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Found 5 papers, 4 papers with code
You Only Look at Once for Real-time and Generic Multi-Task
In this study, we incorporate A-YOLOM, an adaptive, real-time, and lightweight multi-task model designed to concurrently address object detection, drivable area segmentation, and lane line segmentation tasks.
Ranked #3 on
Lane Detection
on BDD100K val
An Attentive-based Generative Model for Medical Image Synthesis
This study proposes an attention-based dual contrast generative model, called ADC-cycleGAN, which can synthesize medical images from unpaired data with multiple slices.
DC-cycleGAN: Bidirectional CT-to-MR Synthesis from Unpaired Data
Specifically, a dual contrast loss is introduced into the discriminators to indirectly build constraints between real source and synthetic images by taking advantage of samples from the source domain as negative samples and enforce the synthetic images to fall far away from the source domain.
Road Network Reconstruction from Satellite Images with Machine Learning Supported by Topological Methods
Next, in a fully automatic framework, we leverage the power of the discrete-Morse based graph reconstruction algorithm to train a CNN from a collection of images without labelled data and use the same algorithm to produce the final output from the segmented images created by the trained CNN.
Graph Reconstruction by Discrete Morse Theory
Specifically, first, leveraging existing theoretical understanding of persistence-guided discrete Morse cancellation, we provide a simplified version of the existing discrete Morse-based graph reconstruction algorithm.
Computational Geometry
