9 citations · 13 across the 5 of their papers we have counts for
5 papers
Vision-LLMs Can Fool Themselves with Self-Generated Typographic Attacks
Maan Qraitem, Nazia Tasnim, Piotr Teterwak +2
Typographic attacks, adding misleading text to images, can deceive vision-language models (LVLMs). The susceptibility of recent large LVLMs like GPT4-V to such attacks is understud…
From Fake to Real: Pretraining on Balanced Synthetic Images to Prevent Spurious Correlations in Image Recognition
Maan Qraitem, Kate Saenko, Bryan A. Plummer
Visual recognition models are prone to learning spurious correlations induced by a biased training set where certain conditions (\eg, Indoors) are over-represented in certain c…
Bias Mimicking: A Simple Sampling Approach for Bias Mitigation
Maan Qraitem, Kate Saenko, Bryan A. Plummer
Prior work has shown that Visual Recognition datasets frequently underrepresent bias groups (\eg Female) within class labels (\eg Programmers). This dataset bias can lead t…
From Coarse to Fine-grained Concept based Discrimination for Phrase Detection
Maan Qraitem, Bryan A. Plummer
Phrase detection requires methods to identify if a phrase is relevant to an image and localize it, if applicable. A key challenge for training more discriminative detection models…
Bridging the Gap: Machine Learning to Resolve Improperly Modeled Dynamics
Maan Qraitem, Dhanushka Kularatne, Eric Forgoston +1
We present a data-driven modeling strategy to overcome improperly modeled dynamics for systems exhibiting complex spatio-temporal behaviors. We propose a Deep Learning framework to…