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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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 59

Retrieval-augmented Image Captioning

Ramos, R., Elliott, D., & Martins, B. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.266 · Cited by 61

Explainable machine learning for precise fatigue crack tip detection

Melching, D., Strohmann, T., Requena, G. C., & Breitbarth, E. (2022) · Scientific Reports · doi:10.1038/s41598-022-13275-1 · Cited by 45

Detecting Gender Stereotypes: Lexicon vs. Supervised Learning Methods

Cryan, J., Tang, S., Zhang, X., Metzger, M. J., Zheng, H.-T., & Zhao, B. Y. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376488 · Cited by 45

Towards Causal Benchmarking of Bias in Face Analysis Algorithms

Balakrishnan, G., Xiong, Y., Xia, W., & Perona, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58523-5_32 · Cited by 48

Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers

Zietlow, D., Lohaus, M., Balakrishnan, G., Kleindeßner, M., Locatello, F., Schölkopf, B., & Russell, C. (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/cvpr52688.2022.01016 · Cited by 54

Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning

Biten, A. F., Gomez, L., & Karatzas, D. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00253 · Cited by 43

Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation

Xie, Y., Sun, F., Deng, Y., Li, Y., & Ding, B. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.10 · Cited by 49

Towards Global Explanations of Convolutional Neural Networks With Concept Attribution

Wu, W., Su, Y., Chen, X., Zhao, S., King, I., Lyu, M. R.-T., & Tai, Y. (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/cvpr42600.2020.00868 · Cited by 43

Measuring Representational Harms in Image Captioning

Wang, A., Barocas, S., Laird, K., & Wallach, H. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533099 · Cited by 40

Exposing and Mitigating Spurious Correlations for Cross-Modal Retrieval

Kim, J. M., Koepke, A. S., Schmid, C., & Akata, Z. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00257 · Cited by 41

Making Short-Form Videos Accessible with Hierarchical Video Summaries

Daele, T. V., Iyer, A., Zhang, Y., Derry, J. C., Huh, M., & Pavel, A. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642839 · Cited by 40

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 37

Explainable Deep Classification Models for Domain Generalization

Zunino, A., Bargal, S. A., Volpi, R., Sameki, M., Zhang, J., Sclaroff, S., Murino, V., & Saenko, K. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00361 · Cited by 44

To "See" is to Stereotype

Barlas, P., Kyriakou, K., Guest, O., Kleanthous, S., & Otterbacher, J. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3432931 · Cited by 41

Learning representational invariances for data-efficient action recognition

Zou, Y., Choi, J., Wang, Q., & Huang, J. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103597 · Cited by 43

FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition

Sixta, T., Jacques, J. C. S., Buch-Cardona, P., Vazquez, E., & Escalera, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-65414-6_32 · Cited by 41

Unshuffling Data for Improved GeneralizationPreprint

Teney, D., Abbasnejad, E., & van den Hengel, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.11894 · Cited by 38

Explain and improve: LRP-inference fine-tuning for image captioning models

Sun, J., Lapuschkin, S., Samek, W., & Binder, A. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.07.008 · Cited by 52

Gender and Racial Bias in Visual Question Answering Datasets

Hirota, Y., Nakashima, Y., & García, N. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533184 · Cited by 43

DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations

Lyu, Y., Liang, P. P., Deng, Z., Salakhutdinov, R., & Morency, L. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534148 · Cited by 46

WeaQA: Weak Supervision via Captions for Visual Question Answering

Banerjee, P., Gokhale, T., Yang, Y., & Baral, C. R. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.302 · Cited by 45

Revealing the role of explainable AI: How does updating AI applications generate agility-driven performance?

Masialeti, M., Talaei‐Khoei, A., & Yang, A. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102779 · Cited by 37

OpenBias: Open-Set Bias Detection in Text-to-Image Generative Models

Moreno D’Incà, Peruzzo, E., Mancini, M., Xu, D., Goe, V., Xu, X., Wang, Z., Shi, H., & Sebe, N. (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/cvpr52733.2024.01162 · Cited by 54

Interpreting the Rhetoric of Visual Advertisements

Ye, K., Nazari, N. H., Hahn, J., Hussain, Z., Zhang, M., & Kovashka, A. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2947440 · Cited by 40

Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly

Whitehead, S., Petryk, S., Shakib, V., Gonzalez, J. E., Darrell, T., Rohrbach, A., & Rohrbach, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20059-5_9 · Cited by 47

Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset BiasesPreprint

Clark, C., Yatskar, M., & Zettlemoyer, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.03683 · Cited by 95

