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Found 5 papers, 2 papers with code
Patched Line Segment Learning for Vector Road Mapping
In our experiments, we demonstrate how an effective representation of a road graph significantly enhances the performance of vector road mapping on established benchmarks, without requiring extensive modifications to the neural network architecture.
NEAT: Distilling 3D Wireframes from Neural Attraction Fields
Instead of leveraging matching-based solutions from 2D wireframes (or line segments) for 3D wireframe reconstruction as done in prior arts, we present NEAT, a rendering-distilling formulation using neural fields to represent 3D line segments with 2D observations, and bipartite matching for perceiving and distilling of a sparse set of 3D global junctions.
Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising
Experiment results demonstrate that the proposed method requires a reduced amount of training data to achieve high reconstruction quality among the state-of-art of MR reconstruction utilizing the Noise2Noise method.
Turbulence-based load alleviation control for wind turbine in extreme turbulence situation
Based on this insight, this work proposes a turbulence-based load alleviation control strategy for adapting the controller to changes in wind condition.
Conceptualized Representation Learning for Chinese Biomedical Text Mining
In this paper, we investigate how the recently introduced pre-trained language model BERT can be adapted for Chinese biomedical corpora and propose a novel conceptualized representation learning approach.
