UniBrain: Unify Image Reconstruction and Captioning All in One Diffusion Model from Human Brain Activity
This work addresses the challenge of decoding visual stimuli from brain activity for neuroscience research, representing an incremental advance by unifying tasks in a single model.
The authors tackled the problem of reconstructing images and generating captions from human brain activity (fMRI) by proposing UniBrain, a unified diffusion model that outperforms current methods in image reconstruction and reports first-time captioning results on the NSD dataset.
Image reconstruction and captioning from brain activity evoked by visual stimuli allow researchers to further understand the connection between the human brain and the visual perception system. While deep generative models have recently been employed in this field, reconstructing realistic captions and images with both low-level details and high semantic fidelity is still a challenging problem. In this work, we propose UniBrain: Unify Image Reconstruction and Captioning All in One Diffusion Model from Human Brain Activity. For the first time, we unify image reconstruction and captioning from visual-evoked functional magnetic resonance imaging (fMRI) through a latent diffusion model termed Versatile Diffusion. Specifically, we transform fMRI voxels into text and image latent for low-level information and guide the backward diffusion process through fMRI-based image and text conditions derived from CLIP to generate realistic captions and images. UniBrain outperforms current methods both qualitatively and quantitatively in terms of image reconstruction and reports image captioning results for the first time on the Natural Scenes Dataset (NSD) dataset. Moreover, the ablation experiments and functional region-of-interest (ROI) analysis further exhibit the superiority of UniBrain and provide comprehensive insight for visual-evoked brain decoding.