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Found 12 papers, 5 papers with code
Improving Text-to-Image Consistency via Automatic Prompt Optimization
In this paper, we address these challenges and introduce a T2I optimization-by-prompting framework, OPT2I, which leverages a large language model (LLM) to improve prompt-image consistency in T2I models.
Quantifying and mitigating the impact of label errors on model disparity metrics
We empirically assess the proposed approach on a variety of datasets and find significant improvement, compared to alternative approaches, in identifying training inputs that improve a model's disparity metric.
VPA: Fully Test-Time Visual Prompt Adaptation
Inspired by the success of textual prompting, several studies have investigated the efficacy of visual prompt tuning.
FACET: Fairness in Computer Vision Evaluation Benchmark
We present a new benchmark named FACET (FAirness in Computer Vision EvaluaTion), a large, publicly available evaluation set of 32k images for some of the most common vision tasks - image classification, object detection and segmentation.
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity
The unprecedented photorealistic results achieved by recent text-to-image generative systems and their increasing use as plug-and-play content creation solutions make it crucial to understand their potential biases.
Pinpointing Why Object Recognition Performance Degrades Across Income Levels and Geographies
As an example, we show that mitigating a model's vulnerability to texture can improve performance on the lower income level.
Towards Reliable Assessments of Demographic Disparities in Multi-Label Image Classifiers
These metrics successfully incentivized performance improvements on person-centric tasks such as face analysis and are used to understand risks of modern models.
Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparities
With these capabilities in mind, we ask: Do vision-language models exhibit gender bias when performing zero-shot image classification, object detection and semantic segmentation?
"I'm sorry to hear that": Finding New Biases in Language Models with a Holistic Descriptor Dataset
As language models grow in popularity, it becomes increasingly important to clearly measure all possible markers of demographic identity in order to avoid perpetuating existing societal harms.
Understanding out-of-distribution accuracies through quantifying difficulty of test samples
Existing works show that although modern neural networks achieve remarkable generalization performance on the in-distribution (ID) dataset, the accuracy drops significantly on the out-of-distribution (OOD) datasets \cite{recht2018cifar, recht2019imagenet}.
A Systematic Study of Bias Amplification
To enable this study, we design a simple image-classification problem in which we can tightly control (synthetic) biases.
Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems
We discuss how we disentangle normative questions of product and policy design (like, "how should the system trade off between different stakeholders' interests and needs?")
