Search Results for author:
Found 15 papers, 1 papers with code
Sat2lod2: A Software For Automated Lod-2 Modeling From Satellite-Derived Orthophoto And Digital Surface Model
We further improve the robustness of the method by 1) intergrading building segmentation based on HRNetV2 into our software; and 2) having implemented a decision strategy to identify complex buildings and directly generate mesh to avoid erroneous LoD2 reconstruction from a system point of view.
Learn to Adapt for Monocular Depth Estimation
To boost the transferability of depth estimation models, we propose an adversarial depth estimation task and train the model in the pipeline of meta-learning.
Unsupervised Monocular Depth Estimation in Highly Complex Environments
While in some challenging environments, like night, rainy night or snowy winter, the photometry of the same pixel on different frames is inconsistent because of the complex lighting and reflection, so that the day-time unsupervised frameworks cannot be directly applied to these complex scenarios.
PIAP-DF: Pixel-Interested and Anti Person-Specific Facial Action Unit Detection Net With Discrete Feedback Learning
Experimental results on the two popular AU detection datasets BP4D and DISFA prove that PIAP-DF can be the new state-of-the-art method.
Multi-task GANs for Semantic Segmentation and Depth Completion with Cycle Consistency
Semantic segmentation and depth completion are two challenging tasks in scene understanding, and they are widely used in robotics and autonomous driving.
Masked GANs for Unsupervised Depth and Pose Prediction with Scale Consistency
This paper proposes a masked generative adversarial network (GAN) for unsupervised monocular depth and ego-motion estimation. The MaskNet and Boolean mask scheme are designed in this framework to eliminate the effects of occlusions and impacts of visual field changes on the reconstruction loss and adversarial loss, respectively.
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
Then, we further review the performance of RL and meta-learning from the aspects of accuracy or transferability or both of them in autonomous systems, involving pedestrian tracking, robot navigation and robotic manipulation.
Monocular Depth Estimation Based On Deep Learning: An Overview
With the rapid development of deep neural networks, monocular depth estimation based on deep learning has been widely studied recently and achieved promising performance in accuracy.
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey
Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks.
Deep Direct Visual Odometry
Extensive experiments on the KITTI dataset show that the proposed constraints can effectively improve the scale-consistency of TrajNet when compared with previous unsupervised monocular methods, and integration with TrajNet makes the initialization and tracking of DSO more robust and accurate.
Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions
A parametrization of the component scale matrices for the mixture of generalized hyperbolic distributions is proposed by including a penalty term in the likelihood constraining the parameters resulting in a flexible model for high dimensional data and a meaningful interpretation.
Text Assisted Insight Ranking Using Context-Aware Memory Network
Extracting valuable facts or informative summaries from multi-dimensional tables, i. e. insight mining, is an important task in data analysis and business intelligence.
Robust Order Scheduling in the Fashion Industry: A Multi-Objective Optimization Approach
In the fashion industry, order scheduling focuses on the assignment of production orders to appropriate production lines.
Differential Evolution with Event-Triggered Impulsive Control
In this paper, an event-triggered impulsive control scheme (ETI) is introduced to improve the performance of DE.
Model Based Clustering of High-Dimensional Binary Data
Recent work on clustering of binary data, based on a $d$-dimensional Gaussian latent variable, is extended by incorporating common factor analyzers.
