Brain-IT-VQA: From Brain Signals to Answers
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 significant progress has been made in recent years in visual question answering (VQA) from fMRI, performance remains limited. Moreover, although recent models can make increasingly accurate predictions, they have rarely been used as tools for understanding the structure of visual representations in the brain. We present Brain-IT-VQA, a framework for visual question answering from fMRI. Unlike previous methods, which extract a fixed representation from the fMRI signal, our extraction is conditioned on the question it...
| Authors | Roman Beliy, Matias Cosarinsky, Oliver Heinimann et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 May 2026 |
| Last updated at source | 2 October 2026 |
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