Zheng, H., Zhang, S., Zhang, H., Chen, B., & Zhou, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.12024 · Cited by 71 · see citing works

Deep learning in retinal optical coherence tomography (OCT): A comprehensive survey

Viedma, I. A., Alonso‐Caneiro, D., Read, S. A., & Collins, M. J. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.08.021 · Cited by 73 · see citing works

Artificial intelligence bias in medical system designs: a systematic review

Kumar, A., Aelgani, V., Vohra, R., Gupta, S. K., Bhagawati, M., Paul, S., Saba, L., Suri, N., Khanna, N. N., Laird, J. R., et al. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16029-x · Cited by 65 · see citing works

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

Eitel, F., Schulz, M.-A., Seiler, M., Walter, H., & Ritter, K. (2021) · Experimental Neurology · doi:10.1016/j.expneurol.2021.113608 · Cited by 68 · see citing works

Adversarial Learning and Self-Teaching Techniques for Domain Adaptation in Semantic Segmentation

Michieli, U., Biasetton, M., Agresti, G., & Zanuttigh, P. (2020) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2020.2980671 · Cited by 61 · see citing works

Deep Visual Domain Adaptation

Csurka, G. (2020) · International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) · doi:10.1109/synasc51798.2020.00013 · Cited by 66 · see citing works

Combining Crowd and Machine Intelligence to Detect False News on Social Media

Wei, X., Zhang, Z., Zhang, M., Chen, W., & Zeng, D. (2022) · MIS Q. · doi:10.25300/misq/2022/16526 · Cited by 50 · see citing works

Fair Generative Modeling via Weak SupervisionPreprint

Choi, K., Grover, A., Singh, T., Shu, R., & Ermon, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12008 · Cited by 54 · see citing works

Unsupervised Domain Adaptation for Semantic Segmentation of Urban Scenes

Biasetton, M., Michieli, U., Agresti, G., & Zanuttigh, P. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00160 · Cited by 51 · see citing works

A Survey on Open Set Recognition

Mahdavi, A., & Carvalho, M. (2021) · IEEE International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) · doi:10.1109/aike52691.2021.00013 · Cited by 52 · see citing works

Unsupervised Monocular Depth Estimation in Highly Complex Environments

Zhao, C., Tang, Y. S., & Sun, Q. (2022) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2022.3182360 · Cited by 52 · see citing works

AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breath

Dentamaro, V., Giglio, P., Impedovo, D., Moretti, L., & Pirlo, G. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108656 · Cited by 48 · see citing works

Automated Brain Masking of Fetal Functional MRI with Open Data

Rutherford, S. E., Sturmfels, P., Angstadt, M., Hect, J. L., Wiens, J., van den Heuvel, M. I., Scheinost, D., Sripada, C., & Thomason, M. E. (2021) · Neuroinformatics · doi:10.1007/s12021-021-09528-5 · Cited by 46 · see citing works

Closing the Gap of Simulation to Reality in Electromagnetic Imaging of Brain Strokes via Deep Neural Networks

Al-Saffar, A., Bialkowski, A., Baktashmotlagh, M., Trakic, A., Guo, L., & Abbosh, A. M. (2020) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2020.3041092 · Cited by 46 · see citing works

On responsible machine learning datasets emphasizing fairness, privacy and regulatory norms with examples in biometrics and healthcare

Mittal, S., Thakral, K., Singh, R., Mayank Vatsa, A., Glaser, T., Ferrer, C. C., & Hassner, T. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00874-y · Cited by 53 · see citing works

Artificial intelligence and deep learning algorithms for epigenetic sequence analysis: A review for epigeneticists and AI experts

Tahir, M., Norouzi, M., Khan, S. S., Davie, J., Yamanaka, S., & Ashraf, A. B. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109302 · Cited by 44 · see citing works

Are we done with object recognition? The iCub robot’s perspective

Pasquale, G., Ciliberto, C., Odone, F., Rosasco, L., & Natale, L. (2018) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2018.11.001 · Cited by 41 · see citing works

Investigating bias in deep face analysis: The KANFace dataset and empirical study

Georgopoulos, M., Panagakis, Y., & Pantic, M. (2020) · Image and Vision Computing · doi:10.1016/j.imavis.2020.103954 · Cited by 42 · see citing works

Multi-adversarial Faster-RCNN with Paradigm Teacher for Unrestricted Object Detection

He, Z., Zhang, L., Gao, X., & Zhang, D. D. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01728-z · Cited by 40 · see citing works

Challenges and Prospects in Vision and Language Research

Kafle, K., Shrestha, R., & Kanan, C. (2019) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2019.00028 · Cited by 39 · see citing works

Image‐based taxonomic classification of bulk insect biodiversity samples using deep learning and domain adaptation

