no code implementations • IWSLT (ACL) 2022 • Weitai Zhang, Zhongyi Ye, Haitao Tang, Xiaoxi Li, Xinyuan Zhou, Jing Yang, Jianwei Cui, Dan Liu, Junhua Liu, LiRong Dai
This paper describes USTC-NELSLIP’s submissions to the IWSLT 2022 Offline Speech Translation task, including speech translation of talks from English to German, English to Chinese and English to Japanese.
no code implementations • 28 Jun 2022 • Ruiquan Huang, Jing Yang, Yingbin Liang
We then particularize the SWEET framework to the tabular and the low-rank MDP settings, and develop algorithms coined Tabular-SWEET and Low-rank-SWEET, respectively.
no code implementations • 24 Jun 2022 • Junyu Lu, Ping Yang, Ruyi Gan, Jing Yang, Jiaxing Zhang
Even as pre-trained language models share a semantic encoder, natural language understanding suffers from a diversity of output schemas.
no code implementations • 21 Jun 2022 • Yuehai Chen, Jing Yang, Badong Chen, Shaoyi Du
Thus, CNN could locate and estimate crowd accurately in low-density regions, while it is hard to properly perceive density in high-density regions.
no code implementations • 13 Jun 2022 • Yuan Cheng, Songtao Feng, Jing Yang, Hong Zhang, Yingbin Liang
To the best of our knowledge, this is the first theoretical study that characterizes the benefit of representation learning in exploration-based reward-free multitask RL for both upstream and downstream tasks.
1 code implementation • 13 May 2022 • Jing Yang, Xiatian Zhu, Adrian Bulat, Brais Martinez, Georgios Tzimiropoulos
The key idea is that we leverage the teacher's classifier as a semantic critic for evaluating the representations of both teacher and student and distilling the semantic knowledge with high-order structured information over all feature dimensions.
no code implementations • 10 May 2022 • Jing Yang, Junwen Chen, Keiji Yanai
In this paper, we present a cross-modal recipe retrieval framework, Transformer-based Network for Large Batch Training (TNLBT), which is inspired by ACME~(Adversarial Cross-Modal Embedding) and H-T~(Hierarchical Transformer).
1 code implementation • 28 Mar 2022 • Xiang An, Jiankang Deng, Jia Guo, Ziyong Feng, Xuhan Zhu, Jing Yang, Tongliang Liu
In each iteration, positive class centers and a random subset of negative class centers are selected to compute the margin-based softmax loss.
Ranked #1 on
Face Recognition
on MFR
no code implementations • 12 Feb 2022 • Cong Shen, Jing Yang, Jie Xu
Catering to the proliferation of Internet of Things devices and distributed machine learning at the edge, we propose an energy harvesting federated learning (EHFL) framework in this paper.
no code implementations • 25 Jan 2022 • Luwei Xiao, Xingjiao Wu, Wen Wu, Jing Yang, Liang He
This paper proposes a Multi-channel Attentive Graph Convolutional Network (MAGCN), consisting of two main components: cross-modality interactive learning and sentimental feature fusion.
no code implementations • CVPR 2022 • Xiang An, Jiankang Deng, Jia Guo, Ziyong Feng, Xuhan Zhu, Jing Yang, Tongliang Liu
In each iteration, positive class centers and a random subset of negative class centers are selected to compute the margin-based softmax loss.
1 code implementation • 30 Dec 2021 • Shice Liu, Shitao Lu, Hongyi Xu, Jing Yang, Shouhong Ding, Lizhuang Ma
However, the improvement is still limited by two issues: 1) It is difficult to perfectly map all faces to a shared feature space.
no code implementations • 27 Dec 2021 • Mengjian Zhang, Guihua Wen, Jing Yang
A swarm intelligence-based optimization algorithm, named Duck Swarm Algorithm (DSA), is proposed in this paper.
no code implementations • NeurIPS 2021 • Ruiquan Huang, Weiqiang Wu, Jing Yang, Cong Shen
This paper presents a novel federated linear contextual bandits model, where individual clients face different $K$-armed stochastic bandits coupled through common global parameters.
1 code implementation • NeurIPS 2021 • Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang
In this paper, we propose BEACON -- Batched Exploration with Adaptive COmmunicatioN -- that closes this gap.
no code implementations • 11 Oct 2021 • Jing Yang, Didier Vega-Oliveros, Taís Seibt, Anderson Rocha
Misleading or false information has been creating chaos in some places around the world.
