Women Also Snowboard: Overcoming Bias in Captioning Models

Microsoft COCO: Common Objects in Context

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Rethinking the Inception Architecture for Computer Vision

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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

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"Why Should I Trust You?": Explaining the Predictions of Any Classifier

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Visualizing and understanding convolutional networks

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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

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Show and tell: A neural image caption generator

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Divergence measures based on the Shannon entropy

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Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

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Long-term recurrent convolutional networks for visual recognition and description

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Simplicity Bias Leads to Amplified Performance Disparities

Bell, S. J., & Sagun, L. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594003 · Cited by 5

Dialect Diversity in Text Summarization on TwitterPreprint

Celis, L. E., & Keswani, V. (2020) · arXiv (Cornell University) · Cited by 2

Emerging Trends of Multimodal Research in Vision and Language.Preprint

Uppal, S., Bhagat, S., Hazarika, D., Majumder, N., Poria, S., Zimmermann, R., & Zadeh, A. (2020) · arXiv (Cornell University) · Cited by 3

Shedding light on underrepresentation and Sampling Bias in machine learningPreprint

Zhioua, S., & Binkytė, R. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.05068 · Cited by 3

Improving Video Model Transfer with Dynamic Representation Learning

Li, Y., & Vasconcelos, N. M. (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/cvpr52688.2022.01868 · Cited by 2

KENGIC: KEyword-driven and N-Gram Graph based Image Captioning

Birmingham, B., & Muscat, A. (2022) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta56598.2022.10034584 · Cited by 2

Fair Scratch Tickets: Finding Fair Sparse Networks without Weight Training

Tang, P., Yao, W., Li, Z., & Liu, Y. (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/cvpr52729.2023.02338 · Cited by 3

VLIS: Unimodal Language Models Guide Multimodal Language Generation

Chung, J., & Yu, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.46 · Cited by 2

FairQuantize: Achieving Fairness Through Weight Quantization for Dermatological Disease Diagnosis

Guo, Y., Jia, Z., Hu, J., & Shi, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72117-5_31 · Cited by 2

What Could Go Wrong? Discovering and Describing Failure Modes in Computer Vision

Csurka, G., Hayes, T. L., Larlus, D., & Volpi, R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92648-8_12 · Cited by 2

Interpreting and Improving Deep-Learning Models with Reality Checks

Singh, C., Ha, W., & Yu, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-04083-2_12 · Cited by 2

Proposing an Interactive Audit Pipeline for Visual Privacy Research

DeHart, J., Xu, C., Grant, C., & Egede, L. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671478 · Cited by 3

Mask & Focus: Conversation Modelling by Learning Concepts

Pandey, G. S., Raghu, D., & Joshi, S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6381 · Cited by 2

Fair Visual Recognition in Limited Data Regime using Self-Supervision and Self-Distillation

Mazumder, P., Singh, P. P., & Namboodiri, V. P. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00394 · Cited by 2

Distilling Translations with Visual AwarenessPreprint

Ive, J., Madhyastha, P., & Specia, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.07701 · Cited by 2

Issues in Computer Vision Data Collection: Bias, Consent, and Label Taxonomy

Dulhanty, C. (2020) · UWSpace (University of Waterloo) · Cited by 3

Reducing Bias in Face Recognition

Gong, S., Liu, X., & Jain, A. K. (2023) · Springer eBooks · doi:10.1007/978-3-031-43567-6_12 · Cited by 1

Evaluation of Gender Bias in Amharic Word Embedding Model

Zenebe, B., Gizaw, S., & Abgaz, Y. M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70242-6_17 · Cited by 1

GRACE: Graph-Based Contextual Debiasing for Fair Visual Question Answering

Zhang, Y., Jiang, M. X., & Zhao, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72643-9_11 · Cited by 1

Navigating representation: utilizing prompt engineering to minimize representational harms in journalist’s image captions

