{"total":30,"rows":[{"id":"2309.00035","title":"FACET: Fairness in Computer Vision Evaluation Benchmark","created":"2023-08-31","cited_by_count":58,"area":null},{"id":"2304.02828","title":"Uncurated Image-Text Datasets: Shedding Light on Demographic Bias","created":"2023-04-06","cited_by_count":52,"area":null},{"id":"2312.01261","title":"TIBET: Identifying and Evaluating Biases in Text-to-Image Generative Models","created":"2023-12-03","cited_by_count":52,"area":null},{"id":"2212.08158","title":"MM-SHAP: A Performance-agnostic Metric for Measuring Multimodal Contributions in Vision and Language Models & Tasks","created":"2022-12-15","cited_by_count":43,"area":null},{"id":"2209.15605","title":"Bias Mimicking: A Simple Sampling Approach for Bias Mitigation","created":"2022-09-30","cited_by_count":30,"area":null},{"id":"2308.11606","title":"StoryBench: A Multifaceted Benchmark for Continuous Story Visualization","created":"2023-08-22","cited_by_count":29,"area":null},{"id":"2305.07558","title":"Measuring Progress in Fine-grained Vision-and-Language Understanding","created":"2023-05-12","cited_by_count":25,"area":null},{"id":"2305.09073","title":"Consensus and Subjectivity of Skin Tone Annotation for ML Fairness","created":"2023-05-16","cited_by_count":22,"area":null},{"id":"2210.11924","title":"Men Also Do Laundry: Multi-Attribute Bias Amplification","created":"2022-10-21","cited_by_count":21,"area":null},{"id":"2402.01787","title":"Harm Amplification in Text-to-Image Models","created":"2024-02-01","cited_by_count":15,"area":null},{"id":"2412.01876","title":"Understanding Bias in Large-Scale Visual Datasets","created":"2024-12-02","cited_by_count":15,"area":null},{"id":"2408.02464","title":"Fairness and Bias Mitigation in Computer Vision: A Survey","created":"2024-08-05","cited_by_count":13,"area":null},{"id":"2308.04553","title":"From Fake to Real: Pretraining on Balanced Synthetic Images to Prevent Spurious Correlations in Image Recognition","created":"2023-08-08","cited_by_count":12,"area":null},{"id":"2308.01948","title":"A Multidimensional Analysis of Social Biases in Vision Transformers","created":"2023-08-03","cited_by_count":8,"area":null},{"id":"2508.17416","title":"Data Leakage in Visual Datasets","created":"2025-08-24","cited_by_count":7,"area":null},{"id":"2305.14281","title":"Weakly-Supervised Learning of Visual Relations in Multimodal Pretraining","created":"2023-05-23","cited_by_count":6,"area":null},{"id":"2303.07615","title":"Variation of Gender Biases in Visual Recognition Models Before and After Finetuning","created":"2023-03-14","cited_by_count":3,"area":null},{"id":"2503.04666","title":"What Are You Doing? A Closer Look at Controllable Human Video Generation","created":"2025-03-06","cited_by_count":3,"area":null},{"id":"2406.00219","title":"Fairness in Autonomous Driving: Towards Understanding Confounding Factors in Object Detection under Challenging Weather","created":"2024-05-31","cited_by_count":2,"area":null},{"id":"2406.06407","title":"A Taxonomy of Challenges to Curating Fair Datasets","created":"2024-06-10","cited_by_count":2,"area":null},{"id":"2503.00020","title":"A Systematic Review of Open Datasets Used in Text-to-Image (T2I) Gen AI Model Safety","created":"2025-02-23","cited_by_count":2,"area":null},{"id":"2503.09763","title":"BiasConnect: Investigating Bias Interactions in Text-to-Image Models","created":"2025-03-12","cited_by_count":2,"area":null},{"id":"2508.17750","title":"From Global to Local: Social Bias Transfer in CLIP","created":"2025-08-25","cited_by_count":2,"area":null},{"id":"2503.07878","title":"A Woman with a Knife or A Knife with a Woman? Measuring Directional Bias Amplification in Image Captions","created":"2025-03-10","cited_by_count":1,"area":null},{"id":"2606.05829","title":"Gender Artifacts from Art History to Text-to-Image Generation","created":"2026-06-04","cited_by_count":1,"area":null},{"id":"2307.03274","title":"It is not Sexually Suggestive, It is Educative. Separating Sex Education from Suggestive Content on TikTok Videos","created":"2023-07-06","cited_by_count":0,"area":null},{"id":"2404.11819","title":"Utilizing Adversarial Examples for Bias Mitigation and Accuracy Enhancement","created":"2024-04-18","cited_by_count":0,"area":null},{"id":"2505.17841","title":"TEDI: Trustworthy and Ethical Dataset Indicators to Analyze and Compare Dataset Documentation","created":"2025-05-23","cited_by_count":0,"area":null},{"id":"2603.05582","title":"Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models","created":"2026-03-05","cited_by_count":0,"area":null},{"id":"2608.05424","title":"Invisible Shortcuts: Why Vision Encoders Know Your Camera","created":"2026-08-05","cited_by_count":0,"area":null}],"next_cursor":null}