As large language models (LLMs) evolve into multimodal systems that can handle text, images, voice and code, they’re also becoming powerful orchestrators of external tools and connectors. With this ...
Researchers at the University of California, Los Angeles (UCLA), in collaboration with pathologists from Hadassah Hebrew ...
WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning BEIJING, Jan. 05, 2026––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the ...
ETRI, South Korea’s leading government-funded research institute, is establishing itself as a key research entity for ...
Researchers have proposed a unifying mathematical framework that helps explain why many successful multimodal AI systems work ...
1 Department of Computer Science, Western University, London, ON, Canada 2 Department of Biochemistry, Western University, London, ON, Canada This repository contains the code, datasets, and trained ...
Abstract: Text-to-image person re-identification aims to utilize textual descriptions to retrieve specific person images from large image databases. The core challenge of this task lies in the ...
A research team has developed a new model, PlantIF, that addresses one of the most pressing challenges in agriculture: the accurate and timely ...
Abstract: Remote sensing cross-modal text-image retrieval (RSCTIR) has emerged as a fundamental task in remote sensing analysis, aiming to bridge the semantic gap between visual and textual modalities ...
For most of photography’s roughly 200-year history, altering a photo convincingly required either a darkroom, some Photoshop expertise, or, at minimum, a steady hand with scissors and glue. On Tuesday ...
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