3 citations · 3 across the 3 of their papers we have counts for
5 papers
Towards Algorithmic Fidelity: Mental Health Representation across Demographics in Synthetic vs. Human-generated Data
Shinka Mori, Oana Ignat, Andrew Lee +1
Synthetic data generation has the potential to impact applications and domains with scarce data. However, before such data is used for sensitive tasks such as mental health, we nee…
Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Cost
Oana Ignat, Longju Bai, Joan Nwatu +1
Current foundation models have shown impressive performance across various tasks. However, several studies have revealed that these models are not effective for everyone due to the…
Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Models
Joan Nwatu, Oana Ignat, Rada Mihalcea
Despite the impressive performance of current AI models reported across various tasks, performance reports often do not include evaluations of how these models perform on the speci…
Augment the Pairs: Semantics-Preserving Image-Caption Pair Augmentation for Grounding-Based Vision and Language Models
Jingru Yi, Burak Uzkent, Oana Ignat +4
Grounding-based vision and language models have been successfully applied to low-level vision tasks, aiming to precisely locate objects referred in captions. The effectiveness of g…
Scalable Performance Analysis for Vision-Language Models
Santiago Castro, Oana Ignat, Rada Mihalcea
Joint vision-language models have shown great performance over a diverse set of tasks. However, little is known about their limitations, as the high dimensional space learned by th…