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Found 4 papers, 1 papers with code
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
In this paper, we provide a systematic review of existing compelling deep learning architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving such as segmentation, detection, and classification.
Squeeze-and-Attention Networks for Semantic Segmentation
The recent integration of attention mechanisms into segmentation networks improves their representational capabilities through a great emphasis on more informative features.
Ranked #6 on
Semantic Segmentation
on PASCAL VOC 2012 test
Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification
In this paper, we address the hyperspectral image (HSI) classification task with a generative adversarial network and conditional random field (GAN-CRF) -based framework, which integrates a semi-supervised deep learning and a probabilistic graphical model, and make three contributions.
Generative Adversarial Networks and Probabilistic Graph Models for Hyperspectral Image Classification
High spectral dimensionality and the shortage of annotations make hyperspectral image (HSI) classification a challenging problem.
