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 ...

Abstract: This article presents a new deep-learning architecture based on an encoder-decoder framework that retains contrast while performing background subtraction (BS) on thermal videos. The ...

The landscape of vision model pre-training has undergone significant evolution, especially with the rise of Large Language Models (LLMs). Traditionally, vision models operated within fixed, predefined ...

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, ...

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 ...

What Is An Encoder-Decoder Architecture? An encoder-decoder architecture is a powerful tool used in machine learning, specifically for tasks involving sequences like text or speech. It’s like a ...

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