5 papers
Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions
Saurav Sengupta, Nazanin Moradinasab, Jiebei Liu +1
Recent research suggests that Vision Language Models (VLMs) often rely on inherent biases learned during training when responding to queries about visual properties of images. Thes…
Examining Vision Language Models through Multi-dimensional Experiments with Vision and Text Features
Saurav Sengupta, Nazanin Moradinasab, Jiebei Liu +1
Recent research on Vision Language Models (VLMs) suggests that they rely on inherent biases learned during training to respond to questions about visual properties of an image. The…
Towards Robust Multimodal Representation: A Unified Approach with Adaptive Experts and Alignment
Nazanin Moradinasab, Saurav Sengupta, Jiebei Liu +2
Healthcare relies on multiple types of data, such as medical images, genetic information, and clinical records, to improve diagnosis and treatment. However, missing data is a commo…
GenGMM: Generalized Gaussian-Mixture-based Domain Adaptation Model for Semantic Segmentation
Nazanin Moradinasab, Hassan Jafarzadeh, Donald E. Brown
Domain adaptive semantic segmentation is the task of generating precise and dense predictions for an unlabeled target domain using a model trained on a labeled source domain. While…
ProtoGMM: Multi-prototype Gaussian-Mixture-based Domain Adaptation Model for Semantic Segmentation
Nazanin Moradinasab, Laura S. Shankman, Rebecca A. Deaton +2
Domain adaptive semantic segmentation aims to generate accurate and dense predictions for an unlabeled target domain by leveraging a supervised model trained on a labeled source do…