1 code implementation • ECCV 2020 • Ning Li, Yongqiang Zhao, Quan Pan, Seong G. Kong, Jonathan Cheung-Wai Chan
Zero-distribution prior embodies the zero-distribution of Angle of Polarization (AoP) of a road scene image, which provides a significant contrast between the road and the background.
no code implementations • 21 Feb 2024 • Yang Li, Wenyi Tan, Chenxing Zhao, Shuangju Zhou, Xinkai Liang, Quan Pan
This involves incorporating a specially designed adversarial loss and covert constraint loss to guarantee the adversarial and covert nature of the camouflage in the physical world.
no code implementations • 19 Feb 2024 • Yuhang Hao, Zengfu Wang, Jing Fu, Quan Pan
Each bandit process is associated with a smart target, of which the estimation state evolves according to different discrete dynamic models for different actions - whether or not the target is being tracked.
no code implementations • 9 Feb 2024 • Xiaoxuan Zhang, Quan Pan, Salvador García
MSADGN can extract domain-invariant and domain-specific features from one labeled source domain and multiple unlabeled source domains, and then generalize these features to an arbitrary unseen target domain for real-time prediction of sea\textendash land clutter.
no code implementations • 31 Jan 2024 • Tiancheng Li, Haozhe Liang, Guchong Li, Jesús García Herrero, Quan Pan
This paper, the fourth part of a series of papers on the arithmetic average (AA) density fusion approach and its application for target tracking, addresses the intricate challenge of distributed heterogeneous multisensor multitarget tracking, where each inter-connected sensor operates a probability hypothesis density (PHD) filter, a multiple Bernoulli (MB) filter or a labeled MB (LMB) filter and they cooperate with each other via information fusion.
no code implementations • 28 Dec 2023 • Wenyi Tan, Yang Li, Chenxing Zhao, ZhunGa Liu, Quan Pan
While ensemble models have proven effective, current research in the field of object detection typically focuses on the simple fusion of the outputs of all models, with limited attention being given to developing general adversarial patches that can function effectively in the physical world.
no code implementations • 20 Dec 2023 • Xiangjuan Li, Feifan Li, Yang Li, Quan Pan
Deep reinforcement learning has advanced greatly and applied in many areas.
no code implementations • 13 Dec 2023 • Yuhang Hao, Zengfu Wang, José Niño-Mora, Jing Fu, Min Yang, Quan Pan
We present numerical evidence that the model satisfies sufficient conditions for indexability (existence of the Whittle index) based upon partial conservation laws, and, through extensive simulations, we validate the effectiveness of the proposed policy in different scenarios.
no code implementations • 19 Oct 2023 • Xianglong Bai, Zengfu Wang, Quan Pan, Tao Yun, Hua Lan
We first introduce a novel neural enhanced message passing approach, where the beliefs obtained by the unified message passing are fed into the neural network as additional information.
no code implementations • 16 Jun 2023 • Yuhang Hao, Zengfu Wang, Jing Fu, Xianglong Bai, Can Li, Quan Pan
We track moving targets with a distributed multiple-input multiple-output (MIMO) radar, for which the transmitters and receivers are appropriately paired and selected with a limited number of radar stations.
no code implementations • 6 May 2023 • Xiaoxuan Zhang, Zengfu Wang, Kun Lu, Quan Pan, Yang Li
The semi-supervised classification performance of WL-SSGAN is evaluated on a sea-land clutter dataset.
no code implementations • 3 Jan 2023 • Xiaoxuan Zhang, Zengfu Wang, Kun Lu, Quan Pan
Using a dataset of OTHR sea-land clutter, both the quality of the synthetic samples and the performance of data augmentation of AC-VAEGAN are verified.
no code implementations • 14 Dec 2022 • Xianglong Bai, Hua Lan, Zengfu Wang, Quan Pan, Yuhang Hao, Can Li
Then, a unified MP algorithm is used to infer the marginal posterior probability distributions of targets, clutter, and data association by splitting the joint probability distribution into a mean-field approximate part and a belief propagation part.
no code implementations • 2 May 2022 • Yang Li, Quan Pan, Erik Cambria
Recent adversarial attack developments have made reinforcement learning more vulnerable, and different approaches exist to deploy attacks against it, where the key is how to choose the right timing of the attack.
