We present two versatile methods to generally enhance self-supervised monocular depth estimation (MDE) models.
In this paper, we redesign the patch-based triplet loss in MDE to alleviate the ubiquitous edge-fattening issue.
Ranked #1 on Unsupervised Monocular Depth Estimation on Kitti Raw
In this study, we propose a framework that combines pathological images and medical reports to generate a personalized diagnosis result for individual patient.
A modified three-dimensional Markov chain model adopting the quitting probability and cluster division is developed for the performance analysis.
Information Theory Information Theory
Image inpainting techniques have shown significant improvements by using deep neural networks recently.