no code implementations • 3 Dec 2013 • Yiyi Liao, Yue Wang, Yong liu
We consider the problem of image representation for the tasks of unsupervised learning and semi-supervised learning.
no code implementations • 12 Jun 2015 • Yiyi Liao, Sarath Kodagoda, Yue Wang, Lei Shi, Yong liu
Furthermore, results show that the features automatically learned from the raw input range data can achieve competitive results to the features constructed based on statistical and geometrical information.
no code implementations • 21 Sep 2015 • Xiaofei Wang, Chao Wu, Pengyuan Zhang, Ziteng Wang, Yong liu, Xu Li, Qiang Fu, Yonghong Yan
This paper presents the contribution to the third 'CHiME' speech separation and recognition challenge including both front-end signal processing and back-end speech recognition.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 22 Sep 2015 • Yiyi Liao, Sarath Kodagoda, Yue Wang, Lei Shi, Yong liu
As scene images have larger diversity than the iconic object images, it is more challenging for deep learning methods to automatically learn features from scene images with less samples.
no code implementations • 23 Sep 2015 • Mengmeng Wang, Yong liu
A discriminative model which accounts for the matching degree of local patches is adopted via a bottom ensemble layer, and a generative model which exploits holistic templates is used to search for the object through the middle ensemble layer as well as an adaptive Kalman filter.
no code implementations • 14 Mar 2016 • Kanzhi Wu, Xiaoyang Li, Ravindra Ranasinghe, Gamini Dissanayake, Yong liu
This paper presents a novel appearance and shape feature, RISAS, which is robust to viewpoint, illumination, scale and rotation variations.
no code implementations • 6 Apr 2016 • Xinxing Xu, Joey Tianyi Zhou, IvorW. Tsang, Zheng Qin, Rick Siow Mong Goh, Yong liu
The Support Vector Machine using Privileged Information (SVM+) has been proposed to train a classifier to utilize the additional privileged information that is only available in the training phase but not available in the test phase.
no code implementations • 27 Jun 2016 • Yong Kiam Tan, Xinxing Xu, Yong liu
Recurrent neural networks (RNNs) were recently proposed for the session-based recommendation task.
4 code implementations • 17 Oct 2016 • Yiyi Liao, Lichao Huang, Yue Wang, Sarath Kodagoda, Yinan Yu, Yong liu
Many standard robotic platforms are equipped with at least a fixed 2D laser range finder and a monocular camera.
no code implementations • 15 Mar 2017 • Mengmeng Wang, Daobilige Su, Lei Shi, Yong liu, Jaime Valls Miro
An ultrasonic sensor array is employed to provide the range information from the target person to the robot and Gaussian Process Regression is used for partial location estimation (2-D).
no code implementations • CVPR 2017 • Mengmeng Wang, Yong liu, Zeyi Huang
Structured output support vector machine (SVM) based tracking algorithms have shown favorable performance recently.
no code implementations • 3 Jul 2017 • Peng Yang, Peilin Zhao, Xin Gao, Yong liu
Morever, the proposed algorithm can be scaled up to large-sized datasets after a relaxation.
no code implementations • 23 Nov 2017 • Xingxing Zuo, Xiaojia Xie, Yong liu, Guoquan Huang
In this paper, we develop a robust efficient visual SLAM system that utilizes heterogeneous point and line features.
no code implementations • CVPR 2018 • Yong Liu, Ruiping Wang, Shiguang Shan, Xilin Chen
Context is important for accurate visual recognition.
2 code implementations • 15 Aug 2018 • Wentao Zhu, Yufang Huang, Liang Zeng, Xuming Chen, Yong liu, Zhen Qian, Nan Du, Wei Fan, Xiaohui Xie
Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generating masks of all OARs of interest in one shot.
no code implementations • 20 Oct 2018 • Yong Liu, Min Wu, Chenghao Liu, Xiao-Li Li, Jie Zheng
Moreover, we also incorporate biological knowledge about genes from protein-protein interaction (PPI) data and Gene Ontology (GO).
no code implementations • 21 Nov 2018 • Yong Liu, Lin Shang, Andy Song
First, we propose a Deep Feature Fusion (DFF) method to exploit the diverse information embedded in a deep feature.
no code implementations • NeurIPS 2018 • Jian Li, Yong liu, Rong Yin, Hua Zhang, Lizhong Ding, Weiping Wang
In this paper, we study the generalization performance of multi-class classification and obtain a shaper data-dependent generalization error bound with fast convergence rate, substantially improving the state-of-art bounds in the existing data-dependent generalization analysis.
no code implementations • 5 Dec 2018 • Kai Lei, Bing Zhang, Yong liu, Yang Deng, Dongyu Zhang, Ying Shen
In Question Entity Discovery and Linking (QEDL) problem, traditional methods are challenged because multiple entities in one short question are difficult to be discovered entirely and the incomplete information in short text makes entity linking hard to implement.
