3 citations · 6 across the 10 of their papers we have counts for
10 papers
Semantic Semi-Incremental Data-Association-Free Object SLAM
Yihao Zhang, Jungseok Hong, John J. Leonard
Data association between landmark measurements and landmark variables has long been a central challenge in SLAM, as estimation accuracy depends critically on associating measuremen…
Sharpness-Aware Minimization Alone can Improve Adversarial Robustness
Zeming Wei, Jingyu Zhu, Yihao Zhang
Sharpness-Aware Minimization (SAM) is an effective method for improving generalization ability by regularizing loss sharpness. In this paper, we explore SAM in the context of adver…
Weighted Automata Extraction and Explanation of Recurrent Neural Networks for Natural Language Tasks
Zeming Wei, Xiyue Zhang, Yihao Zhang +1
Recurrent Neural Networks (RNNs) have achieved tremendous success in processing sequential data, yet understanding and analyzing their behaviours remains a significant challenge. T…
Using Z3 for Formal Modeling and Verification of FNN Global Robustness
Yihao Zhang, Zeming Wei, Xiyue Zhang +1
While Feedforward Neural Networks (FNNs) have achieved remarkable success in various tasks, they are vulnerable to adversarial examples. Several techniques have been developed to v…
Data-Association-Free Landmark-based SLAM
Yihao Zhang, Odin A. Severinsen, John J. Leonard +2
We study landmark-based SLAM with unknown data association: our robot navigates in a completely unknown environment and has to simultaneously reason over its own trajectory, the po…
SLAM-Supported Self-Training for 6D Object Pose Estimation
Ziqi Lu, Yihao Zhang, Kevin Doherty +3
Recent progress in object pose prediction provides a promising path for robots to build object-level scene representations during navigation. However, as we deploy a robot in novel…
A Front-End for Dense Monocular SLAM using a Learned Outlier Mask Prior
Yihao Zhang, John J. Leonard
Recent achievements in depth prediction from a single RGB image have powered the new research area of combining convolutional neural networks (CNNs) with classical simultaneous loc…
Bootstrapped Self-Supervised Training with Monocular Video for Semantic Segmentation and Depth Estimation
Yihao Zhang, John J. Leonard
For a robot deployed in the world, it is desirable to have the ability of autonomous learning to improve its initial pre-set knowledge. We formalize this as a bootstrapped self-sup…
FasterRCNN Monitoring of Road Damages: Competition and Deployment
Hascoet Tristan, Yihao Zhang, Persch Andreas +3
Maintaining aging infrastructure is a challenge currently faced by local and national administrators all around the world. An important prerequisite for efficient infrastructure ma…
Local Contrast Learning
Chuanyun Xu, Yang Zhang, Xin Feng +3
Learning a deep model from small data is yet an opening and challenging problem. We focus on one-shot classification by deep learning approach based on a small quantity of training…