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

Abstract: Infrared small target detection (ISTD) faces significant challenges in effectively utilizing shallow and deep features while mitigating spatial detail degradation during sampling. To address ...

MOOZY is a foundation model for computational pathology that treats the patient case, not the individual slide, as the fundamental unit of representation. It encodes one or more whole-slide images ...

Hallucination is one of the most critical obstacles to reliably deploying Large Vision-Language Models (LVLMs): the model produces fluent, confident text that is factually inconsistent with what is ...

Abstract: The present portable communication devices need high speed data transmission to support different interfaces and display technologies. These communication devices transmit data between ...

Neuroscience has long been a field of divide and conquer. Researchers typically map specific cognitive functions to isolated brain regions—like motion to area V5 or faces to the fusiform gyrus—using ...

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 ...