MindVLM: Neural-Grounded Visual Captioning via Subject-Aware Semantic Evidence Selection

Abstract

Translating visual brain activity into natural language offers a powerful window into human cognition and neural semantic representation. Most prior brain-captioning approaches project fMRI signals into language space using paired supervision. However, the widely used Natural Scenes Dataset (NSD) was built based only on image-fMRI pairs, while existing methods rely on MS-COCO captions as indirect textual supervision. Critically, during fMRI acquisition, participants observe only cropped image regions, whereas the associated MS-COCO captions describe full scenes. This mismatch creates a fundamental gap between neural representations and textual supervision, introducing an inherent semantic loss in NSD-based brain captioning. To mitigate this limitation, we reformulate brain captioning as Neural-Grounded Visual Captioning, where language is generated directly from visually grounded neural evidence rather than text supervision. We propose MindVLM, a framework that maps fMRI signals into subject-aware visual tokens and leverages a frozen vision language model (VLM) to produce interpretable and personalized descriptions. Instead of point-wise regression, MindVLM focuses on extracting semantically relevant visual cues from neural activity, naturally accommodating partial observability and inter-subject variability. Extensive experiments on NSD show that MindVLM consistently improves caption quality across multiple VLM backbones, including LLaVA-v1.5, Qwen2.5-VL, and Gemma3, beyond intrinsic model priors. Despite requiring neither text supervision nor an LLM during training and operating under an image-grounded inference paradigm distinct from conventional captioning methods, MindVLM remains competitive with supervised approaches, suggesting that subject-specific neural grounding provides an efficient, scalable, and interpretable path toward faithful brain-to-language generation.