Sarhan, H., Shahrezaye, M., & Hegelich, S. (2025) · AI and Ethics · doi:10.1007/s43681-025-00773-x · Cited by 1

Measuring Text-Image Retrieval Fairness with Synthetic Data

Gómez, L. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730030 · Cited by 1

GenderBias-VL: Benchmarking Gender Bias in Vision Language Models via Counterfactual Probing

Xiao, Y., Liu, X., Cheng, Q., Yin, Z., Liang, S., Li, J., Shao, J., Liu, A., & Tao, D. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02556-7 · Cited by 24

Stylized Explanations: Enhancing Neural Networks Visual Interpretations Using Neural Style Transfer

Ibrahim, R. (2026) · IEEE Access · doi:10.1109/access.2026.3658548 · Cited by 1

Same Person, Different Depiction: Counterfactual Evaluation of Vision-Language Models on Individuals with Limb Deficiencies

Du, H., Ying, J., Chen, X., Wang, S., Li, X., & Yu, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36969-7_37 · Cited by 1

Affirmative Equality: A Revised Goal of De-bias for Artificial Intelligence Based on Difference Principle

Peng, K. (2020) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1109/icaice51518.2020.00009 · Cited by 1

SHAI 2023: Workshop on Designing for Safety in Human-AI Interactions

Goyal, N., Hong, S.-S., Mandryk, R. L., Li, T. J.-J., Luther, K., & Wang, D. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581754.3584169 · Cited by 1

Addressing Weak Decision Boundaries in Image Classification by Leveraging Web Search and Generative Models

Dammu, P. P. S., Feng, Y., & Shah, C. S. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/659 · Cited by 2

Targeted Activation Penalties Help CNNs Ignore Spurious Signals

Zhang, D., Williams, M., & Toni, F. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i15.29610 · Cited by 1

Towards Deconfounded Visual Question Answering via Dual-causal Intervention

Peng, D., & Wei, W. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679594 · Cited by 1

Top-GAP: Integrating Size Priors in CNNs for More Interpretability, Robustness, and Bias Mitigation

Nieradzik, L., Stephani, H., & Keuper, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92648-8_9 · Cited by 1

One Size Doesn't Fit All: Adaptive Label Smoothing

Krothapalli, U., & Abbott, L. (2021) · Cited by 1

Domain-Robust Pedestrian-View Intersection Classification

Astrid, M., Zaheer, M. Z., Lee, J.-Y., & Lee, S. (2021) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc52510.2021.9621138 · Cited by 1

Machine Learning

Uddin, M. Z. (2022) · Springer eBooks · doi:10.1007/978-3-031-11534-9_3 · Cited by 2

Hide-and-Tell: Learning to Bridge Photo Streams for Visual StorytellingPreprint

Jung, Y., Kim, D., Woo, S., Kim, K., Kim, S.-J., & Kweon, I. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.00774 · Cited by 1

Machines Learn Appearance Bias in Face Recognition.Preprint

Steed, R., & Caliskan, A. (2020) · arXiv (Cornell University) · Cited by 1

Debiasing Convolutional Neural Networks via Meta OrthogonalizationPreprint

David, K. E. A., Liu, Q., & Fong, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.07453 · Cited by 1

The Geometry of Distributed Representations for Better Alignment, Attenuated Bias, and Improved InterpretabilityPreprint

Dev, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.12465 · Cited by 1

Automatic Face Understanding: Recognizing Families in PhotosPreprint

Robinson, J. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.08941 · Cited by 1

SMURF: SeMantic and linguistic UndeRstanding Fusion for Caption Evaluation via Typicality AnalysisPreprint

Feinglass, J., & Yang, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.01444 · Cited by 1

Effective Explainable Artificial Intelligence using Visual Explanations in Images

Ibrahim, R. (2022) · doi:10.22215/etd/2022-15159 · Cited by 1

Multimodal Transformer Model for Moment Localization in Surveillance Videos Via Natural Language QueriesPreprint

