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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TIBET: Identifying and Evaluating Biases in Text-to-Image Generative Models

Chinchure, A., Shukla, P., Bhatt, G., Salij, K., Hosanagar, K., Sigal, L., & Turk, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72986-7_25 · Cited by 37

Explaining Deep Convolutional Neural Networks via Latent Visual-Semantic Filter Attention

Yang, Y., Kim, S., & Joo, 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/cvpr52688.2022.00815 · Cited by 13

Towards Ground Truth Evaluation of Visual ExplanationsPreprint

Osman, A., Arras, L., & Samek, W. (2020) · arXiv (Cornell University) · Cited by 13

Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue modelsPreprint

Smith, E. M., & Williams, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03300 · Cited by 33

Crossing the Format Boundary of Text and Boxes: Towards Unified Vision-Language ModelingPreprint

Yang, Z., Gan, Z., Wang, J., Hu, X., Ahmed, F., Liu, Z., Lu, Y., & Wang, L. (2021) · arXiv (Cornell University) · Cited by 13

Opacity, Machine Learning and Explainable AI

Fernández, A. (2023) · The International library of ethics, law and technology · doi:10.1007/978-3-031-48135-2_3 · Cited by 13

Show, tell and rectify: Boost image caption generation via an output rectifier

Ge, G., Han, Y., Hao, L., Hao, K., Wei, B., & Tang, X. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127651 · Cited by 12

Machine learning data practices through a data curation lens: An evaluation framework

Bhardwaj, E., Gujral, H., Wu, S., Zogheib, C., Maharaj, T., & Becker, C. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658955 · Cited by 22

Person Perception Biases Exposed: Revisiting the First Impressions Dataset

Jacques, J. C. S., Lapedriza, À., Palmero, C., Baró, X., & Escalera, S. (2021) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw52041.2021.00006 · Cited by 14

Disentangled Graph Recurrent Network for Document Ranking

Qian, D., Niu, S., Yuan, T., & Li, Y. (2022) · Data Science and Engineering · doi:10.1007/s41019-022-00179-3 · Cited by 12

Overcoming Language Disparity in Online Content Classification with Multimodal Learning

Verma, G., Mujumdar, R., Wang, Z. J., Choudhury, M. D., & Kumar, S. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19356 · Cited by 14

UDIS: Unsupervised Discovery of Bias in Deep Visual Recognition Models

Krishnakumar, A., Prabhu, V., Sudhakar, S., & Hoffman, J. (2021) · British Machine Vision Conference (BMVC) · doi:10.5244/c.35.102 · Cited by 12

CLIPScore: A Reference-free Evaluation Metric for Image CaptioningPreprint

Hessel, J., Holtzman, A., Forbes, M., Bras, R. J. L., & Choi, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.08718 · Cited by 1,051

Fair-VQA: Fairness-Aware Visual Question Answering Through Sensitive Attribute Prediction

Park, S., Hwang, S., Hong, J. K., & Byun, H. (2020) · IEEE Access · doi:10.1109/access.2020.3041503 · Cited by 12

Robustness of Fusion-based Multimodal Classifiers to Cross-Modal Content Dilutions

Verma, G., Vinay, V., Rossi, R. A., & Kumar, S. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.25 · Cited by 11

Improving Fairness using Vision-Language Driven Image Augmentation

Moreno D’Incà, Tzelepis, C., Patras, I., & Sebe, N. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00463 · Cited by 15

Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations

Sun, T., Gao, Y., Khaladkar, S., Liu, S., Zhao, L., Kim, Y., & Hong, S.-S. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610187 · Cited by 15

Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection

Inel, O., Draws, T., & Aroyo, L. M. (2023) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v11i1.27547 · Cited by 14

Would Deep Generative Models Amplify Bias in Future Models?