Fujisawa, T., Noguerales, V., Meramveliotakis, Ε., Papadopoulou, A., & Vogler, A. P. (2023) · Systematic Entomology · doi:10.1111/syen.12583 · Cited by 34 · see citing works

Evaluation of the benchmark datasets for testing the efficacy of deep convolutional neural networks

Dhar, S., & Shamir, L. (2021) · Visual Informatics · doi:10.1016/j.visinf.2021.10.001 · Cited by 33 · see citing works

Deep Domain Adaptation With Differential Privacy

Wang, Q., Li, Z., Zou, Q., Zhao, L., & Wang, S. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2983254 · Cited by 33 · see citing works

Generalization of vision pre-trained models for histopathology

Sikaroudi, M., Hosseini, M., González, R., Rahnamayan, S., & Tizhoosh, H. R. (2023) · Scientific Reports · doi:10.1038/s41598-023-33348-z · Cited by 28 · see citing works

Autoencoder-based transfer learning in brain–computer interface for rehabilitation robot

Tan, C., Sun, F., Fang, B., Kong, T., & Zhang, W. (2019) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881419840860 · Cited by 27 · see citing works

OCT Retinal and Choroidal Layer Instance Segmentation Using Mask R-CNN

Viedma, I. A., Alonso‐Caneiro, D., Read, S. A., & Collins, M. J. (2022) · Sensors · doi:10.3390/s22052016 · Cited by 27 · see citing works

A Survey of Deep Learning Solutions for Multimedia Visual Content Analysis

Nadeem, M. S., Franqueira, V. N. L., Zhai, X., & Kurugöllü, F. (2019) · IEEE Access · doi:10.1109/access.2019.2924733 · Cited by 25 · see citing works

A First Look at Dataset Bias in License Plate Recognition

Laroca, R., dos Santos, M., Estevam, V., Luz, E. J. D. S., & Menotti, D. (2022) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi55357.2022.9991768 · Cited by 22 · see citing works

Federated Learning of Explainable Artificial Intelligence (FED-XAI): A Review

López-Blanco, R., Alonso, R. S., Arrieta, A. G., Chamoso, P., & Prieto, J. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-38333-5_32 · Cited by 22 · see citing works

Overinterpretation reveals image classification model pathologiesPreprint

Carter, B., Jain, S., Mueller, J. W., & Gifford, D. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.08907 · Cited by 25 · see citing works

Can language representation models think in bets?

Tang, Z., & Kejriwal, M. (2023) · Royal Society Open Science · doi:10.1098/rsos.221585 · Cited by 21 · see citing works

A noise audit of human-labeled benchmarks for machine commonsense reasoning

Kejriwal, M., Santos, H., Shen, K., Mulvehill, A. M., & McGuinness, D. L. (2024) · Scientific Reports · doi:10.1038/s41598-024-58937-4 · Cited by 21 · see citing works

Fair classification by loss balancing via fairness-aware batch sampling

Kim, D., Park, S., Hwang, S., & Byun, H. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.018 · Cited by 20 · see citing works

Are You Tampering with My Data?

Alberti, M., Pondenkandath, V., Würsch, M., Bouillon, M., Seuret, M., Ingold, R., & Liwicki, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11012-3_25 · Cited by 20 · see citing works

Next-generation surgical navigation: Marker-less multi-view 6DoF pose estimation of surgical instruments

Hein, J., Cavalcanti, N., Suter, D., Zingg, L., Carrillo, F., Calvet, L., Farshad, M., Navab, N., Pollefeys, M., & Fürnstahl, P. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103613 · Cited by 19 · see citing works

Improving fairness generalization through a sample-robust optimization method

Ferry, J., Aïvodji, U., Gambs, S., Huguet, M., & Siala, M. (2022) · Machine Learning · doi:10.1007/s10994-022-06191-y · Cited by 19 · see citing works

Do We Train on Test Data? The Impact of Near-Duplicates on License Plate Recognition

Laroca, R., Estevam, V., Britto, A. S., Minetto, R., & Menotti, D. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191584 · Cited by 19 · see citing works

Deep Space-Time Video Upsampling Networks

Kang, J., Jo, Y., Oh, S. W., Vajda, P., & Kim, S. J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58607-2_41 · Cited by 19 · see citing works

EAT-NAS: elastic architecture transfer for accelerating large-scale neural architecture search

Fang, J., Chen, Y., Zhang, X., Zhang, Q., Huang, C., Meng, G., Liu, W., & Wang, X. (2021) · Science China Information Sciences · doi:10.1007/s11432-020-3112-8 · Cited by 21 · see citing works

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 19 · see citing works

Detecting discriminatory risk through data annotation based on Bayesian inferences

Beretta, E., Vetrò, A., Lepri, B., & Martin, J. C. D. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445940 · Cited by 17 · see citing works

Combining Crowd and Machine Intelligence to Detect False News on Social MediaPreprint

Wei, X., Zhang, Z., Zhang, M., Chen, W., & Zeng, D. D. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3355763 · Cited by 17 · see citing works

Automatic coronavirus disease 2019 diagnosis based on chest radiography and deep learning – Success story or dataset bias?

