9 papers
Brain-IT-VQA: From Brain Signals to Answers
Roman Beliy, Matias Cosarinsky, Oliver Heinimann +2
Decoding visual content from fMRI signals recorded while a person views images, and specifically answering questions about the seen images, is a long-standing challenge. While sign…
BrainExplore: Large-Scale Discovery of Interpretable Visual Representations in the Human Brain
Navve Wasserman, Matias Cosarinsky, Yuval Golbari +4
Understanding how the human brain represents visual concepts, and in which brain regions these representations are encoded, remains a long-standing challenge. Decades of work have…
The Wisdom of a Crowd of Brains: A Universal Brain Encoder
Roman Beliy, Navve Wasserman, Amit Zalcher +1
Image-to-fMRI encoding is important for both neuroscience research and practical applications. However, such "Brain-Encoders" have been typically trained per-subject and per fMRI-d…
From Activation to Specificity: Automating Counterfactual Testing of Visual Representations in the Human Brain
Yuval Golbari, Navve Wasserman, Matias Cosarinsky +5
Identifying which brain regions represent a visual concept in the human brain is a central challenge in neuroscience. Existing approaches have localized coarse functional regions (…
Brain-IT: Image Reconstruction from fMRI via Brain-Interaction Transformer
Roman Beliy, Amit Zalcher, Jonathan Kogman +2
Reconstructing images seen by people from their fMRI brain recordings provides a non-invasive window into the human brain. Despite recent progress enabled by diffusion models, curr…
ImpMIA: Leveraging Implicit Bias for Membership Inference Attack
Yuval Golbari, Navve Wasserman, Gal Vardi +1
Determining which data samples were used to train a model, known as Membership Inference Attack (MIA), is a well-studied and important problem with implications on data privacy. So…