Chen, T., Hirota, Y., Otani, M., García, N., & Nakashima, Y. (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/cvpr52733.2024.01030 · Cited by 18

A comprehensive analysis of gender, racial, and prompt-induced biases in large language models

Torres, N., Ulloa, C., Araya, I., Ayala, M., & Jara, S. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00696-6 · Cited by 14

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

A Methodology for Non-Functional Property Evaluation of Machine Learning Models

Anisetti, M., Ardagna, C. A., Damiani, E., & Panero, P. G. (2020) · International Conference on Management of Emergent Digital EcoSystems (MEDES) · doi:10.1145/3415958.3433101 · Cited by 10

Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias

Agarwal, S., Muku, S., Anand, S., & Arora, C. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00395 · Cited by 10

Latent Space Smoothing for Individually Fair Representations

Peychev, M., Ruoss, A., Balunović, M., Baader, M., & Vechev, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19778-9_31 · Cited by 11

Vision to Language: Methods, Metrics and Datasets

Sharif, N., Nadeem, U., Shah, S. A. A., Bennamoun, M., & Liu, W. (2020) · Learning and Analytics in Intelligent Systems · doi:10.1007/978-3-030-49724-8_2 · Cited by 9

Dialect Diversity in Text Summarization on Twitter

Keswani, V., & Celis, L. E. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3450108 · Cited by 13

The Sins of the Parents Are to Be Laid Upon the Children: Biased Humans, Biased Data, Biased Models

Osborne, M. R., Omrani, A., & Dehghani, M. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916231180099 · Cited by 9

Statistical learning from biased training samples

Clémençon, S., & Laforgue, P. (2022) · Electronic Journal of Statistics · doi:10.1214/22-ejs2084 · Cited by 10

APIs de Visão Computacional: investigando mediações algorítmicas a partir de estudo de bancos de imagens

da Silva, T. R., Mintz, A. G., Omena, J. J., Gobbo, B., de Oliveira, T., Takamitsu, H. T., Pilipets, E., & Azhar, H. (2020) · Logos · doi:10.12957/logos.2020.51523 · Cited by 12

Gender Slopes: Counterfactual Fairness for Computer Vision Models by Attribute ManipulationPreprint

Joo, J., & Kärkkäinen, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.10430 · Cited by 9

Balancing out Bias: Achieving Fairness Through Training Reweighting.Preprint

Han, X., Baldwin, T., & Cohn, T. (2021) · arXiv (Cornell University) · Cited by 9

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts

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Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)

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CafeBoost: Causal Feature Boost to Eliminate Task-Induced Bias for Class Incremental Learning

Qiu, B., Li, H., Wen, H., Qiu, H., Wang, L., Meng, F., Wu, Q., & Pan, L. (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/cvpr52729.2023.01537 · Cited by 8

Knowledge Proxy Intervention for Deconfounded Video Question Answering

Li, J., Niu, L., & Zhang, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00261 · Cited by 8

Improving Efficiency in Facial Recognition Tasks Through a Dataset Optimization Approach

Vilaça, L., Viana, P., Carvalho, P., & Andrade, M. T. (2024) · IEEE Access · doi:10.1109/access.2024.3371020 · Cited by 8

Exploiting Transferable Knowledge for Fairness-Aware Image Classification

Hwang, S., Park, S., Lee, P., Jeon, S., Kim, D., & Byun, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69538-5_2 · Cited by 8

Should I take a walk? Estimating Energy Expenditure from Video Data

Peng, K., Roitberg, A., Yang, K., Zhang, J., & Stiefelhagen, R. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00225 · Cited by 8

Mitigating Dataset Imbalance via Joint Generation and Classification

Sahoo, A., Singh, A., Panda, R., Feris, R., & Das, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-65414-6_14 · Cited by 8

Adaptive Label SmoothingPreprint

Krothapalli, U., & Abbott, A. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.06432 · Cited by 8

Benchmarking CNN on 3D Anatomical Brain MRI: Architectures, Data Augmentation and Deep Ensemble LearningPreprint

Dufumier, B., Gori, P., Battaglia, I., Victor, J., Grigis, A., & Duchesnay, É. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.01132 · Cited by 22

Attribute annotation and bias evaluation in visual datasets for autonomous driving

Llorca, D. F., Frau, P., Parra, I., Izquierdo, R., & Gómez, E. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00976-9 · Cited by 8

Listen and Speak Fairly: a Study on Semantic Gender Bias in Speech Integrated Large Language Models

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Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies?

