在目标检测领域，小样本目标检测（Few-Shot Object Detection, FSOD）一直是个“硬骨头”。传统的做法通常需要在大规模基类数据上预训练，再针对极少数的新类样本进行微调。但微调过程不仅耗时，还容易导致模型对新类样本过拟合。近日，来自澳门大学和英特灵达的研究团队提出了一种全新的框架—— FSOD-VFM 。

This figure illustrates the difference between digital and physical attacks in the context of remote sensing. It is observed that: • For physical attacks, the attacker manipulates either the actual ...

This article is part of our coverage of the latest in AI research. A new machine learning technique developed by researchers at Edge Impulse, a platform for creating ML models for the edge, makes it ...

The object detection required for machine vision applications such as autonomous driving, smart manufacturing, and surveillance applications depends on AI modeling. The goal now is to improve the ...

AI detects objects in images by using computer vision techniques that analyze the visual features of an image. The process typically involves using a convolutional neural network (CNN) to identify ...

AVENTURA, Fla.--(BUSINESS WIRE)--Safe Pro Group Inc. (Nasdaq: SPAI) (“Safe Pro” or the “Company”), a leading provider of artificial intelligence (AI) solutions specializing in drone imagery processing ...

What if you could teach a computer to recognize a zebra without ever showing it one? Imagine a world where object detection isn’t bound by the limits of endless training data or high-powered hardware.