Search Results for author: Yantao Lu

Found 9 papers, 6 papers with code

Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Point Density Level Estimation

1 code implementation18 Nov 2021 Yantao Lu, Xuetao Hao, Yilan Li, Weiheng Chai, Shiqi Sun, Senem Velipasalar

It is worth to note that our proposed RAA convolution is lightweight and compatible to be integrated into any CNN architecture used for detection from a BEV.

3D Object Detection Autonomous Driving +2

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 +4

Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction

2 code implementations CVPR 2020 Yantao Lu, Yunhan Jia, Jian-Yu Wang, Bai Li, Weiheng Chai, Lawrence Carin, Senem Velipasalar

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

Adversarial Attack Image Classification +3

Autonomous Human Activity Classification from Ego-vision Camera and Accelerometer Data

no code implementations28 May 2019 Yantao Lu, Senem Velipasalar

For instance, the sitting activity can be detected by IMU data, but it cannot be determined whether the subject has sat on a chair or a sofa, or where the subject is.

General Classification Multimodal Activity Recognition

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 +4

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 +2

Autonomously and Simultaneously Refining Deep Neural Network Parameters by a Bi-Generative Adversarial Network Aided Genetic Algorithm

no code implementations24 Sep 2018 Yantao Lu, Burak Kakillioglu, Senem Velipasalar

The choice of parameters, and the design of the network architecture are important factors affecting the performance of deep neural networks.

Autonomously and Simultaneously Refining Deep Neural Network Parameters by Generative Adversarial Networks

no code implementations24 May 2018 Burak Kakillioglu, Yantao Lu, Senem Velipasalar

Our proposed approach can be used to autonomously refine the parameters, and improve the accuracy of different deep neural network architectures.

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