Nikolaus, M., Salin, E., Ayache, S., Fourtassi, A., & Favre, B. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.100 · Cited by 14

Weakly supervised grounded image captioning with semantic matching

Du, S., Zhu, H., Lin, G., Liu, Y., Wang, D. Y., Shi, J., & Wu, Z. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05389-y · Cited by 7

Simple Token-Level Confidence Improves Caption Correctness

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Diversity Blocks for De-biasing Classification Models

Nagpal, S., Singh, M., Singh, R., & Mayank Vatsa, A. (2020) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb48548.2020.9304931 · Cited by 7

Unsupervised Mitigating Gender Bias by Character Components: A Case Study of Chinese Word Embedding

Chen, X., Li, M., Yan, R., Gao, X., & Zhang, X. (2022) · Workshop on Gender Bias in Natural Language Processing (GeBNLP) · doi:10.18653/v1/2022.gebnlp-1.14 · Cited by 7

Indigenous Language Revitalization and the Dilemma of Gender Bias

Hansal, O., Le, N. T., & Sadat, F. (2022) · Workshop on Gender Bias in Natural Language Processing (GeBNLP) · doi:10.18653/v1/2022.gebnlp-1.25 · Cited by 7

Learning Debiased Representations via Conditional Attribute Interpolation

Zhang, Y., Wang, Q., Zhan, D., & Ye, H.-J. (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/cvpr52729.2023.00734 · Cited by 9

Computer Vision Applications and their Ethical Risks in the Global South

Dufresne‐Camaro, C., Chevalier, F., & Ahmed, S. I. (2020) · Proceedings · doi:10.20380/gi2020.17 · Cited by 6

Estimating semantic structure for the VQA answer spacePreprint

Kervadec, C., Antipov, G., Baccouche, M., & Wolf, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.05726 · Cited by 9

README: REpresentation learning by fairness-Aware Disentangling MEthodPreprint

Park, S., Kim, D., Hwang, S., & Byun, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.03775 · Cited by 7

Injecting Bias through Prompts: Analyzing the Influence of Language on LLMs

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Flexible Regularization Approaches for Fairness in Deep Learning

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Human-Centered Deferred Inference: Measuring User Interactions and Setting Deferral Criteria for Human-AI Teams

Lemmer, S. J., Guo, A., & Corso, J. J. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584092 · Cited by 9

Mitigating Dataset Bias in Image Captioning Through Clip Confounder-Free Captioning Network

Kim, Y., Kim, J., Lee, B., Shin, S., & Ro, Y. M. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222502 · Cited by 6

Adventures of Trustworthy Vision-Language Models: A Survey

Mayank Vatsa, A., Jain, A., & Singh, R. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i20.30275 · Cited by 10

ImageCaptioner2: Image Captioner for Image Captioning Bias Amplification Assessment

Abdelrahman, E., Sun, P., Li, L. E., & Elhoseiny, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i19.30080 · Cited by 11

Buscando a los desaparecidos de la “guerra sucia”: ontologías computacionales y la búsqueda de verdad

Chávez, V. M., Martínez-Sánchez, M. E., Winocur, J. Y., & Nateras, G. S. (2021) · Iberoforum Revista de Ciencias Sociales · doi:10.48102/if.2021.v1.n1.149 · Cited by 6

Predicting Visual Political Bias Using Webly Supervised Data and an Auxiliary Task

Thomas, C., & Kovashka, A. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01506-3 · Cited by 19

Informative Attention Supervision for Grounded Video Description

Wan, B., Jiang, W., & Fang, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746751 · Cited by 6

Leveraging explainability for understanding object descriptions in ambiguous 3D environments

Doğan, F. I., Melsión, G. I., & Leite, I. (2023) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.937772 · Cited by 11

Learning to Generate Equitable Text in Dialogue from Biased Training Data

Sicilia, A., & Alikhani, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.163 · Cited by 6

Gender Biases in Automatic Evaluation Metrics for Image Captioning

Qiu, H., Dou, Z.-Y., Wang, T., Çelikyılmaz, A., & Peng, N. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.520 · Cited by 12

Sex and gender bias in natural language processing

Cirillo, D., Gonen, H., Santus, E., Valencia, A., Costa‐jussà, M. R., & Villegas, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-821392-6.00009-1 · Cited by 8

Misclassifications of Contact Lens Iris PAD Algorithms: Is it Gender Bias or Environmental Conditions?

