4 papers
GeoDiv: Framework For Measuring Geographical Diversity In Text-To-Image Models
Abhipsa Basu, Mohana Singh, Shashank Agnihotri +2
Text-to-image (T2I) models are rapidly gaining popularity, yet their outputs often lack geographical diversity, reinforce stereotypes, and misrepresent regions. Given their broad r…
Where Do Images Come From? Analyzing Captions to Geographically Profile Datasets
Abhipsa Basu, Yugam Bahl, Kirti Bhagat +3
Recent studies show that text-to-image models often fail to generate geographically representative images, raising concerns about the representativeness of their training data and…
Harnessing Diffusion-Generated Synthetic Images for Fair Image Classification
Abhipsa Basu, Aviral Gupta, Abhijnya Bhat +1
Image classification systems often inherit biases from uneven group representation in training data. For example, in face datasets for hair color classification, blond hair may be…
Balancing Act: Distribution-Guided Debiasing in Diffusion Models
Rishubh Parihar, Abhijnya Bhat, Abhipsa Basu +3
Diffusion Models (DMs) have emerged as powerful generative models with unprecedented image generation capability. These models are widely used for data augmentation and creative ap…