no code implementations • 22 Nov 2024 • Sen yang, Minyue Jiang, Ziwei Fan, Xiaolu Xie, Xiao Tan, YingYing Li, Errui Ding, Liang Wang, Jingdong Wang
Recent advances in autonomous driving systems have shifted towards reducing reliance on high-definition maps (HDMaps) due to the huge costs of annotation and maintenance.
1 code implementation • 1 Nov 2024 • Negin Musavi, Ziyao Guo, Geir Dullerud, YingYing Li
Compared with the counter-example based on piecewise-affine systems in the literature, the success of non-active exploration in our setting relies on a key assumption on the system dynamics: we require the system functions to be real-analytic.
no code implementations • 10 Oct 2024 • Jing Yang, Minyue Jiang, Sen yang, Xiao Tan, YingYing Li, Errui Ding, Hanli Wang, Jingdong Wang
The construction of Vectorized High-Definition (HD) map typically requires capturing both category and geometry information of map elements.
no code implementations • 1 Jun 2024 • Haonan Xu, YingYing Li
This paper studies the uncertainty set estimation of system parameters of linear dynamical systems with bounded disturbances, which is motivated by robust (adaptive) constrained control.
1 code implementation • ICCV 2023 • Jiaming Li, Xiangru Lin, Wei zhang, Xiao Tan, YingYing Li, Junyu Han, Errui Ding, Jingdong Wang, Guanbin Li
To tackle the confirmation bias from incorrect pseudo labels of minority classes, the class-rebalancing sampling module resamples unlabeled data following the guidance of the gradient-based reweighting module.
no code implementations • 27 Feb 2024 • Mehmet Caner, Qingliang Fan, YingYing Li
This paper analyzes the statistical properties of constrained portfolio formation in a high dimensional portfolio with a large number of assets.
no code implementations • 24 Oct 2023 • Yaoyao Liu, YingYing Li, Bernt Schiele, Qianru Sun
In experiments, we show that our method 1) is surprisingly effective even when there is no class overlap between placebos and original old class data, 2) does not require any additional supervision or memory budget, and 3) significantly outperforms a number of top-performing CIL methods, in particular when using lower memory budgets for old class exemplars, e. g., five exemplars per class.
1 code implementation • 26 Sep 2023 • YingYing Li, Jing Yu, Lauren Conger, Taylan Kargin, Adam Wierman
This paper studies uncertainty set estimation for unknown linear systems.
no code implementations • 17 Jun 2023 • YingYing Li, Tianpeng Zhang, Subhro Das, Jeff Shamma, Na Li
This paper considers a single-trajectory system identification problem for linear systems under general nonlinear and/or time-varying policies with i. i. d.
1 code implementation • 11 Jan 2023 • Yaoyao Liu, YingYing Li, Bernt Schiele, Qianru Sun
Class-incremental learning (CIL) aims to train a classification model while the number of classes increases phase-by-phase.
no code implementations • 25 Dec 2022 • Runzhe Wan, YingYing Li, Wenbin Lu, Rui Song
Latent factor model estimation typically relies on either using domain knowledge to manually pick several observed covariates as factor proxies, or purely conducting multivariate analysis such as principal component analysis.
1 code implementation • 20 Oct 2022 • Zhuo Chen, Wen Zhang, Yufeng Huang, Mingyang Chen, Yuxia Geng, Hongtao Yu, Zhen Bi, Yichi Zhang, Zhen Yao, Wenting Song, Xinliang Wu, Yi Yang, Mingyi Chen, Zhaoyang Lian, YingYing Li, Lei Cheng, Huajun Chen
In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents.