Learning Credible Deep Neural Networks with Rationale Regularization

Du, M., Liu, N., Yang, F., & Hu, X. (2019) · Proceedings · doi:10.1109/icdm.2019.00025 · Cited by 39

Mitigating Gender Bias in Natural Language Processing: Literature ReviewPreprint

Sun, T., Gaut, A., Tang, S., Huang, Y., ElSherief, M., Zhao, J., Mirza, D., Belding, E., Chang, K.-W., & Wang, W. Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.08976 · Cited by 109

Gender bias in transformers: A comprehensive review of detection and mitigation strategies

Nemani, P., Joel, Y. D., Vijay, P., & Liza, F. F. (2023) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100047 · Cited by 55

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

Bennetot, A., Franchi, G., Ser, J. D., Chatila, R., & Díaz-Rodríguez, N. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109947 · Cited by 33

Discover and Mitigate Unknown Biases with Debiasing Alternate Networks

Li, Z., Hoogs, A. J., & Xu, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19778-9_16 · Cited by 39

Worst of Both Worlds: Biases Compound in Pre-trained Vision-and-Language Models

Srinivasan, T., & Bisk, Y. (2022) · Workshop on Gender Bias in Natural Language Processing (GeBNLP) · doi:10.18653/v1/2022.gebnlp-1.10 · Cited by 53

He is very intelligent, she is very beautiful? On Mitigating Social Biases in Language Modelling and Generation

Garimella, A., Amarnath, A., KUMAR, K. T. K., Yalla, A. P., Natarajan, A., Chhaya, N., & Srinivasan, B. V. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.397 · Cited by 37

RES: A Robust Framework for Guiding Visual Explanation

Gao, Y., Sun, T., Bai, G., Gu, S., Hong, S. R., & Liang, Z. (2022) · Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539419 · Cited by 40

Open Long-Tailed Recognition in a Dynamic World

Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., & Yu, S. X. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3200091 · Cited by 38

Taxonomizing and Measuring Representational Harms: A Look at Image Tagging

Katzman, J., Wang, A., Scheuerman, M., Blodgett, S. L., Laird, K., Wallach, H., & Barocas, S. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26670 · Cited by 32

Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service”

Lewicki, K., Lee, M. S. A., Cobbe, J., & Singh, J. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581463 · Cited by 30

Gender Slopes

Joo, J., & Kärkkäinen, K. (2020) · doi:10.1145/3422841.3423533 · Cited by 45

Hierarchical Memory Learning for Fine-Grained Scene Graph Generation

Deng, Y., Li, Y., Zhang, Y., Xiang, X., Wang, J., Chen, J., & Ma, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19812-0_16 · Cited by 32

Balancing Biases and Preserving Privacy on Balanced Faces in the Wild

Robinson, J. P., Qin, C., Henon, Y., Timoner, S. J., & Fu, Y. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3282837 · Cited by 33

From Show to Tell: A Survey on Image CaptioningPreprint

Stefanini, M., Cornia, M., Baraldi, L., Cascianelli, S., Fiameni, G., & Cucchiara, R. (2021) · arXiv (Cornell University) · Cited by 31

Mitigating Gender Bias Amplification in Distribution by Posterior Regularization

Jia, S., Meng, T., Zhao, J., & Chang, K.-W. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.264 · Cited by 30

Directional Bias Amplification

Wang, A., & Russakovsky, O. (2021) · arXiv · doi:10.48550/arxiv.2102.12594 · Cited by 30

Viewpoint: AI as Author – Bridging the Gap Between Machine Learning and Literary Theory

Van Heerden, I., & Bas, A. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12593 · Cited by 29

iPerceive: Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question AnsweringPreprint

Chadha, A. R., Arora, G., & Kaloty, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.07735 · Cited by 36

The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models

Birhane, A., Dehdashtian, S., Prabhu, V., & Boddeti, V. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658968 · Cited by 28

Visual Commonsense Representation Learning via Causal Inference

Wang, T., Huang, J., Zhang, H., & Sun, Q. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00197 · Cited by 28

Unsupervised Learning of Debiased Representations with Pseudo-Attributes

Seo, S., Lee, J., & Han, B. (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/cvpr52688.2022.01624 · Cited by 29

Multi-objective optimization determines when, which and how to fuse deep networks: An application to predict COVID-19 outcomes

Guarrasi, V., & Soda, P. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106625 · Cited by 29

Deconfounded Image Captioning: A Causal RetrospectPreprint

Yang, X., Zhang, H., & Cai, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.03923 · Cited by 43

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

Jung, Y., Kim, D., Woo, S., Kim, K., Kim, S.-J., & Kweon, I. S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6780 · Cited by 27