Pawar, K., Halbe, S., Patil, V., Gupta, V., & Attar, V. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4880715 · Cited by 1

Application of deep learning and geo-knowledge bases to scene understanding

Bhattacharya, S., Agrawal, R. K., & Wagner, N. (2018) · International Conference on Management of Emergent Digital EcoSystems (MEDES) · doi:10.1145/3281375.3281405

Understanding Unconscious Bias by Large-scale Data Analysis

Tang, S. (2019) · eScholarship (California Digital Library)

Addressing Target Shift in Zero-shot Learning using Grouped Adversarial Learning

Chemmengath, S., Paul, S., Bharadwaj, S., Samanta, S., & Sankaranarayanan, K. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00268

Application and Perspectives of Convolutional Neural Networks in Digital Intelligence

Čík, I., Jaščur, M., Rasamoelina, A. D., Magyar, J., Hruška, L., Adjailia, F., Mach, M., Bundzel, M., & Sinčák, P. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-63872-6_2

Enhanced Modality Transition for Image CaptioningPreprint

Wang, Z., Luo, Y., & Huang, Z. H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.11526

Explain and Improve: LRP-Inference Fine-Tuning for Image Captioning ModelsPreprint

Sun, J., Lapuschkin, S., Samek, W., & Binder, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.01037

Incorporating additional knowledge into image captioners

Yang, X. (2021) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/151726

Visual Recognition with Deep Learning from Biased Image Datasets.Preprint

Vogel, R., Clémençon, S., & Laforgue, P. (2021) · arXiv (Cornell University)

Come Again? Re-Query in Referring Expression Comprehension.Preprint

Lemmer, S. J., & Corso, J. J. (2021) · arXiv (Cornell University)

Towards robust inference against distribution shifts in computer vision

Tang, K. (2021) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/154119

Questions from a Contraceptive Pill Junkie: Applying Human Psychometrics to Investigate Gender Bias in Machine Learning

Darney, H. (2020) · Open Access Victoria University of Wellington | Te Herenga Waka (Figshare) · doi:10.26686/wgtn.17151377.v1 · Cited by 1

Questioning causality on sex, gender and COVID-19, and identifying bias in large-scale data-driven analyses: the Bias Priority Recommendations and Bias Catalog for PandemicsPreprint

Díaz-Rodríguez, N., Binkytė-Sadauskienė, R., Bakkali, W., Bookseller, S., Tubaro, P., Bacevičius, A., & Chatila, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.14492 · Cited by 1

Towards the Detection of Hateful Sentiment in Social Networks

González, S. G., González, A. B. G., & López, V. F. (2022) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-14859-0_13

Serially Disentangled Learning for Multi-Layered Neural Networks

Kamimura, R., & Kitajima, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08530-7_56

Min-Max Cost and Information Control in Multi-layered Neural Networks

Kamimura, R., & Kitajima, R. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-18461-1_1

Fair Machine Learning-An Analytical Study Based on CiteSpace

Luo, X., Cui, J., & Ma, S. (2023) · International Conference on Consumer Electronics and Computer Engineering (ICCECE) · doi:10.1109/iccece58074.2023.10135360

Destructive computing with winner-lose-all competition in multi-layered neural networks

Kamimura, R. (2023) · International Journal of Hybrid Intelligent Systems · doi:10.3233/his-230011

Fighting selection bias in statistical learning: application to visual recognition from biased image databases

Clémençon, S., Laforgue, P., & Vogel, R. (2021) · Journal of nonparametric statistics (Print) · doi:10.1080/10485252.2023.2259011

A Novel and Adaptive Evaluation Mechanism for Deep Learning Models in Medical Imaging and Disease Recognition

Loukil, Z., Mirza, Q. K. A., & Sayers, W. (2023) · International Conference on Future Internet of Things and Cloud (FiCloud) · doi:10.1109/ficloud58648.2023.00047

