A deep learning framework enhances medical image recognition by optimizing RNN architectures with LSTM, GRU, multimodal fusion, and CNN ...
An intelligent AFM processing framework integrates optimized scanning trajectories, distortion correction, and deep learning segmentation to improve imaging stability, accuracy, and automation. By ...
Abstract: Segment Anything Model (SAM) is a foundational image segmentation model, which shows superior performance for natural image segmentation tasks. Several SAM-based medical image segmentations ...
The idea of using the Apple II home computer for digital photography purposes may seem somewhat daft considering that this is not a purpose that they were ever designed for, yet this is the goal that ...
Much of the expense of developing AI models, and much of the recent backlash to said models, stems from the massive amount of power they tend to consume. If you’re willing to sacrifice some ability ...
This repository contains the code implementation for the paper RSRefSeg: Referring Remote Sensing Image Segmentation with Foundation Models, developed based on the MMSegmentation project. The current ...
As shown below, the inferred masks predicted by our segmentation model trained on the PNG dataset appear similar to the ground truth masks, but lack precision in some areas. To improve segmentation ...
Abstract: The purpose of this paper is to study of how machine learning algorithms, artificial intelligence, semantic segmentation and fine recognition can be used to enhance computer vision in order ...
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