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20192023
most citedFormal Verification of Robustness and Resilience of Learning-Enabled State Estimation Systems

4 citations · 7 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CV2023★ 1 cited

Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation

Shuai Yang, Yifan Zhou, Ziwei Liu +1

Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal…

cs.CV2022

CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous Driving

Hao Shi, Yifan Zhou, Kailun Yang +2

Optical flow estimation is an essential task in self-driving systems, which helps autonomous vehicles perceive temporal continuity information of surrounding scenes. The calculatio…

cs.CV2022

PanoFlow: Learning 360° Optical Flow for Surrounding Temporal Understanding

Hao Shi, Yifan Zhou, Kailun Yang +7

Optical flow estimation is a basic task in self-driving and robotics systems, which enables to temporally interpret traffic scenes. Autonomous vehicles clearly benefit from the ult…

cs.CV2021

Friends and Foes in Learning from Noisy Labels

Yifan Zhou, Yifan Ge, Jianxin Wu

Learning from examples with noisy labels has attracted increasing attention recently. But, this paper will show that the commonly used CIFAR-based datasets and the accuracy evaluat…

cs.CV2020★ 2 cited

Reliability Validation of Learning Enabled Vehicle Tracking

Youcheng Sun, Yifan Zhou, Simon Maskell +2

This paper studies the reliability of a real-world learning-enabled system, which conducts dynamic vehicle tracking based on a high-resolution wide-area motion imagery input. The s…

cs.CV2019

Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)

Yifan Zhou, Simon Maskell

This paper proposes an approach to detect moving objects in Wide Area Motion Imagery (WAMI), in which the objects are both small and well separated. Identifying the objects only us…