This comprehensive survey explored the evolving landscape of generative Artificial
Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts …

We present the latest generation of MobileNets: MobileNetV4 (MNv4). They feature
universally-efficient architecture designs for mobile devices. We introduce the Universal …

Abstract Recently lightweight Vision Transformers (ViTs) demonstrate superior performance
and lower latency compared with lightweight Convolutional Neural Networks (CNNs) on …

Segment anything model (SAM) is a prompt-guided vision foundation model for cutting out
the object of interest from its background. Since Meta research team released the SA project …

U-Net has become a cornerstone in various visual applications such as image segmentation
and diffusion probability models. While numerous innovative designs and improvements …

Abstract Segment Anything Model (SAM) has emerged as a powerful tool for numerous
vision applications. A key component that drives the impressive performance for zero-shot …

Large models based on the Transformer architecture play increasingly vital roles in artificial
intelligence, particularly within the realms of natural language processing (NLP) and …

We present a foundation model for zero-shot metric monocular depth estimation. Our model,
Depth Pro, synthesizes high-resolution depth maps with unparalleled sharpness and high …

Contrastive pre-training of image-text foundation models such as CLIP demonstrated
excellent zero-shot performance and improved robustness on a wide range of downstream …

Abstract Unmanned Aerial Vehicle (UAV) remote sensing object recognition plays a vital
role in a variety of sectors including military, agriculture, forestry, and construction. Accurate …