1 code implementation • 22 Sep 2021 • Jing Yang, Didier Vega-Oliveros, Tais Seibt, Anderson Rocha
Researchers have been investigating automated solutions for fact-checking in a variety of fronts.
no code implementations • 12 Jul 2021 • Xinyu Gao, Tianlang Wang, Jing Yang, Jinchao Tao, Yanqing Qiu, Yanlong Meng, Banging Mao, Pengwei Zhou, Yi Li
Hyperspectral image (HSI) contains both spatial pattern and spectral information which has been widely used in food safety, remote sensing, and medical detection.
no code implementations • 23 Jun 2021 • Yuehai Chen, Jing Yang, Dong Zhang, Kun Zhang, Badong Chen, Shaoyi Du
More specifically, we scan the whole input images and its priority maps in the form of column vector to obtain a relevance matrix estimating their similarity.
no code implementations • CVPR 2021 • Jiankang Deng, Jia Guo, Jing Yang, Alexandros Lattas, Stefanos Zafeiriou
Deep face recognition has achieved remarkable improvements due to the introduction of margin-based softmax loss, in which the prototype stored in the last linear layer represents the center of each class.
no code implementations • 10 May 2021 • Jing Yang, Augusto Aubry, Antonio De Maio, Xianxiang Yu, Guolong Cui
This paper focuses on the joint synthesis of constant envelope transmit signal and receive filter aimed at optimizing radar performance in signal-dependent interference and spectrally contested-congested environments.
no code implementations • 7 Apr 2021 • Xingjiao Wu, Ziling Hu, Xiangcheng Du, Jing Yang, Liang He
The document layout analysis (DLA) aims to split the document image into different interest regions and understand the role of each region, which has wide application such as optical character recognition (OCR) systems and document retrieval.
2 code implementations • 30 Mar 2021 • Adrian Bulat, Shiyang Cheng, Jing Yang, Andrew Garbett, Enrique Sanchez, Georgios Tzimiropoulos
Recent work on Deep Learning in the area of face analysis has focused on supervised learning for specific tasks of interest (e. g. face recognition, facial landmark localization etc.)
Ranked #1 on
Arousal Estimation
on AffectNet
no code implementations • 26 Feb 2021 • Liang Chen, Peng Jin, Jing Yang, Yang Li, Yi Song
To obtain the accurate transient states of the big scale natural gas pipeline networks under the bad data and non-zero mean noises conditions, a robust Kalman filter-based dynamic state estimation method is proposed using the linearized gas pipeline transient flow equations in this paper.
1 code implementation • 25 Feb 2021 • Chengshuai Shi, Cong Shen, Jing Yang
A general framework of personalized federated multi-armed bandits (PF-MAB) is proposed, which is a new bandit paradigm analogous to the federated learning (FL) framework in supervised learning and enjoys the features of FL with personalization.
no code implementations • 2 Feb 2021 • Jing Yang, Ligong Bian
We study the magnetic fields generation from the cosmological first-order electroweak phase transition.
Cosmology and Nongalactic Astrophysics High Energy Physics - Phenomenology High Energy Physics - Theory
no code implementations • 2 Feb 2021 • S. A. Wadood, Yiyu Zhou, Jing Yang, Kevin Liang, M. A. Alonso, X. -F. Qian, T. Malhotra, S. M. Hashemi Rafsanjani, Andrew N. Jordan, Robert W. Boyd, A. N. Vamivakas
Analyses based on quantum metrology have shown that the ability to localize the positions of two incoherent point sources can be significantly enhanced through the use of mode sorting.
Optics Quantum Physics
no code implementations • ICLR 2021 • Jing Yang, Brais Martinez, Adrian Bulat, Georgios Tzimiropoulos
We advocate for a method that optimizes the output feature of the penultimate layer of the student network and hence is directly related to representation learning.
no code implementations • 24 Nov 2020 • Jing Yang, Chun Ouyang, Wil M. P. van der Aalst, Arthur H. M. ter Hofstede, Yang Yu
We demonstrate the feasibility of this framework by proposing an approach underpinned by the framework for organizational model discovery, and also conduct experiments on real-life event logs to discover and evaluate organizational models.
no code implementations • 16 Nov 2020 • Jing Yang, James P. Wilson, Shalabh Gupta
With the growth of sensing, control and robotic technologies, autonomous underwater vehicles (AUVs) have become useful assistants to human divers for performing various underwater operations.
no code implementations • 8 Jul 2020 • Kun Li, Jing Yang, Nianhong Jiao, Jinsong Zhang, Yu-Kun Lai
3D face reconstruction from a single image is a challenging problem, especially under partial occlusions and extreme poses.