Agarwal, A., Ratha, N., Afzel Noore, B., Singh, R., & Mayank Vatsa, A. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00102 · Cited by 6

Compounding Injustice: The Cascading Effect of Algorithmic Bias in Risk AssessmentsPreprint

O'Brien, T. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3694818 · Cited by 6

Learning Representational Invariances for Data-Efficient Action RecognitionPreprint

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How Data Workers Shape Datasets: The Role of Positionality in Data Collection and Annotation for Computer Vision

Scheuerman, M. K., Woodruff, A., & Brubaker, J. R. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757481 · Cited by 7

Surveying the Landscape of Image Captioning Evaluation: A Comprehensive Taxonomy, Trends, and Metrics Analysis

Berger, U., Stanovsky, G., Abend, O., & Frermann, L. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.52 · Cited by 13

POET: Supporting Prompting Creativity and Personalization with Automated Expansion of Text-to-Image Generation

Han, E. X., Zhang, A. Q., Zhu, H., Shen, H., Liang, P. P., & Hsieh, J. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747710 · Cited by 6

Towards Responsible Spatial Data Science and Geo-AI

Gupta, J., An, S., & Shekhar, S. (2023) · International Conference on Contemporary Computing · doi:10.1145/3607947.3608060 · Cited by 5

VLSlice: Interactive Vision-and-Language Slice Discovery

Slyman, E., Kahng, M., & Lee, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01403 · Cited by 5

FairDeDup: Detecting and Mitigating Vision-Language Fairness Disparities in Semantic Dataset Deduplication

Slyman, E., Lee, S., Cohen, S. D., & Kafle, K. (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/cvpr52733.2024.01319 · Cited by 10

Large-Scale Bidirectional Training for Zero-Shot Image Captioning

Kim, T., Marsden, M. A., Ahn, P., Kim, S., Lee, S., Sala, A., & Kim, S. H. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00733 · Cited by 6

Controlling biases and diversity in diverse image-to-image translation

Wang, Y., González-García, A., Herranz, L., & van de Weijer, J. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2020.103082 · Cited by 5

Reframing explanation as an interactive medium: The EQUAS (Explainable QUestion Answering System) project

Ferguson, W., Batra, D., Mooney, R. J., Parikh, D., Torralba, A. B., Bau, D., Diller, D., Fasching, J., Fiotto-Kaufman, J., Goyal, Y., et al. (2021) · Applied AI Letters · doi:10.1002/ail2.60 · Cited by 6

A non-Discriminatory Approach to Ethical Deep Learning

Tartaglione, E., & Grangetto, M. (2020) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom50675.2020.00126 · Cited by 4

Jigsaw-VAE: Towards Balancing Features in Variational AutoencodersPreprint

Taghanaki, S. A., Havaei, M., Lamb, A., Sanghi, A., Danielyan, A., & Custis, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.05496 · Cited by 5

Counterfactual Generation and Fairness Evaluation Using Adversarially Learned InferencePreprint

Dash, S., & Sharma, A. K. (2020) · arXiv (Cornell University) · Cited by 5

Causal Attention for Vision-Language TasksPreprint

Yang, X., Zhang, H., Qi, G.-J., & Cai, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.03493 · Cited by 5

Leveraging Explainability for Comprehending Referring Expressions in the Real WorldPreprint

Doğan, F. I., Melsión, G. I., & Leite, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.05593 · Cited by 5

Adversarial Fairness Network

Jang, T., Wang, X., & Huang, H. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i20.30220 · Cited by 4

Fairness in machine learning: definition, testing, debugging, and application

Gao, X., Shen, C. Q., Jiang, W., Lin, C., Li, Q., Wang, Q., Li, Q., & Guan, X. (2024) · Science China Information Sciences · doi:10.1007/s11432-023-4060-x · Cited by 4

What’s in a Caption? Dataset-Specific Linguistic Diversity and Its Effect on Visual Description Models and Metrics

Chan, D. M., Myers, A., Vijayanarasimhan, S., Ross, D. A., Seybold, B., & Canny, J. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00520 · Cited by 6

MultiViz: Towards User-Centric Visualizations and Interpretations of Multimodal Models

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Fair Facial Attribute Classification via Causal Graph-Based Attribute Translation

Kang, S., Kim, G., & Yoo, C. D. (2022) · Sensors · doi:10.3390/s22145271 · Cited by 5

Evaluating Gender-Neutral Training Data for Automated Image Captioning

Amend, J. J., Wazzan, A., & Souvenir, R. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671774 · Cited by 4

ViBE: A Tool for Measuring and Mitigating Bias in Image DatasetsPreprint

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Using Depth for Improving Referring Expression Comprehension in Real-World EnvironmentsPreprint

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Efficient Bias Mitigation Without Privileged Information

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Computer Vision, Human Likeness, and Problematic Behaviors

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Visual Semantic Relatedness Dataset for Image Captioning

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De-Biasing Methods in Neural Networks: A Survey

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Women Wearing Lipstick: Measuring the Bias Between an Object and Its Related Gender

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