Computer vision models have known performance disparities across attributes such as
gender and skin tone. This means during tasks such as classification and detection, model …

Machine learning models have been shown to inherit biases from their training datasets.
This can be particularly problematic for vision-language foundation models trained on …

Bias amplification is a phenomenon in which models increase imbalances present in the
training data. In this paper, we study bias amplification in the text-to-image domain using …

We address the challenge of generating fair and unbiased image retrieval results given
neutral textual queries (with no explicit gender or race connotations), while maintaining the …

We investigate the impact of deep generative models on potential social biases in upcoming
computer vision models. As the internet witnesses an increasing influx of AI-generated …

We study cultural and socioeconomic diversity in contrastive vision-language models
(VLMs). Using a broad range of benchmark datasets and evaluation metrics, we bring to …

Large Vision-Language Models (LVLMs) have shown significant progress in well
responding to visual-instructions from users. However, these instructions, encompassing …

Following on recent advances in large language models (LLMs) and subsequent chat
models, a new wave of large vision-language models (LVLMs) has emerged. Such models …

While vision-language models (VLMs) have achieved remarkable performance
improvements recently there is growing evidence that these models also posses harmful …

Large Language Models (LLMs) have demonstrated remarkable success across various
domains. However, despite their promising performance in numerous real-world …