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Wills Aligner: A Robust Multi-Subject Brain Representation Learner
We meticulously evaluate the performance of our approach across coarse-grained and fine-grained visual decoding tasks.
MindTuner: Cross-Subject Visual Decoding with Visual Fingerprint and Semantic Correction
Decoding natural visual scenes from brain activity has flourished, with extensive research in single-subject tasks and, however, less in cross-subject tasks.
Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrast
In real-world scenarios, multimodal federated learning often faces the practical challenge of intricate modality missing, which poses constraints on building federated frameworks and significantly degrades model inference accuracy.
Lite-Mind: Towards Efficient and Robust Brain Representation Network
The limited data availability and the low signal-to-noise ratio of fMRI signals lead to the challenging task of fMRI-to-image retrieval.
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