Maan Qraitem 🌲
Hello! I am a PhD student in the Image and Video Computing (IVC) Group at Boston University, advised by Professor Kate Saenko and Professor Bryan A. Plummer. I am broadly interested in uncovering and mitigating blindspots in data, training metrics, and modeling assumptions.
I've recently been interested in geospatial deep learning. I am really excited about where the field is at! If you do something similar and you're interested in collaborating, please reach out!
Selected Publications
Spurious Correlations in Vision-Language Models
- Maan Qraitem, Piotr Teterwak, Kate Saenko, Bryan A. Plummer. Web Artifact Attacks Disrupt Vision Language Models. ICCV 2025. [PDF] [Code]
- Maan Qraitem, Nazia Tasnim, Piotr Teterwak, Kate Saenko, Bryan A. Plummer. Vision-LLMs Can Fool Themselves with Self-Generated Typographic Attacks. NeurIPS 2024 Workshop. [PDF] [Code]
- Maan Qraitem, Kate Saenko, Bryan A. Plummer. From Fake to Real: Pretraining on Balanced Synthetic Images to Prevent Spurious Correlations. ECCV 2024. (Oral) [PDF] [Code]
- Maan Qraitem, Kate Saenko, Bryan A. Plummer. Bias Mimicking: A Simple Sampling Approach for Bias Mitigation. CVPR 2023. [PDF] [Code]
Geospatial & Earth Observation AI
- Paulo A Arevalo, Maan Qraitem, Sujit Roy. Detecting Key Land Surface Phenometrics with the Prithvi Foundation Model and Harmonized Landsat Sentinel-2 Data. AGU 2025. [Code]
- Prithvi-EO: An Open-Access Geospatial Foundation Model Advancing Earth Science through Global Collaboration. AGU 2025. [40+ authors; contributed model adaptation pipelines for phenology forecasting and mining detection.]
Evaluation & Reasoning in Multimodal Models
- Eunice Yiu, Maan Qraitem, Charlie Wong, Anisa Noor Majhi, Yutong Bai, Shiry Ginosar, Alison Gopnik, Kate Saenko. KiVA: Kid-inspired Visual Analogies for Testing Large Multimodal Models. ICLR 2025. [PDF] [Code]
- Maan Qraitem, Kate Saenko, Bryan A. Plummer. Breaking the Assistant Mold: Modeling Behavioral Variation in LLM Based Procedural Character Generation. In Submission. [PDF] [Code]
Physics Informed Deep Learning
- Maan Qraitem, Dhanushka Kularatne, Eric Forgoston and M Ani Hsieh Bridging the gap: Machine learning to resolve improperly modeled dynamics. Physica D Journal 2019. [PDF]
- Bruce A Maxwell, Casey A Smith, Maan Qraitem, Ross Messing, Spencer Whitt, Nicolas Thien, Richard M Friedhoff Real-time physics-based removal of shadows and shading from road surfaces. CVPR Workshop 2019. [PDF]