BI-Cap emulates human visual processing via neuromimetic transformations and an evidence-driven latent space to outperform prior methods on zero-shot brain-to-image retrieval by 9.2% and 8.0% on two benchmarks.
Vieeg: Hierarchical visual neural representation for eeg brain decoding
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MB2L achieves 80.5% top-1 and 97.6% top-5 accuracy on zero-shot EEG-to-image retrieval by using biomimetic modules and bidirectional contrastive learning to align neural and visual features.
citing papers explorer
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Brain-Inspired Capture: Evidence-Driven Neuromimetic Perceptual Simulation for Visual Decoding
BI-Cap emulates human visual processing via neuromimetic transformations and an evidence-driven latent space to outperform prior methods on zero-shot brain-to-image retrieval by 9.2% and 8.0% on two benchmarks.
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Multi-Level Bidirectional Biomimetic Learning for EEG-Based Visual Decoding
MB2L achieves 80.5% top-1 and 97.6% top-5 accuracy on zero-shot EEG-to-image retrieval by using biomimetic modules and bidirectional contrastive learning to align neural and visual features.