A multimodal sentiment model based on causal gating attention mechanismPreprint

Gan, Z., Sun, X.-E., & Liu, M. (2024) · Research Square · doi:10.21203/rs.3.rs-3932545/v1

Gender Bias in Pre-Trained Deep Learning Models

Islam, A. U., & Islam, A. R. (2023) · IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) · doi:10.1109/csde59766.2023.10487724

Practical Tools from Domain Adaptation for Designing Inclusive, Equitable, and Robust Generative AI

Sicilia, A., & Alikhani, M. (2023) · doi:10.18653/v1/2023.ijcnlp-tutorials.3

Beyond Cloud Service Certification

Anisetti, M., Ardagna, C. A., Damiani, E., & Ioini, N. E. (2024) · Synthesis lectures on information security, privacy, and trust · doi:10.1007/978-3-031-59724-4_5

A Way of Making Smart Health Through Collaborating Machine Learning with the Bibliometrics

Vignesh, T., Sruthi, P. S., Mohanraj, T., Kumar, G., Varkale, A., & Sheela, M. S. (2024) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite60783.2024.10617395

Element-Centered Multi-granularity Network for Dense Video Captioning

Dang, X., Wang, G., Wu, X., & Qin, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8792-0_31

Unsupervised Learning of Unbiased Visual Representations

Barbano, C. A., Tartaglione, E., & Grangetto, M. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3514554

Societal Bias in Image Captioning: Identifying and Measuring Bias Amplification

HIROTA, Y., Nakashima, Y., & García, N. (2025) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2024edp7116

Mitigating Gender Bias Amplification in Image Captioning

HIROTA, Y., Nakashima, Y., & García, N. (2025) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2024edp7164

Mitigating Gender Bias in Hindi to English Neural Machine Translation

Tyagi, S., & Tyagi, A. (2025) · Algorithms for intelligent systems · doi:10.1007/978-981-97-7946-8_8

MASS: Overcoming Language Bias in Image-Text Matching

Chung, J., Lim, S., Lee, S., & Yu, Y. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i3.32262

FairFLRep: Fairness-Aware Fault Localization and Repair of Deep Neural Networks

Openja, M., Arcaini, P., Khomh, F., & Ishikawa, F. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3766890 · Cited by 1

DR-VQA: Decompose-then-Reconstruct for Visual Question Answering in BLV Assistance

Pan, B., Shi, H., & Gao, X. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755393

Make "V" and "Q" Inseparable: Deliberately Dual-Channel Adversarial Learning for Robust Visual Question Answering

Wu, H., Li, Z., Chen, F., Wang, Z., Xu, J., Hu, L., Cao, H., Li, Y., Wang, J., & Chang, J. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228973

Explainable and Efficient Hateful Meme Detection using Multimodal Vision-Language Fusion

He, J., Yu, C.-Y., & Zhang, W. (2025) · doi:10.1145/3774791.3774800

Gender and Ethnicity Bias in Deep LearningPreprint

Islam, A. U., Zarin, I., Tabassum, T., Perera, W. S., & Islam, A. R. (2025) · Research Square · doi:10.21203/rs.3.rs-8321287/v1

MEDebiaser: A Human-AI Feedback System for Mitigating Bias in Multi-label Medical Image Classification

Shi, S., Shao, Y., Jiang, H., Yao, Y., Zhang, Z., Ding, X., & Li, Q. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747725 · Cited by 1

A Deconfounded Model for Fault Diagnosis and Retrieval of Fault Texts

Lin, B., Chen, Z., Yang, S., Zhang, Z., & 彭红雷 (2025) · International Conference on Big-data Service and Intelligent Computation (BDSIC) · doi:10.1145/3778265.3778273

Multi-Perspective Visual Contrastive Decoding for Reliable Assistance

Pan, B., Shi, H., & Gao, X. (2026) · ACM Transactions on Internet of Things · doi:10.1145/3785360

