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: Can multimodal encoder evolve when facing increasingly tough circumstances? Our work investigates this possibility in the context of continuous vision-language navigation (continuous VLN), ...

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

Sparse autoencoders (SAEs) have emerged as a powerful technique for extracting human-interpretable features from neural networks activations. Previous works compared different models based on ...

OpenAI's CLIP, released in early 2021, have long been the go-to choice of vision encoder for building multimodal foundation models. Although recent alternatives such as SigLIP have begun to challenge ...

Artificial intelligence is evolving into a new phase that more closely resembles human perception and interaction with the world. Multimodal AI enables systems to process and generate information ...