Transformers are a type of neural network architecture that was first developed by Google in its DeepMind laboratories. The ...

The core innovation lies in replacing the traditional DETR backbone with ConvNeXt, a convolutional neural network inspired by ...

Meanwhile, aiming at the problem of low segmentation accuracy with traditional convolution neural network-based methods in the crop disease leaf image, this paper proposes a spatial pyramid-oriented ...

This letter proposes an encoder-generator-decoder SR reconstruction (SRR) network for remote sensing named EGDSR. We design three modules: multiscale feature extraction and latent code generation ...

By training a neural network to recognize the subtle patterns ... which seeks to use crowdsourcing to decode unopened scrolls. Launched in 2023, the challenge invites participants to use AI ...

April 21, 2022-- Xylon has just revealed two new IP products for lossless and on-the-fly MJPEG video compression and decompression. New logiJPGE-LS and logiJPGD-LS IP cores from the logicBRICKS by ...

High-entropy alloys (HEAs) offer tunable compositions and surface structures, presenting significant potential for creating novel active sites to enhance CO2 reduction (CO2RR) catalysis, a key process ...

Necati Catbas collaborated with his former civil engineering student Marwan Debees, Ph.D., who now works as a NASA Bridge ...

Understanding neural network dynamics is a cornerstone of systems neuroscience, bridging the gap between biological neural networks and artificial neural ...