1 code implementation • ICLR 2020 • Brais Martinez, Jing Yang, Adrian Bulat, Georgios Tzimiropoulos
This paper shows how to train binary networks to within a few percent points ($\sim 3-5 \%$) of the full precision counterpart.
no code implementations • 9 Mar 2020 • Jing Yang, Brais Martinez, Adrian Bulat, Georgios Tzimiropoulos
To this end, we propose a new knowledge distillation method based on transferring feature statistics, specifically the channel-wise mean and variance, from the teacher to the student.
no code implementations • 5 Mar 2020 • Weiqiang Wu, Jing Yang, Cong Shen
In this paper, we investigate the impact of context diversity on stochastic linear contextual bandits.
no code implementations • 29 Feb 2020 • Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang
The decentralized stochastic multi-player multi-armed bandit (MP-MAB) problem, where the collision information is not available to the players, is studied in this paper.
no code implementations • 20 Feb 2020 • Hanshu Cai, Yiwen Gao, Shuting Sun, Na Li, Fuze Tian, Han Xiao, Jianxiu Li, Zhengwu Yang, Xiaowei Li, Qinglin Zhao, Zhenyu Liu, Zhijun Yao, Minqiang Yang, Hong Peng, Jing Zhu, Xiaowei Zhang, Guoping Gao, Fang Zheng, Rui Li, Zhihua Guo, Rong Ma, Jing Yang, Lan Zhang, Xiping Hu, Yumin Li, Bin Hu
The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications.
1 code implementation • 16 Feb 2020 • Xiaowen Shi, Xin Li, Caili Wu, Shuchen Kong, Jing Yang, Liang He
Automatic analysis of highly crowded people has attracted extensive attention from computer vision research.
no code implementations • 25 Nov 2019 • Zhichao Fu, Yu Kong, Yingbin Zheng, Hao Ye, Wenxin Hu, Jing Yang, Liang He
The accuracy of OCR is usually affected by the quality of the input document image and different kinds of marred document images hamper the OCR results.
no code implementations • 16 Sep 2019 • Jingkai Weng, Yujiang Ding, Chengbo Hu, Xue-Feng Zhu, Bin Liang, Jing Yang, Jianchun Cheng
Deep-learning recently show great success across disciplines yet conventionally require time-consuming computer processing or bulky-sized diffractive elements.
no code implementations • 4 Jul 2019 • Zhichao Fu, Tianlong Ma, Yingbin Zheng, Hao Ye, Jing Yang, Liang He
In this paper, we resort to human visual demands of sharp edges and propose a two-phase edge-aware deep network to improve deep image deblurring.
no code implementations • 4 Jul 2019 • Xingjiao Wu, Baohan Xu, Yingbin Zheng, Hao Ye, Jing Yang, Liang He
Crowd counting aims to count the number of instantaneous people in a crowded space, and many promising solutions have been proposed for single image crowd counting.
no code implementations • 23 Jan 2019 • Chao Gan, Jing Yang, Ruida Zhou, Cong Shen
We aim to show that when the user preferences are sufficiently diverse and each arm can be optimal for certain users, the O(log T) regret incurred by exploring the sub-optimal arms under the standard stochastic MAB setting can be reduced to a constant.
1 code implementation • 6 Dec 2018 • Xingjiao Wu, Yingbin Zheng, Hao Ye, Wenxin Hu, Jing Yang, Liang He
Crowd counting, i. e., estimation number of the pedestrian in crowd images, is emerging as an important research problem with the public security applications.
no code implementations • 30 Nov 2018 • Benjamin Doerr, Carsten Witt, Jing Yang
We propose and analyze a self-adaptive version of the $(1,\lambda)$ evolutionary algorithm in which the current mutation rate is part of the individual and thus also subject to mutation.
2 code implementations • ECCV 2018 • Adrian Bulat, Jing Yang, Georgios Tzimiropoulos
This paper is on image and face super-resolution.
1 code implementation • 24 Jul 2018 • Jing Yang, Biao Zhang, Yue Qin, Xiangwen Zhang, Qian Lin, Jinsong Su
Although neural machine translation(NMT) yields promising translation performance, it unfortunately suffers from over- and under-translation is- sues [Tu et al., 2016], of which studies have become research hotspots in NMT.
no code implementations • 9 Jul 2018 • Benjamin Doerr, Carola Doerr, Jing Yang
It has been observed that some working principles of evolutionary algorithms, in particular, the influence of the parameters, cannot be understood from results on the asymptotic order of the runtime, but only from more precise results.
no code implementations • 11 Apr 2018 • Chao Gan, Ruida Zhou, Jing Yang, Cong Shen
Our objective is to understand how the costs and reward of the actions would affect the optimal behavior of the user in both offline and online settings, and design the corresponding opportunistic spectrum access strategies to maximize the expected cumulative net reward (i. e., reward-minus-cost).
no code implementations • E2E NLG Challenge System Descriptions 2018 • Biao Zhang, Jing Yang, Qian Lin, Jinsong Su
This paper describes our system used for the end-to-end (E2E) natural language generation (NLG) challenge.
Ranked #7 on
Data-to-Text Generation
on E2E NLG Challenge
no code implementations • 7 Apr 2017 • Benjamin Doerr, Christian Gießen, Carsten Witt, Jing Yang
We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms in discrete search spaces.
no code implementations • 21 Apr 2015 • Qingshan Liu, Jing Yang, Kaihua Zhang, Yi Wu
Recently, the compressive tracking (CT) method has attracted much attention due to its high efficiency, but it cannot well deal with the large scale target appearance variations due to its data-independent random projection matrix that results in less discriminative features.