Upload README.md

README.md

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+# [ECCV 2024] Enhancing Cross-Subject fMRI-to-Video Decoding with Global-Local Functional Alignment
+
+## Introduction
+fMRI-video dataset, 8 subjects (6 male and 2 female, aged 23-27, 3 for FCVID and 5 for WebVid) participated, and fMRI data are acquired using a 3T scanner and a 32-channel RF head coil, with the fMRI sampled at 1 frame per 0.8 seconds. 
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+In detail, stimuli videos of dimensions 256$\times$256 and 596$\times$336 are sourced from the FCVID video dataset and WebVid dataset respectively.
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+Within the FCVID dataset, a selection of 100 video categories, encompassing diverse events, scenes, and objects (e.g., accordion performances, amusement parks, and elephants), is chosen. For each category, 4 10-second clips are meticulously selected, with 3 assigned to the training set and 1 to the test set. Random combinations of these clips result in 6 500-second videos for both training and test sets, ensuring each training clip appeared once and each test clip appeared three times.
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+For WebVid dataset in text-to-video synthesis, we curated a selection of 1200 8-second clips. Subsequently, 5 subjects are tasked with viewing these videos in random order, with 1000 clips designated for the training set and the remaining 200 clips allocated to the test set. Videos from test set are viewed twice to reduce noise,  exclusively originate from the WebVid test set. Additionally, in between consecutive clips, a 2.4-second blank interval is introduced to allow the participants to clear their minds, thereby reducing the overlap of neural activity encoding information from different videos.
+
+## Repository Structure
+
+- **fd-fmri-video-FCVID/WebVid**
+  - **raw-data**:  Raw fMRI data collected directly from the imaging machine.
+  - **processed-fmri**: We utilized fMRIPrep and the methodologies described in our paper to derive and store the data in .h5 format.
+  - **stimuli**: All videos shown to subjects during the fMRI scans and corresponding caption.
+