Search Results for author: Zhenyu Zhong

Found 10 papers, 6 papers with code

Scaling Efficient Masked Autoencoder Learning on Large Remote Sensing Dataset

1 code implementation17 Jun 2024 Fengxiang Wang, Hongzhen Wang, Di Wang, Zonghao Guo, Zhenyu Zhong, Long Lan, Jing Zhang, Zhiyuan Liu, Maosong Sun

To address this, we propose an efficient MIM method, termed \textbf{SelectiveMAE}, which dynamically encodes and reconstructs a subset of patch tokens selected based on their semantic richness.

A Survey of Time Series Anomaly Detection Methods in the AIOps Domain

no code implementations1 Aug 2023 Zhenyu Zhong, Qiliang Fan, Jiacheng Zhang, Minghua Ma, Shenglin Zhang, Yongqian Sun, QIngwei Lin, Yuzhi Zhang, Dan Pei

Internet-based services have seen remarkable success, generating vast amounts of monitored key performance indicators (KPIs) as univariate or multivariate time series.

Anomaly Detection Time Series +1

Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems

3 code implementations14 Sep 2021 Ziyuan Zhong, Zhisheng Hu, Shengjian Guo, Xinyang Zhang, Zhenyu Zhong, Baishakhi Ray

We define the failures (e. g., car crashes) caused by the faulty MSF as fusion errors and develop a novel evolutionary-based domain-specific search framework, FusED, for the efficient detection of fusion errors.

Autonomous Driving Sensor Fusion

Coverage-based Scene Fuzzing for Virtual Autonomous Driving Testing

no code implementations2 Jun 2021 Zhisheng Hu, Shengjian Guo, Zhenyu Zhong, Kang Li

Simulation-based virtual testing has become an essential step to ensure the safety of autonomous driving systems.

Autonomous Driving

Towards Practical Lottery Ticket Hypothesis for Adversarial Training

1 code implementation6 Mar 2020 Bai Li, Shiqi Wang, Yunhan Jia, Yantao Lu, Zhenyu Zhong, Lawrence Carin, Suman Jana

Recent research has proposed the lottery ticket hypothesis, suggesting that for a deep neural network, there exist trainable sub-networks performing equally or better than the original model with commensurate training steps.

Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking

1 code implementation ICLR 2020 Yunhan Jia, Yantao Lu, Junjie Shen, Qi Alfred Chen, Hao Chen, Zhenyu Zhong, Tao Wei

Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models.

Adversarial Attack Autonomous Driving +5

Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking

1 code implementation27 May 2019 Yunhan Jia, Yantao Lu, Junjie Shen, Qi Alfred Chen, Zhenyu Zhong, Tao Wei

Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models.

Adversarial Attack Autonomous Driving +5

Enhancing Cross-task Transferability of Adversarial Examples with Dispersion Reduction

1 code implementation8 May 2019 Yunhan Jia, Yantao Lu, Senem Velipasalar, Zhenyu Zhong, Tao Wei

Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i. e., they maintain their effectiveness even against other models.

Image Classification object-detection +3

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