Dhont, J., Wolfs, C. J. A., & Verhaegen, F. (2021) · Medical Physics · doi:10.1002/mp.15419 · Cited by 15 · see citing works

Tackling Dataset Bias With an Automated Collection of Real-World Samples

Sevetlidis, V., Pavlidis, G. P., Mouroutsos, S. G., & Gasteratos, A. C. (2022) · IEEE Access · doi:10.1109/access.2022.3226517 · Cited by 15 · see citing works

One Versus All for Deep Neural Network for Uncertainty (OVNNI) Quantification

Franchi, G., Bursuc, A., Aldea, E., Dubuisson, S., & Isabelle Bloch, A. (2021) · IEEE Access · doi:10.1109/access.2021.3138978 · Cited by 15 · see citing works

Towards Federated Learning for HVAC Analytics

Guo, Y., Wang, D., Vishwanath, A., Cheng, X., & Li, Q. L. (2020) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3396851.3397717 · Cited by 15 · see citing works

Attention-guided convolutional network for bias-mitigated and interpretable oral lesion classification

Patel, A., Besombes, C., Dillibabu, T., Sharma, M., Tamimi, F., Ducret, M., Chauvin, P. J., & Madathil, S. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-81724-0 · Cited by 14 · see citing works

Minimizing Vehicle Re-Identification Dataset Bias Using Effective Data Augmentation Method

Jamali, Z., Deng, J., Cai, J., Aftab, M. U., & Hussain, K. (2019) · International Conference on Semantics, Knowledge and Grid (SKG) · doi:10.1109/skg49510.2019.00030 · Cited by 14 · see citing works

Bridging the Gap of Lane Detection Performance Between Different Datasets: Unified Viewpoint Transformation

Wen, T., Yang, D., Jiang, K., Yu, C., Lin, J., Wijaya, B., & Jiao, X. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2989349 · Cited by 14 · see citing works

Real time fall detection using infrared cameras and reflective tapes under day/night luminance

Ramanujam, E., & Padmavathi, S. V. (2021) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-210605 · Cited by 14 · see citing works

Open-World Semantic Segmentation Including Class Similarity

Sodano, M., Magistri, F., Nunes, L., Behley, J., & Stachniss, C. (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/cvpr52733.2024.00307 · Cited by 13 · see citing works

Exploiting image translations via ensemble self-supervised learning for Unsupervised Domain Adaptation

Piva, F. J., & Dubbelman, G. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103745 · Cited by 13 · see citing works

A Conservative Approach for Unbiased Learning on Unknown Biases

Jeon, M., Kim, D., Lee, W., Kang, M., & Lee, J. (2022) · 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/cvpr52688.2022.01625 · Cited by 13 · see citing works

Semi-supervised Multi-structure Segmentation in Chest X-Ray Imaging

Brioso, R. C., Pedrosa, J., Mendonça, A. M., & Campilho, A. (2023) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms58004.2023.00325 · Cited by 13 · see citing works

Preterm Newborn Presence Detection in Incubator and Open Bed Using Deep Transfer Learning

Weber, R., Cabon, S., Simon, A., Porée, F., & Carrault, G. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3062617 · Cited by 13 · see citing works

Understanding Bias in Machine LearningPreprint

Gu, J., & Oelke, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.01866 · Cited by 13 · see citing works

Pulmonary abnormality screening on chest x-rays from different machine specifications: a generalized AI-based image manipulation pipeline

Shin, H., Kim, T., Park, J.-H., Raj, H., Jabbar, M. S., Abebaw, Z. D., Lee, J., Văn, C. C., Kim, H., & Shin, D. (2023) · European Radiology Experimental · doi:10.1186/s41747-023-00386-1 · Cited by 12 · see citing works

Fine-Grained Face Annotation Using Deep Multi-Task CNN

Celona, L., Bianco, S., & Schettini, R. (2018) · Sensors · doi:10.3390/s18082666 · Cited by 12 · see citing works

An Empirical Investigation of Bias in the Multimodal Analysis of Financial Earnings Calls

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Designing a Computer-Vision Application: A Case Study for Hand-Hygiene Assessment in an Open-Room Environment

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A Testbed for Cross-Dataset AnalysisPreprint

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