1 citations · 1 across the 7 of their papers we have counts for
8 papers
Imaginative Perception Tokens Enhance Spatial Reasoning in Multimodal Language Models
Mahtab Bigverdi, Linjie Li, Weikai Huang +8
Vision language models (VLMs) excel at many tasks but still struggle with spatial reasoning when critical information is not directly observable. Many such problems require imagina…
Ablate-to-Validate: Are Vision-Language Models Really Using Continuous Thought Tokens?
Tianyi Zhang, Mahtab Bigverdi, Ranjay Krishna
Vision-language models (VLMs) are increasingly augmented with continuous or latent non-textual tokens intended to support "visual thinking." Despite improved task accuracy, this al…
MedBLINK: Probing Basic Perception in Multimodal Language Models for Medicine
Mahtab Bigverdi, Wisdom Ikezogwo, Kevin Zhang +5
Multimodal language models (MLMs) show promise for clinical decision support and diagnostic reasoning, raising the prospect of end-to-end automated medical image interpretation. Ho…
Unfolding Spatial Cognition: Evaluating Multimodal Models on Visual Simulations
Linjie Li, Mahtab Bigverdi, Jiawei Gu +5
Spatial cognition is essential for human intelligence, enabling problem-solving through visual simulations rather than solely relying on verbal reasoning. However, existing AI benc…
Perception Tokens Enhance Visual Reasoning in Multimodal Language Models
Mahtab Bigverdi, Zelun Luo, Cheng-Yu Hsieh +4
Multimodal language models (MLMs) still face challenges in fundamental visual perception tasks where specialized models excel. Tasks requiring reasoning about 3D structures benefit…
Gene-Level Representation Learning via Interventional Style Transfer in Optical Pooled Screening
Mahtab Bigverdi, Burkhard Hockendorf, Heming Yao +3
Optical pooled screening (OPS) combines automated microscopy and genetic perturbations to systematically study gene function in a scalable and cost-effective way. Leveraging the re…
Data Alignment for Zero-Shot Concept Generation in Dermatology AI
Soham Gadgil, Mahtab Bigverdi
AI in dermatology is evolving at a rapid pace but the major limitation to training trustworthy classifiers is the scarcity of data with ground-truth concept level labels, which are…
MIMIC: Masked Image Modeling with Image Correspondences
Kalyani Marathe, Mahtab Bigverdi, Nishat Khan +7
Dense pixel-specific representation learning at scale has been bottlenecked due to the unavailability of large-scale multi-view datasets. Current methods for building effective pre…