Abstract: Haze obscures remote sensing images, hindering valuable information extraction. To this end, we propose RSHazeNet, an encoder-minimal and decoder-minimal framework for efficient remote ...

Abstract: To carry out cell counting, it is common to use neural network models with an encoder-decoder structure to generate regression density maps. In the encoder-decoder structure, skip ...

Alfatron Electronics, the Raleigh, N.C.-based, manufacturer, has introduced the ALF-IPK1HE 4K Networked Encoder and ALF-IPK1HD 4K Networked Decoder, designed for distributing high-quality AV signals ...

Encoder models like BERT and RoBERTa have long been cornerstones of natural language processing (NLP), powering tasks such as text classification, retrieval, and toxicity detection. However, while ...

Large language models (LLMs) have changed the game for machine translation (MT). LLMs vary in architecture, ranging from decoder-only designs to encoder-decoder frameworks. Encoder-decoder models, ...

FAST (FIX Adapted for STreaming protocol) is a space and processing efficient encoding method for message oriented data streams. The FAST protocol has been developed as part of the FIX Market Data ...

CNNs are specialized deep neural networks for processing data with a grid-like topology, such as images. A CNN automatically detects the important features without any human supervision. They are ...

Abstract: Biomedical image segmentation plays an important role in Diabetic Retinopathy (DR)-related biomarker detection. DR is an ocular disease that affects the retina in people with diabetes and ...