no code implementations • 5 Dec 2018 • Ying Shen, Joël Colloc, Armelle Jacquet-Andrieu, Ziyi Guo, Yong liu
Disease Ontology (DO) that pertains to cancer's clinical stages and their corresponding information components is utilized to improve the reasoning ability of a decision support system (DSS).
no code implementations • 5 Dec 2018 • Ken Chen, Fei Chen, Baisheng Lai, Zhongming Jin, Yong liu, Kai Li, Long Wei, Pengfei Wang, Yandong Tang, Jianqiang Huang, Xian-Sheng Hua
To capture the graph dynamics, we use the graph prediction stream to predict the dynamic graph structures, and the predicted structures are fed into the flow prediction stream.
no code implementations • 5 Dec 2018 • Ying Shen, Yang Deng, Kaiqi Yuan, Li Liu, Yong liu
Experiments show that our selected features have achieved a precision rate of 86. 77%, a recall rate of 89. 03% and an F1 score of 87. 89%.
no code implementations • 19 Dec 2018 • Yong Liu, Jian Li, Weiping Wang
We study the risk performance of distributed learning for the regularization empirical risk minimization with fast convergence rate, substantially improving the error analysis of the existing divide-and-conquer based distributed learning.
1 code implementation • 4 Feb 2019 • Chenge Li, Weixi Zhang, Yong liu, Yao Wang
In this work, we treat the FoV prediction as a sequence learning problem, and propose to predict the target user's future FoV not only based on the user's own past FoV center trajectory but also other users' future FoV locations.
no code implementations • 13 Feb 2019 • Yong Liu, Jian Li, Guangjun Wu, Lizhong Ding, Weiping Wang
In this paper, we provide a method to approximate the CV for manifold regularization based on a notion of robust statistics, called Bouligand influence function (BIF).
no code implementations • 26 Feb 2019 • Chaoyue He, Yong liu, Qingyu Guo, Chunyan Miao
To this end, architectural inductive biases such as Markov-Chains, Recurrent models, Convolutional networks and many others have demonstrated reasonable success on this task.
no code implementations • 19 Mar 2019 • Yong Liu, Yinan Zhang, Qiong Wu, Chunyan Miao, Lizhen Cui, Binqiang Zhao, Yin Zhao, Lu Guan
Interactive recommendation that models the explicit interactions between users and the recommender system has attracted a lot of research attentions in recent years.
no code implementations • 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) 2019 • Dan Jin, Jian Xu, Kun Zhao, Fangzhou Hu, Zhengyi Yang, Bing Liu, Tianzi Jiang, Yong liu
Modern advancements in deep learning provide a powerful framework for disease classification based on neuroimaging data.
no code implementations • 19 Apr 2019 • Yong Liu, Pavel Dmitriev, Yifei HUANG, Andrew Brooks, Li Dong
Our results show that fine-tuning of the BERT model outperforms with as few as 300 labeled samples, but underperforms with fewer than 300 labeled samples, relative to all the feature-based approaches using different embeddings.
no code implementations • 16 May 2019 • Qiong Wu, Yong liu, Chunyan Miao, Yin Zhao, Lu Guan, Haihong Tang
With the rapid development of recommender systems, accuracy is no longer the only golden criterion for evaluating whether the recommendation results are satisfying or not.
no code implementations • 27 May 2019 • Guanzhong Tian, Yi Yuan, Yong liu
We propose an end to end deep learning approach for generating real-time facial animation from just audio.
1 code implementation • CVPR 2020 • Jiangning Zhang, Xianfang Zeng, Mengmeng Wang, Yusu Pan, Liang Liu, Yong liu, Yu Ding, Changjie Fan
This paper presents a novel multi-identity face reenactment framework, named FReeNet, to transfer facial expressions from an arbitrary source face to a target face with a shared model.
no code implementations • 4 Jun 2019 • Shengfei Lyu, Linghao Sun, Huixiong Yi, Yong liu, Huanhuan Chen, Chunyan Miao
In the process of translation, CAN obtain the attention matrices that align the two languages.
Low Resource Named Entity Recognition named-entity-recognition +4
1 code implementation • 7 Jun 2019 • Jian Li, Yong liu, Weiping Wang
In this paper, using refined proof techniques, we first extend the optimal rates for distributed learning with random features to the non-attainable case.
no code implementations • 19 Jun 2019 • Guangyao Zhai, Liang Liu, Linjian Zhang, Yong liu
The feature-encoding module encodes the short-term motion feature in an image pair, while the memory-propagating module captures the long-term motion feature in the consecutive image pairs.
no code implementations • 1 Jul 2019 • Yong Liu, Yingtai Xiao, Qiong Wu, Chunyan Miao, Juyong Zhang
Interactive recommender systems that enable the interactions between users and the recommender system have attracted increasing research attentions.
no code implementations • 4 Jul 2019 • Shaohua Li, Yong liu, Xiuchao Sui, Cheng Chen, Gabriel Tjio, Daniel Shu Wei Ting, Rick Siow Mong Goh
Deep learning for medical image classification faces three major challenges: 1) the number of annotated medical images for training are usually small; 2) regions of interest (ROIs) are relatively small with unclear boundaries in the whole medical images, and may appear in arbitrary positions across the x, y (and also z in 3D images) dimensions.
