Vision Transformers News Today

Data on the company’s portable MR imaging system show it can acquire brain images at the point of care that may assist physicians with screening and monitoring for Alzheimer’s and multiple...

Automatic detection of agricultural pests is a challenging problem that is of great interest in biosecurity and precision agriculture. The detection model must cope well with the dense distribution of small-sized pests in complex backgrounds. This paper proposes a lightweight attention-based network, called TP-YOLO, for tiny pest detection. We introduce two attention-based components, namely Contextual Transformer and Omni-Dimensional Dynamic Convolution modules, to enhance feature extraction. T...

Every Friday at Oxen.ai we host a paper club called "Arxiv Dives" to make us smarter Oxen �� ��. We believe diving into the details of research papers is the best way to build fundamental knowledge and keep up with the bleeding edge. If you would like to join the discussion

/PRNewswire/ -- Continued innovation, broad use in a variety of applications, ethical issues, and increased efficiency characterise the Vision Transformers...

ConvNets Match Vision Transformers at Scale

BrainChip has announced the Early Access availability of its second-generation Akida IP solution for use in a wide range of applications across the Smart Home, Smart City, Industrial and Automotive markets.

LAGUNA HILLS, Calif.--(BUSINESS WIRE)--BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power, fully digital, event-based, neuromorphic

A deep dive into Transformer a neural network architecture that was introduced in the famous paper “attention is all you need” in 2017, its applications, impacts, challenges and future directions

Transformers were first introduced by the team at Google Brain in 2017 in their paper, “Attention is All You Need“. Since their introduction, transformers ...

Vision transformers (ViTs) are powerful artificial intelligence (AI) technologies that can identify or categorize objects in images -- however, there are significant challenges related to both computing power requirements and decision-making transparency. Researchers have now developed a new methodology that addresses both challenges, while also improving the ViTs ability to identify, classify and segment objects in images.