no code implementations • 19 Apr 2024 • Asmar Muqeet, Shaukat Ali, Tao Yue, Paolo Arcaini
Results show that, compared to the baseline, Q-LEAR achieved a 25% average improvement in error mitigation on both real quantum computers and simulators.
1 code implementation • 30 Nov 2023 • Chengjie Lu, Shaukat Ali, Tao Yue
Testing autonomous vehicles (AVs) under various environmental scenarios that lead the vehicles to unsafe situations is known to be challenging.
1 code implementation • 8 Oct 2023 • Chengjie Lu, Tao Yue, Man Zhang, Shaukat Ali
In addition, existing ADS testing techniques have limited effectiveness in ensuring the realism of test scenarios, especially the realism of weather conditions and their changes over time.
1 code implementation • 27 Sep 2023 • Qinghua Xu, Shaukat Ali, Tao Yue
LATTICE also, on average, reduces the training time of ATTAIN by 4. 2% on the five datasets and is on par with the baselines in terms of detection delay time.
no code implementations • 8 Sep 2023 • Qinghua Xu, Shaukat Ali, Tao Yue, Zaimovic Nedim, Inderjeet Singh
However, constructing a DT for anomaly detection in TCMS necessitates sufficient training data and extracting both chronological and context features with high quality.
1 code implementation • 6 Sep 2023 • Chengjie Lu, Qinghua Xu, Tao Yue, Shaukat Ali, Thomas Schwitalla, Jan F. Nygård
To tackle this challenge, we propose EvoCLINICAL, which considers the CCDT developed for the previous version of GURI as the pretrained model and fine-tunes it with the dataset labelled by querying a new GURI version.
no code implementations • 21 Mar 2022 • Chenxi Qiu, Tao Yue, Xuemei Hu
Scene-dependent adaptive compressive sensing (CS) has been a long pursuing goal which has huge potential in significantly improving the performance of CS.
no code implementations • CVPR 2022 • Jiaqu Li, Tao Yue, Sijie Zhao, Xuemei Hu
Indirect Time-of-Flight (ToF) imaging is widely applied in practice for its superiorities on cost and spatial resolution.
1 code implementation • 11 Jul 2021 • Ferhat Ozgur Catak, Tao Yue, Shaukat Ali
Object detection in autonomous cars is commonly based on camera images and Lidar inputs, which are often used to train prediction models such as deep artificial neural networks for decision making for object recognition, adjusting speed, etc.
no code implementations • CVPR 2021 • Sijie Zhao, Tao Yue, Xuemei Hu
In this paper, we explore the compression of deep neural networks by quantizing the weights and activations into multi-bit binary networks (MBNs).
no code implementations • CVPR 2021 • Siqi Ni, Xueyun Cao, Tao Yue, Xuemei Hu
Existing rain image editing methods focus on either removing rain from rain images or rendering rain on rain-free images.
1 code implementation • ICCV 2021 • Zhicong Huang, Xuemei Hu, Zhou Xue, Weizhu Xu, Tao Yue
Light field images contain both angular and spatial information of captured light rays.
1 code implementation • 5 Jun 2018 • Haojie Liu, Tong Chen, Qiu Shen, Tao Yue, Zhan Ma
We present a lossy image compression method based on deep convolutional neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and JPEG as measured via multi-scale structural similarity (MS-SSIM), at the same bit rate.
no code implementations • CVPR 2018 • Qian Huang, Weixin Zhu, Yang Zhao, Linsen Chen, Yao Wang, Tao Yue, Xun Cao
In this paper, a new Multispectral Image Intrinsic Decomposition model (MIID) is presented to decompose the shading and reflectance from a single multispectral image.
no code implementations • 24 Feb 2018 • Qian Huang, Weixin Zhu, Yang Zhao, Linsen Chen, Yao Wang, Tao Yue, Xun Cao
In this paper, a Low Rank Multispectral Image Intrinsic Decomposition model (LRIID) is presented to decompose the shading and reflectance from a single multispectral image.
no code implementations • CVPR 2015 • Tao Yue, Jinli Suo, Jue Wang, Xun Cao, Qionghai Dai
Furthermore, by investigating the visual artifacts of aberration degenerated images captured by consumer-level cameras, the non-uniform distribution of sharpness across color channels and the image lattice is exploited as visual priors, resulting in a novel strategy to utilize the guidance from the sharpest channel and local image regions to improve the overall performance and robustness.