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Gender and Smile Classification Using Deep Convolutional Neural Networks

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CLIPScore: A Reference-free Evaluation Metric for Image Captioning

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FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation

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Requirements Engineering for Machine Learning: Perspectives from Data Scientists

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Causal Attention for Vision-Language Tasks

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Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development

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Gender Bias in Machine Translation

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Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases

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More Grounded Image Captioning by Distilling Image-Text Matching Model

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Deconfounded Image Captioning: A Causal Retrospect

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FairFace: Face Attribute Dataset for Balanced Race, Gender, and AgePreprint

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Face Recognition: Too Bias, or Not Too Bias?

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Reducing Sentiment Bias in Language Models via Counterfactual Evaluation

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Black Box Attacks on Explainable Artificial Intelligence(XAI) methods in Cyber Security

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Fair Attribute Classification through Latent Space De-biasing

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Understanding and Mitigating Annotation Bias in Facial Expression Recognition

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Explainable Convolutional Neural Networks: A Taxonomy, Review, and Future Directions

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Muse: Text-To-Image Generation via Masked Generative Transformers

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Detecting Human-Object Interactions via Functional Generalization

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Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems

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Mitigating face recognition bias via group adaptive classifier

Gong, S., Liu, X., & Jain, A. K. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00342 · Cited by 92 · 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

Explicit Bias Discovery in Visual Question Answering Models

Manjunatha, V., Saini, N., & Davis, L. S. (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.2019.00979 · Cited by 85 · see citing works

Text-Only Training for Image Captioning using Noise-Injected CLIP

Nukrai, D., Mokady, R., & Globerson, A. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.299 · Cited by 89 · see citing works

A Study of Face Obfuscation in ImageNetPreprint

Yang, K., Yau, J., Fei-Fei, L., Deng, J., & Russakovsky, O. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.06191 · Cited by 103 · see citing works

Distilling Translations with Visual Awareness

Ive, J., Madhyastha, P., & Specia, L. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1653 · Cited by 85 · see citing works

Roses are Red, Violets are Blue… But Should VQA expect Them To?

Kervadec, C., Antipov, G., Baccouche, M., & Wolf, C. (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/cvpr46437.2021.00280 · Cited by 84 · see citing works

Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder

Gouthaman, K. V., & Mittal, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58601-0_2 · Cited by 82 · see citing works

Understanding and Evaluating Racial Biases in Image Captioning

Zhao, D., Wang, A., & Russakovsky, O. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01456 · Cited by 84 · 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

A causal perspective on dataset bias in machine learning for medical imaging

Jones, C., Castro, D. C., Ribeiro, F. D. S., Oktay, O., McCradden, M. D., & Glocker, B. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00797-8 · Cited by 87 · see citing works

Addressing fairness issues in deep learning-based medical image analysis: a systematic review

Xu, Z., Li, J., Yao, Q., Li, H., Zhao, M., & Zhou, S. K. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01276-5 · Cited by 82 · see citing works

Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects

Grand, G., & Belinkov, Y. (2019) · doi:10.18653/v1/w19-1801 · Cited by 77 · see citing works

REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets

Wang, A., Liu, A., Zhang, R., Kleiman, A., Kim, L., Zhao, D., Shirai, I., Narayanan, A., & Russakovsky, O. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01625-5 · Cited by 85 · see citing works

Deep image captioning: A review of methods, trends and future challenges

Xu, L., Tang, Q., Lv, J., Zheng, B., Zeng, X., & Li, W. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126287 · Cited by 78 · see citing works

Are You Looking? Grounding to Multiple Modalities in Vision-and-Language Navigation

Hu, R.-H., Fried, D., Rohrbach, A., Klein, D., Darrell, T., & Saenko, K. (2019) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1655 · Cited by 114 · see citing works

REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets

Wang, A., Narayanan, A., & Russakovsky, O. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58580-8_43 · Cited by 72 · see citing works

Show, Deconfound and Tell: Image Captioning with Causal Inference

Liu, B., Wang, D., Yang, X., Zhou, Y. Z., Yao, R., Shao, Z., & Zhao, 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/cvpr52688.2022.01751 · Cited by 71 · see citing works

Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning

Gao, Y., Gu, S., Jiang, J., Hong, S.-S., Yu, D., & Zhao, L. (2024) · ACM Computing Surveys · doi:10.1145/3644073 · Cited by 71 · see citing works

In Defense of Scene Graphs for Image Captioning

Nguyen, K., Tripathi, S., Du, B., Guha, T., & Nguyen, T. Q. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00144

Unshuffling Data for Improved Generalization in Visual Question Answering

Teney, D., Abbasnejad, E., & van den Hengel, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00145 · Cited by 66 · see citing works

Using Explainability to Help Children UnderstandGender Bias in AI

Melsión, G. I., Torre, I., Vidal, E., & Leite, I. (2021) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3459990.3460719 · Cited by 65 · see citing works

Multiple Adverse Weather Conditions Adaptation for Object Detection via Causal Intervention

Zhang, H., Xiao, L., Cao, X., & Foroosh, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3166765 · Cited by 65 · see citing works

Explainable Artificial Intelligence for Cybersecurity

Sharma, D. K., Mishra, J., Singh, A., Govil, R., Srivastava, G., & Lin, J. C. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108356 · Cited by 64 · see citing works