Enhancing Fairness Without Demographic Labels via Identifying and Mitigating Potential Biases

Lee, P., & Park, S. (2026) · Symmetry · doi:10.3390/sym18020344

Multimodal machine learning : complementarity of textual and visual contexts

Zablocki, É. (2019) · theses.fr (ABES) · doi:10.70675/5c353aafz9b11z40d4zb921z3af1e200002a

Contribution à des problèmes statistiques d'ordonnancement et d'apprentissage par renforcement avec aversion au risque

Achab, M. (2020) · doi:10.70675/b1501209z5cb8z4688z96eczd44e7c361461

Learning to detect visual relations

Peyre, J. (2019) · theses.fr (ABES) · doi:10.70675/e491bc42z84b1z4107zadf9z95414bcd6961

Deep kernel representation learning for complex data and reliability issues

Laforgue, P. (2020) · theses.fr (ABES) · doi:10.70675/e8c2e6ccz9ff4z451cza8a2zf31fdf4eabd5

Ordonnancement par similarité pour la biométrie : théorie et pratique

Vogel, R. (2020) · doi:10.70675/5bdaaedcz78d8z48aczb5e6zc178ffa29310

Deep Multimodal Learning for Joint Textual and Visual Reasoning

Bordes, P. (2020) · theses.fr (ABES) · doi:10.70675/8bd1beefz5760z4f96z8d8cz87e025608c32

Deep Multimodal Learning for Vision and Language Processing

Cadène, R. (2020) · theses.fr (ABES) · doi:10.70675/674b1e7aza957z4fc5za03czde9aab142a84 · Cited by 1

A Neural-Symbolic learning framework to produce interpretable predictions for image classification

Bennetot, A. (2022) · theses.fr (ABES) · doi:10.70675/2b59f598z2028z49afz8a20z29b21f19f394

Collateral-Free Learning of Deep Representations : From Natural Images to Biomedical Applications

Barbano, C. A. (2023) · theses.fr (ABES) · doi:10.70675/d0edb18bzae2bz48f8zba04z48d7240e8e0d

Study of the multimodal understanding of vision-language transformer models

Salin, E. (2023) · theses.fr (ABES) · doi:10.70675/2beca704z5d3bz421dzbf61z61e00610aaeb

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

Promoting Interpretability in Image Captioning Through Attribution Segmentation Alignment

Catanpatan, W. Y., Aballe, Z., & Maderazo, C. V. (2026) · International Conference on Multi-scale Artificial Intelligence (MAI) · doi:10.1109/mai69289.2026.11543767

Towards Pareto Efficiency in Fair Facial Expression and Action Unit Recognition

Lian, T., & Çeliktutan, O. (2026) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg67764.2026.11557017

Attention-Level Causal Intervention Framework for Multimodal Fake News Detection

Hao, S., Li, S., Lin, R., Zhang, J., & Wang, X. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10090301

Data for Machine Learning

Law, E., & Yin, M. (2026) · Cambridge University Press eBooks · doi:10.1017/9781108872225.008

Doing Human-Centered Machine Learning

Fiebrink, R., Gillies, M. F. P., & Ramos, G. (2026) · Cambridge University Press eBooks · doi:10.1017/9781108872225.006

Architectural Vulnerability Mapping and Ethical Risk Taxonomy in Vision‐Language Models

Bakır, Ç., & Gezer, A. (2026) · Journal of Software Evolution and Process · doi:10.1002/smr.70169

Visual cues in MI-BCI induce biases in EEG classification models with deep learning but not with standard machine learning

Trocellier, D., Kojima, S., N’Kaoua, B., & Lotte, F. (2026) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2026.1804143

Explanation Needs Emerge Before Explicit Requests in Human-Robot Interaction

Kontogiorgos, D., Gustafson, J., & Shah, J. (2026) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3776574.3831198

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

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

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 13

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 49

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