no code implementations • 19 Dec 2021 • Lianmeng Jiao, Feng Wang, Zhun-Ga Liu, Quan Pan
As a representative evidential clustering algorithm, evidential c-means (ECM) provides a deeper insight into the data by allowing an object to belong not only to a single class, but also to any subset of a collection of classes, which generalizes the hard, fuzzy, possibilistic, and rough partitions.
no code implementations • 7 Mar 2021 • Zaidao Wen, Jiaxiang Liu, ZhunGa Liu, Quan Pan
This letter presents a novel framework termed DistSTN for the task of synthetic aperture radar (SAR) automatic target recognition (ATR).
no code implementations • 3 Oct 2020 • Lianmeng Jiao, Thierry Denoeux, Zhun-Ga Liu, Quan Pan
The Gaussian mixture model (GMM) provides a simple yet principled framework for clustering, with properties suitable for statistical inference.
no code implementations • 5 May 2020 • Zhen Guo, Zengfu Wang, Hua Lan, Quan Pan, Kun Lu
Therefore, to improve the localization accuracy of OTHR, it is important to develop accurate models and estimation methods of ionospheric parameters and the corresponding target tracking algorithms.
no code implementations • 6 Oct 2019 • Liyuan Pan, Yuchao Dai, Miaomiao Liu, Fatih Porikli, Quan Pan
Under our model, these three tasks are naturally connected and expressed as the parameter estimation of 3D scene structure and camera motion (structure and motion for the dynamic scenes).
no code implementations • 28 Sep 2018 • Kuang Zhou, Quan Pan, Arnaud Martin
Credal partitions in the framework of belief functions can give us a better understanding of the analyzed data set.
no code implementations • 15 Sep 2017 • Yang Li, Quan Pan, Suhang Wang, Haiyun Peng, Tao Yang, Erik Cambria
The majority of existing semi-supervised VAEs utilize a classifier to exploit label information, where the parameters of the classifier are introduced to the VAE.
no code implementations • 25 Jul 2017 • Kuang Zhou, Arnaud Martin, Quan Pan
It will keep the spirit of the conjunctive rule to reinforce the belief on the focal elements with which the sources are in agreement.
no code implementations • 27 Oct 2016 • Hua Lan, Shuai Sun, Zengfu Wang, Quan Pan, Zhishan Zhang
We consider multitarget detection and tracking problem for a class of multipath detection system where one target may generate multiple measurements via multiple propagation paths, and the association relationship among targets, measurements and propagation paths is unknown.
no code implementations • 29 Jul 2016 • Kuang Zhou, Arnaud Martin, Quan Pan
In the task of community detection, there often exists some useful prior information.
no code implementations • 13 Jun 2016 • Kuang Zhou, Arnaud Martin, Quan Pan, Zhun-Ga Liu
With the increasing size of social networks in real world, community detection approaches should be fast and accurate.
no code implementations • 3 Jun 2016 • Kuang Zhou, Arnaud Martin, Quan Pan
In this paper, an extension of NOR model based on the theory of belief functions, named Belief Noisy-OR (BNOR), is proposed.
no code implementations • 3 Jun 2016 • Kuang Zhou, Arnaud Martin, Quan Pan, Zhun-Ga Liu
In the application of FCMdd and original ECMdd, a single medoid (prototype), which is supposed to belong to the object set, is utilized to represent one class.
no code implementations • 8 Feb 2016 • Zhun-Ga Liu, Quan Pan, Jean Dezert, Arnaud Martin
We propose a credal classification method for incomplete pattern with adaptive imputation of missing values based on belief function theory.
no code implementations • 15 Jul 2015 • Kuang Zhou, Arnaud Martin, Quan Pan, Zhun-Ga Liu
Medoid-based clustering algorithms, which assume the prototypes of classes are objects, are of great value for partitioning relational data sets.
no code implementations • 7 Jan 2015 • Kuang Zhou, Arnaud Martin, Quan Pan
Evidential-EM (E2M) algorithm is an effective approach for computing maximum likelihood estimations under finite mixture models, especially when there is uncertain information about data.
no code implementations • 7 Jan 2015 • Kuang Zhou, Arnaud Martin, Quan Pan, Zhun-Ga Liu
In this paper, a new prototype-based clustering method, called Median Evidential C-Means (MECM), which is an extension of median c-means and median fuzzy c-means on the theoretical framework of belief functions is proposed.