According to DeepLearning.AI, researchers have introduced Sample-Efficient Modality Integration (SEMI), a framework that enables any pretrained encoder—covering images, audio, video, sensors, and ...

With the great success of large language models, self-supervised pre-training technologies have shown the great promise in the field of drug discovery. In particular, multimodal pre-training models ...

Ray's innovative disaggregated hybrid parallelism significantly enhances multimodal AI training efficiency, achieving up to 1.37x throughput improvement and overcoming memory challenges. In a ...

Visual tokens consume substantial computational resources in multi-modal large models (MLLMs), significantly compromising their efficiency. Recent works have attempted to improve efficiency by ...

What if artificial intelligence could see, read, and understand the world as seamlessly as humans do? Imagine an AI capable of analyzing a complex image, generating a detailed description, and ...

ABSTRACT: This work presents an innovative Intrusion Detection System (IDS) for Edge-IoT environments, based on an unsupervised architecture combining LSTM networks and Autoencoders. Deployed on ...

Abstract: Recent contrastive multimodal vision-language models like CLIP have demonstrated robust open-world semantic understanding, becoming the standard image backbones for vision-language ...