Modality-agnostic decoders leverage modality-invariant representations in human subjects' brain activity to predict stimuli irrespective of their modality (image, text, mental imagery).

Abstract: Accurate segmentation of pulmonary infection regions is critical for diagnosing respiratory diseases such as COVID-19 and pneumonia. Although recent deep learning approaches have achieved ...

Summary: Meta’s Fundamental AI Research team has unveiled TRIBE, a groundbreaking foundation model designed to predict how the human brain processes visual and auditory stimuli. Trained on massive ...

Spec-Bench is a comprehensive benchmark designed for assessing Speculative Decoding methods across diverse scenarios. Based on Spec-Bench, we aim to establish and maintain a unified evaluation ...

Abstract: In deep learning-based dehazing strategies, attention mechanisms are widely used to refine feature representations and improve overall performance. However, conventional contextual attention ...