Diverse Image Captioning with Context-Object Split Latent Spaces

Mahajan, S., & Roth, S. (2020) · TUbilio (Technical University of Darmstadt) · doi:10.48550/arxiv.2011.00966 · Cited by 27

Towards Causal Benchmarking of Biasin Face Analysis Algorithms

Balakrishnan, G., Xiong, Y., Xia, W., & Perona, P. (2021) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-74697-1_15 · Cited by 27

T2IAT: Measuring Valence and Stereotypical Biases in Text-to-Image Generation

Wang, J., Liu, X., Di, Z., Liu, Y., & Wang, X. E. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.160 · Cited by 50

Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation

Yang, Y., Gupta, A., Feng, J., Singhal, P., Yadav, V., Wu, Y., Natarajan, P., Hedau, V., & Joo, J. (2022) · Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3514094.3534153 · Cited by 24

Attribute-Guided Adversarial Training for Robustness to Natural Perturbations

Gokhale, T., Anirudh, R., Kailkhura, B., Thiagarajan, J. J., Baral, C. R., & Yang, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i9.16927 · Cited by 25

How can Explainability Methods be Used to Support Bug Identification in Computer Vision Models?

Balayn, A., Rikalo, N., Lofi, C., Yang, J., & Bozzon, A. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517474 · Cited by 26

Gender Artifacts in Visual Datasets

Meister, N., Zhao, D., Wang, A., Ramaswamy, V., Fong, R., & Russakovsky, O. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00446 · Cited by 38

Reducing Sentiment Bias in Language Models via Counterfactual EvaluationPreprint

Huang, P.-S., Zhang, H., Jiang, R., Stanforth, R., Welbl, J., Rae, J. W., Maini, V., Yogatama, D., & Kohli, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.03064 · Cited by 98

Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance DetectionPreprint

Dulhanty, C., Deglint, J., Daya, I. B., & Wong, A. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.11951 · Cited by 38

MultiBench: Multiscale Benchmarks for Multimodal Representation Learning

Liang, P. P., Lyu, Y., Fan, X., Wu, Z., Cheng, Y., Wu, J., Chen, L., Wu, P., Lee, M. A., Zhu, Y., et al. (2021) · arXiv · doi:10.48550/arxiv.2107.07502 · Cited by 85

Explainability and vision foundation models: A survey

Kazmierczak, R., Berthier, É., Frehse, G., & Franchi, G. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103184 · Cited by 45

Discovering and Mitigating Visual Biases Through Keyword Explanation

Kim, Y., Mo, S., Kim, M., Lee, K., Lee, J., & Shin, J. (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/cvpr52733.2024.01054 · Cited by 46

Self-Supervised Test-Time Learning for Reading Comprehension

Banerjee, P., Gokhale, T., & Baral, C. R. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.95 · Cited by 30

U-Noise: Learnable Noise Masks for Interpretable Image Segmentation

Koker, T., Mireshghallah, F., Titcombe, T., & Kaissis, G. A. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506345 · Cited by 21

Computer Vision – ECCV 2018

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A study on the distribution of social biases in self-supervised learning visual models

Sirotkin, K., Carballeira, P., & Escudero-Viñolo, 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/cvpr52688.2022.01019 · Cited by 21

Inspecting the Geographical Representativeness of Images from Text-to-Image Models

Basu, A., Babu, R. V., & Pruthi, D. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00474 · Cited by 21

Make your data fair: A survey of data preprocessing techniques that address biases in data towards fair AI

Tawakuli, A., & Engel, T. (2024) · Journal of Engineering Research · doi:10.1016/j.jer.2024.06.016 · Cited by 21

How Do We Talk about Other People? Group (Un)Fairness in Natural Language Image Descriptions

Otterbacher, J., Barlas, P., Kleanthous, S., & Kyriakou, K. (2019) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v7i1.5267 · Cited by 20

The World of an Octopus: How Reporting Bias Influences a Language Model’s Perception of Color

Paik, C., Aroca-Ouellette, S., Roncone, A., & Kann, K. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.63 · Cited by 28

Tutorial on Multimodal Machine Learning

Morency, L., Liang, P. P., & Zadeh, A. (2022) · doi:10.18653/v1/2022.naacl-tutorials.5 · Cited by 20

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

Balancing out Bias: Achieving Fairness Through Balanced Training

Han, X., Baldwin, T., & Cohn, T. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.779 · Cited by 50

Estimating and Improving Fairness with Adversarial LearningPreprint

Li, X., Cui, Z., Wu, Y., Gu, L., & Harada, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.04243 · Cited by 20

DIME-FM : DIstilling Multimodal and Efficient Foundation Models

Sun, X., Zhang, P., Zhang, P., Shah, H., Saenko, K., & Xia, X. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01423 · Cited by 24