7 code implementations • 5 Oct 2022 • Silvio Giancola, Anthony Cioppa, Adrien Deliège, Floriane Magera, Vladimir Somers, Le Kang, Xin Zhou, Olivier Barnich, Christophe De Vleeschouwer, Alexandre Alahi, Bernard Ghanem, Marc Van Droogenbroeck, Abdulrahman Darwish, Adrien Maglo, Albert Clapés, Andreas Luyts, Andrei Boiarov, Artur Xarles, Astrid Orcesi, Avijit Shah, Baoyu Fan, Bharath Comandur, Chen Chen, Chen Zhang, Chen Zhao, Chengzhi Lin, Cheuk-Yiu Chan, Chun Chuen Hui, Dengjie Li, Fan Yang, Fan Liang, Fang Da, Feng Yan, Fufu Yu, Guanshuo Wang, H. Anthony Chan, He Zhu, Hongwei Kan, Jiaming Chu, Jianming Hu, Jianyang Gu, Jin Chen, João V. B. Soares, Jonas Theiner, Jorge De Corte, José Henrique Brito, Jun Zhang, Junjie Li, Junwei Liang, Leqi Shen, Lin Ma, Lingchi Chen, Miguel Santos Marques, Mike Azatov, Nikita Kasatkin, Ning Wang, Qiong Jia, Quoc Cuong Pham, Ralph Ewerth, Ran Song, RenGang Li, Rikke Gade, Ruben Debien, Runze Zhang, Sangrok Lee, Sergio Escalera, Shan Jiang, Shigeyuki Odashima, Shimin Chen, Shoichi Masui, Shouhong Ding, Sin-wai Chan, Siyu Chen, Tallal El-Shabrawy, Tao He, Thomas B. Moeslund, Wan-Chi Siu, Wei zhang, Wei Li, Xiangwei Wang, Xiao Tan, Xiaochuan Li, Xiaolin Wei, Xiaoqing Ye, Xing Liu, Xinying Wang, Yandong Guo, YaQian Zhao, Yi Yu, YingYing Li, Yue He, Yujie Zhong, Zhenhua Guo, Zhiheng Li
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
no code implementations • 25 Mar 2022 • Xiaoqing Ye, Mao Shu, Hanyu Li, Yifeng Shi, YingYing Li, Guangjie Wang, Xiao Tan, Errui Ding
On the other hand, the data captured from roadside cameras have strengths over frontal-view data, which is believed to facilitate a safer and more intelligent autonomous driving system.
no code implementations • 20 Jan 2022 • Yi Ding, YingYing Li, Rui Song
We show that our proposed Discretization and Regression with generalized fOlded concaVe penalty on Effect discontinuity (DROVE) approach enjoys desirable theoretical properties and allows for statistical inference of the optimal value associated with optimal decision-making.
no code implementations • CVPR 2022 • Xiaoqing Ye, Mao Shu, Hanyu Li, Yifeng Shi, YingYing Li, Guangjie Wang, Xiao Tan, Errui Ding
On the other hand, the data captured from roadside cameras have strengths over frontal-view data, which is believed to facilitate a safer and more intelligent autonomous driving system.
no code implementations • 31 Oct 2021 • YingYing Li, Subhro Das, Jeff Shamma, Na Li
We study the adaptive control of an unknown linear system with a quadratic cost function subject to safety constraints on both the states and actions.
no code implementations • 9 Aug 2021 • Jie Wu, Wei zhang, Guanbin Li, Wenhao Wu, Xiao Tan, YingYing Li, Errui Ding, Liang Lin
In this paper, we introduce a novel task, referred to as Weakly-Supervised Spatio-Temporal Anomaly Detection (WSSTAD) in surveillance video.
1 code implementation • 25 May 2021 • Wenhao Wu, Yuxiang Zhao, Yanwu Xu, Xiao Tan, Dongliang He, Zhikang Zou, Jin Ye, YingYing Li, Mingde Yao, ZiChao Dong, Yifeng Shi
Long-range and short-range temporal modeling are two complementary and crucial aspects of video recognition.
Ranked #6 on
Action Recognition
on ActivityNet
1 code implementation • 9 May 2021 • Yuxiang Zhao, Wenhao Wu, Yue He, YingYing Li, Xiao Tan, Shifeng Chen
In this paper, we propose a straightforward and efficient framework that includes pre-processing, a dynamic track module, and post-processing.
no code implementations • 19 Dec 2020 • Xin Li, YingYing Li, Shushu Li
Object detection is an important and challenging problem in computer vision.
no code implementations • NeurIPS 2020 • YingYing Li, Na Li
To address this question, we introduce a gradient-based online algorithm, Receding Horizon Inexact Gradient (RHIG), and analyze its performance by dynamic regrets in terms of the temporal variation of the environment and the prediction errors.
no code implementations • 25 Oct 2020 • Mingyang Qian, Yi Fu, Xiao Tan, YingYing Li, Jinqing Qi, Huchuan Lu, Shilei Wen, Errui Ding
Video segmentation approaches are of great importance for numerous vision tasks especially in video manipulation for entertainment.
no code implementations • 11 Oct 2020 • Xin Chen, YingYing Li, Jun Shimada, Na Li
This paper studies the automated control method for regulating air conditioner (AC) loads in incentive-based residential demand response (DR).
no code implementations • 10 Oct 2020 • YingYing Li, Subhro Das, Na Li
We show that OGD-BZ can achieve a policy regret upper bound that is the square root of the horizon length multiplied by some logarithmic terms of the horizon length under proper algorithm parameters.
no code implementations • 20 Mar 2020 • YingYing Li, Qinran Hu, Na Li
One challenge in the optimization and control of societal systems is to handle the unknown and uncertain user behavior.