Search Results for author:
Found 12 papers, 1 papers with code
GAM-Depth: Self-Supervised Indoor Depth Estimation Leveraging a Gradient-Aware Mask and Semantic Constraints
Self-supervised depth estimation has evolved into an image reconstruction task that minimizes a photometric loss.
Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention
Moreover, a case study on practical CAM/CAD segmentation is presented to demonstrate the effectiveness of our approach for real-world applications.
Multi-Frequency-Aware Patch Adversarial Learning for Neural Point Cloud Rendering
The proposed approach aims to improve the rendering realness by minimizing the spectrum discrepancy between real and synthesized images, especially on the high-frequency localized sharpness information which causes image blur visually.
Masked Self-Supervision for Remaining Useful Lifetime Prediction in Machine Tools
With the availability of deep learning approaches, the great potential and prospect of utilizing these for RUL prediction have resulted in several models which are designed driven by operation data of manufacturing machines.
CAM/CAD Point Cloud Part Segmentation via Few-Shot Learning
However, the disadvantage is that the resulting models from the fully-supervised learning methodology are highly reliant on the completeness of the available dataset, and its generalization ability is relatively poor to new unknown segmentation types (i. e. further additional novel classes).
Incremental Few-Shot Learning via Implanting and Compressing
Specifically, in the \textbf{Implanting} step, we propose to mimic the data distribution of novel classes with the assistance of data-abundant base set, so that a model could learn semantically-rich features that are beneficial for discriminating between the base and other unseen classes.
Towards Generalized and Incremental Few-Shot Object Detection
We conduct experiments on both Pascal VOC and MS-COCO, which demonstrate that our method can effectively solve the problem of incremental few-shot detection and significantly improve the detection accuracy on both base and novel classes.
Few-Shot Object Detection via Classification Refinement and Distractor Retreatment
The failure modes of FSOD are investigated that the performance degradation is mainly due to the classification incapability (false positives), which motivates us to address it from a novel aspect of hard example mining.
Convex Parameterization and Optimization for Robust Tracking of a Magnetically Levitated Planar Positioning System
Magnetic levitation positioning technology has attracted considerable research efforts and dedicated attention due to its extremely attractive features.
Grasping Detection Network with Uncertainty Estimation for Confidence-Driven Semi-Supervised Domain Adaptation
The proposed grasping detection network specially provides a prediction uncertainty estimation mechanism by leveraging on Feature Pyramid Network (FPN), and the mean-teacher semi-supervised learning utilizes such uncertainty information to emphasizing the consistency loss only for those unlabelled data with high confidence, which we referred it as the confidence-driven mean teacher.
Robust Fixed-Order Controller Design for Uncertain Systems with Generalized Common Lyapunov Strictly Positive Realness Characterization
It is also noteworthy that the proposed methodology additionally provides the necessary and sufficient conditions for this robust controller design with the consideration of a prescribed finite frequency range, and therefore significantly less conservatism is attained in the system performance.
Incremental Few-Shot Object Detection for Robotics
Incremental few-shot learning is highly expected for practical robotics applications.