Partiality and Misconception: Investigating Cultural Representativeness in Text-to-Image Models

Zhang, L., Liao, X., Yang, Z., Gao, B., Wang, C., Yang, Q., & Li, D. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642877 · Cited by 34

Large language models (LLMs) as agents for augmented democracy

Gudiño, J., Grandi, U., & Hidalgo, C. A. (2024) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2024.0100 · Cited by 27

Attribute Aware Filter-Drop for Bias-Invariant Classification

Nagpal, S., Singh, M., Singh, R., & Mayank Vatsa, A. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00024 · Cited by 20

Data Efficient Masked Language Modeling for Vision and Language

Bitton, Y., Elhadad, M., Stanovsky, G., & Schwartz, R. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.259 · Cited by 20

On Guiding Visual Attention with Language Specification

Petryk, S., Dunlap, L., Nasseri, K., Gonzalez, J. E., Darrell, T., & Rohrbach, 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/cvpr52688.2022.01756 · Cited by 23

Human Consensus-Oriented Image Captioning

Wang, Z., Huang, Z. H., & Luo, Y. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/92 · Cited by 18

Augmented Human and Human-Machine Co-evolution: Efficiency and Ethics

Dengel, A. R., Devillers, L. Y., & Schaal, L. M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69128-8_13 · Cited by 18

Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image DatasetsPreprint

Dulhanty, C., & Wong, A. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.01347 · Cited by 40

Fair-VPT: Fair Visual Prompt Tuning for Image Classification

Park, S., & Byun, H. (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/cvpr52733.2024.01166 · Cited by 18

Benchmark suites instead of leaderboards for evaluating AI fairness

Wang, A., Hertzmann, A., & Russakovsky, O. (2024) · Patterns · doi:10.1016/j.patter.2024.101080 · Cited by 19

Gender Bias in Natural Language Processing and Computer Vision: A Comparative Survey

Bartl, M., Mandal, A., Leavy, S., & Little, S. (2024) · ACM Computing Surveys · doi:10.1145/3700438 · Cited by 23

Measuring and Improving BERT’s Mathematical Abilities by Predicting the Order of Reasoning.

Piękos, P., Malinowski, M., & Michalewski, H. (2021) · ReIn UW (University of Warsaw) · doi:10.18653/v1/2021.acl-short.49 · Cited by 10

SOAR: Scene-debiasing Open-set Action Recognition

Zhai, Y., Liu, Z., Wu, Z., Wu, Y., Zhou, C., Doermann, D., Yuan, J., & Hua, G. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00940 · Cited by 15

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

Feinglass, J., & Yang, Y. (2021) · doi:10.18653/v1/2021.acl-long.175 · Cited by 17

Learning credible DNNs via incorporating prior knowledge and model local explanation

Du, M., Liu, N., Yang, F., & Hu, X. (2020) · Knowledge and Information Systems · doi:10.1007/s10115-020-01517-5 · Cited by 17

Generating and leveraging explanations of AI/ML models in materials and manufacturing research

Braham, E. J., Ruddock, J. M., & Hardin, J. O. (2025) · Patterns · doi:10.1016/j.patter.2025.101340 · Cited by 17

Assessing Multilingual Fairness in Pre-trained Multimodal Representations

Wang, J., Liu, Y., & Wang, X. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.211 · Cited by 28

In-Group Bias in Deep Learning-Based Face Recognition Models Due to Ethnicity and Age

Nagpal, S., Singh, M., Singh, R., Mayank Vatsa, A., & Ratha, N. (2023) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2023.3241010 · Cited by 17

Understanding and evaluating harms of AI-generated image captions in political images

Sarhan, H., & Hegelich, S. (2023) · Frontiers in Political Science · doi:10.3389/fpos.2023.1245684 · Cited by 16

CNN-based Driver Activity Understanding: Shedding Light on Deep Spatiotemporal Representations

Roitberg, A., Haurilet, M., Reis, S., & Stiefelhagen, R. (2020) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc45102.2020.9294731 · Cited by 14

Explicit Image Caption Editing

Wang, Z., Chen, L., Ma, W., Han, G., Niu, Y., Shao, J., & Xiao, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20059-5_7 · Cited by 14

It’s About Time: A View of Crowdsourced Data Before and During the Pandemic

Christoforou, E., Barlas, P., & Otterbacher, J. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445317 · Cited by 14

LMCap: Few-shot Multilingual Image Captioning by Retrieval Augmented Language Model Prompting

Ramos, R., Martins, B., & Elliott, D. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.104 · Cited by 22

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