AI Technology & Industry Review
In the new paper An Improved One millisecond Mobile Backbone, an Apple research team presents MobileOne, a novel mobile backbone that cuts inference time to under one millisecond on an iPhone12 and reaches 75.9 percent top-1 accuracy on ImageNet.
A research team from University of California San Diego and Microsoft proposes Micro-Factorized Convolution (MF-Conv), a novel approach that can deal with extremely low computational costs (4M–21M FLOPs) and achieves significant performance gains over state of the art models in the low FLOP regime.
ICLR 2021 paper An Image Is Worth 16×16 Words: Transformers for Image Recognition at Scale suggests Transformers can outperform top CNNs on CV at scale.
Image recognition technology in particular has come to play a valuable role in wildlife conservation, where endangered species are tallied and tracked so their numbers and migrations can be more accurately measured and understood.
In a bid to combine anime and cartoon culture with machine learning, a research team from China’s leading video streaming service iQIYI has introduced a novel large unconstrained cartoon dataset they call “iCartoonFace.”
Neural networks for image recognition have matured from simple chain-like models to structures with multiple wiring paths. The emergence of Neural Architecture Search (NAS) can optimize models with more elaborate wiring and operation types.
Researchers from Beijing-based AI unicorn SenseTime and Nanyang Technological University have trained ImageNet/AlexNet in a record-breaking 1.5 minutes, a significant 2.6 times speedup over the previous record of 4 minutes.
CUHK researchers recently teamed up with Chinese AI giant SenseTime to develop a greatly improved iteration in DeepFashion2, a large-scale benchmark with comprehensive tasks and annotations of fashion image understanding.
Text-based CAPTCHA remain one of the most visible and commonly used mechanisms for website security. As a sort of online gatekeeper that distinguishes between humans and bots, the little solvable image fields have critical commercial applications in blocking automatic spam and preventing e-transfer fraud; and can also stop bots from spreading fraudulent information, etc.
At last month’s RE•WORK Deep Learning in Finance Summit in London, leading AI industry practitioners and academics from prestigious universities discussed their research, provided insights on business trends and real-life AI applications, and addressed current challenges facing the AI industry as a whole.
Recently, CNNs have been successfully used in estimating optical flow. Compared with traditional methods, these methods achieved a large improvement in quality. Here, we will give a brief review on the following papers.
This repository did some toy experiments based on Tensorflow in order to introduce some deep learning concepts which are used for image recognition and language modeling.
Researchers from Microsoft Research Asia and Shanghai Jiaotong University have recently published a paper on transferring visual attributes of images using a new technique called Deep Image Analogy.
Synced got a chance to interview with Dr. Zhou Xi . from personal experience to CouldWalk ‘s development and future trend.