A multimodal EEG-image-text contrastive framework with dynamic gradient balancing and stochastic noise improves zero-shot object recognition from EEG on ThingsEEG, raising top-1 accuracy from 13.8% to 15.8%.
Decoding visual neural representations by multimodal learning of brain-visual-linguistic features,
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Decoding Visual Neural Representations by Multimodal with Dynamic Balancing
A multimodal EEG-image-text contrastive framework with dynamic gradient balancing and stochastic noise improves zero-shot object recognition from EEG on ThingsEEG, raising top-1 accuracy from 13.8% to 15.8%.