Deep Learning for Face Recognition: Pride or Prejudiced?Preprint

Nagpal, S., Singh, M., Singh, R., & Mayank Vatsa, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.01219 · Cited by 63 · see citing works

Mitigating Demographic Bias in Facial Datasets with Style-Based Multi-attribute Transfer

Georgopoulos, M., Oldfield, J. P., Nicolaou, M. A., Panagakis, Y., & Pantić, M. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01448-w · Cited by 63 · see citing works

Perturbation augmentation for fairer NLP

Qian, R., Ross, C., Fernandes, J., Smith, E. M., Kiela, D., & Williams, A. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.646 · Cited by 69 · see citing works

Data and its (dis)contents: A survey of dataset development and use in machine learning research

Paullada, A., Raji, I. D., Bender, E. M., Denton, E., & Hanna, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100336 · Cited by 548 · see citing works

Detecting Bias with Generative Counterfactual Face Attribute AugmentationPreprint

Denton, E., Hutchinson, B., Mitchell, M., & Gebru, T. (2019) · arXiv (Cornell University) · Cited by 61 · see citing works

Fairness in Deep Learning: A Survey on Vision and Language Research

Parraga, O., Móre, M. D., Mattjie, C., Gavenski, N. S., Kupssinskü, L. S., Medronha, A., de Moura, L. V., Simões, G. S., & Barros, R. C. (2023) · ACM Computing Surveys · doi:10.1145/3637549 · Cited by 67 · see citing works

Accessible Cultural Heritage through Explainable Artificial Intelligence

Díaz-Rodríguez, N., & Pisoni, G. (2020) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3386392.3399276 · Cited by 55 · see citing works

On Scaling Up a Multilingual Vision and Language Model

Chen, X., Djolonga, J., Padlewski, P., Mustafa, B., Changpinyo, S., Wu, J., Riquelme, C., Goodman, S., Wang, X., Tay, Y., et al. (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/cvpr52733.2024.01368 · Cited by 140 · see citing works

RUBi: Reducing Unimodal Biases for Visual Question Answering

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Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search

Wang, J., Liu, Y., & Wang, X. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.151 · Cited by 64 · see citing works

Knowing What to Learn: A Metric-Oriented Focal Mechanism for Image Captioning

Ji, J., Ma, Y., Sun, X., Zhou, Y., Wu, Y., & Ji, R. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3183434 · Cited by 50 · see citing works

FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis

Wu, Y., Zeng, D., Xu, X., Shi, Y., & Hu, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16431-6_70 · Cited by 49 · see citing works

Quantifying Societal Bias Amplification in Image Captioning

Hirota, Y., Nakashima, Y., & García, N. (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/cvpr52688.2022.01309 · Cited by 50 · see citing works

SQuINTing at VQA Models: Introspecting VQA Models With Sub-Questions

Selvaraju, R. R., Tendulkar, P., Parikh, D., Horvitz, E., Ribeiro, M. T., Nushi, B., & Kamar, E. S. (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/cvpr42600.2020.01002 · Cited by 51 · see citing works

Mitigating Gender Bias in Captioning Systems

Tang, R., Du, M., Li, Y., Liu, Z., Zou, N., & Hu, X. (2021) · Proceedings of the Web Conference 2021 · doi:10.1145/3442381.3449950 · Cited by 50 · see citing works

Symphony: Composing Interactive Interfaces for Machine Learning

Bäuerle, A., Cabrera, Á. A., Hohman, F., Maher, M., Koski, D., Suau, X., Barik, T., & Moritz, D. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502102 · Cited by 50 · see citing works

Visual Cluster Grounding for Image Captioning

Jiang, W., Zhu, M., Fang, Y., Shi, G., Zhao, X., & Liu, Y. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3177318 · Cited by 48 · see citing works

SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering

Gupta, V. K., Li, Z., Kortylewski, A., Zhang, C., Li, Y., & Yuille, A. (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/cvpr52688.2022.00502 · Cited by 48 · see citing works

Tutorial on Multimodal Machine Learning: Principles, Challenges, and Open Questions

Liang, P. P., & Morency, L. (2023) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3610661.3617602 · Cited by 48 · see citing works

Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles

Clark, C., Yatskar, M., & Zettlemoyer, L. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.272 · Cited by 48 · see citing works

Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals

Dash, S., Balasubramanian, V. N., & Sharma, A. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00393 · Cited by 50 · see citing works

Women also Snowboard: Overcoming Bias in Captioning Models (Extended Abstract)Preprint

Hendricks, L. A., Burns, K., Saenko, K., Darrell, T., & Rohrbach, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.00517 · Cited by 1 · see citing works

Mitigating Gender Bias in Captioning Systems

Tang, R., Du, M., Li, Y., Liu, Z., Zou, N., & Hu, X. (2021) · Proceedings of the Web Conference 2021 · doi:10.1145/3442381.3449950 · Cited by 50 · see citing works

Model-Agnostic Gender Debiased Image Captioning

Hirota, Y., Nakashima, Y., & García, N. (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/cvpr52729.2023.01458 · Cited by 10 · see citing works

Exposing and Correcting the Gender Bias in Image Captioning Datasets and ModelsPreprint

Bhargava, S., & Forsyth, D. A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.00578 · Cited by 19 · see citing works

A Woman with a Knife or A Knife with a Woman? Measuring Directional Bias Amplification in Image Captions

Nair, R., Tokas, B., & Kerner, H. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00033