1 code implementation • ICLR 2020 • Weixun Wang, Tianpei Yang, Yong liu, Jianye Hao, Xiaotian Hao, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao
ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them.
1 code implementation • IJCAI 2019 • Qiong Wu, Yong liu, Chunyan Miao, Binqiang Zhao, Yin Zhao, Lu Guan
This paper proposes Personalized Diversity-promoting GAN (PD-GAN), a novel recommendation model to generate diverse, yet relevant recommendations.
no code implementations • ICCV 2019 • Tianyang Shi, Yi Yuan, Changjie Fan, Zhengxia Zou, Zhenwei Shi, Yong liu
Character customization system is an important component in Role-Playing Games (RPGs), where players are allowed to edit the facial appearance of their in-game characters with their own preferences rather than using default templates.
no code implementations • 4 Sep 2019 • Xin Kong, Guangyao Zhai, Baoquan Zhong, Yong liu
In this paper, we propose PASS3D to achieve point-wise semantic segmentation for 3D point cloud.
no code implementations • 6 Sep 2019 • Weixun Wang, Tianpei Yang, Yong liu, Jianye Hao, Xiaotian Hao, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao
In this paper, we design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents.
no code implementations • 8 Sep 2019 • Mingming Zhang, Xingxing Zuo, Yiming Chen, Yong liu, Mingyang Li
In this paper, we focus on motion estimation dedicated for non-holonomic ground robots, by probabilistically fusing measurements from the wheel odometer and exteroceptive sensors.
no code implementations • 9 Sep 2019 • Xingxing Zuo, Patrick Geneva, Woosik Lee, Yong liu, Guoquan Huang
This paper presents a tightly-coupled multi-sensor fusion algorithm termed LiDAR-inertial-camera fusion (LIC-Fusion), which efficiently fuses IMU measurements, sparse visual features, and extracted LiDAR points.
Robotics
1 code implementation • 11 Sep 2019 • Jian Li, Yong liu, Weiping Wang
Vector-valued learning, where the output space admits a vector-valued structure, is an important problem that covers a broad family of important domains, e. g. multi-task learning and transfer learning.
1 code implementation • 11 Sep 2019 • Jian Li, Yong liu, Weiping Wang
The generalization performance of kernel methods is largely determined by the kernel, but common kernels are stationary thus input-independent and output-independent, that limits their applications on complicated tasks.
1 code implementation • 18 Oct 2019 • Yinghuan Shi, Tiexin Qin, Yong liu, Jiwen Lu, Yang Gao, Dinggang Shen
By introducing an unified optimization goal, DeepAugNet intends to combine the data augmentation and the deep model training in an end-to-end training manner which is realized by simultaneously training a hybrid architecture of dueling deep Q-learning algorithm and a surrogate deep model.
no code implementations • 23 Oct 2019 • Yilin Kang, Yong liu, Weiping Wang
By detailed theoretical analysis, we show that in distributed setting, the noise bound and the excess empirical risk bound can be improved by considering different weights held by multiple parties.
no code implementations • 13 Nov 2019 • Xingxing Zuo, Mingming Zhang, Yiming Chen, Yong liu, Guoquan Huang, Mingyang Li
While visual localization or SLAM has witnessed great progress in past decades, when deploying it on a mobile robot in practice, few works have explicitly considered the kinematic (or dynamic) constraints of the real robotic system when designing state estimators.
no code implementations • 25 Nov 2019 • Yong Liu, Weixun Wang, Yujing Hu, Jianye Hao, Xingguo Chen, Yang Gao
Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents.
no code implementations • NeurIPS 2019 • Lizhong Ding, Mengyang Yu, Li Liu, Fan Zhu, Yong liu, Yu Li, Ling Shao
DEAN can be interpreted as a GOF game between two generative networks, where one explicit generative network learns an energy-based distribution that fits the real data, and the other implicit generative network is trained by minimizing a GOF test statistic between the energy-based distribution and the generated data, such that the underlying distribution of the generated data is close to the energy-based distribution.
no code implementations • 11 Dec 2019 • Tianying Wang, Hao Zhang, Wei Qi Toh, Hongyuan Zhu, Cheston Tan, Yan Wu, Yong liu, Wei Jing
The proposed method is able to efficiently generalize the previously learned task by model fusion to solve the environment adaptation problem.
no code implementations • 11 Dec 2019 • Tianying Wang, Wei Qi Toh, Hao Zhang, Xiuchao Sui, Shaohua Li, Yong liu, Wei Jing
The proposed RoboCoDraw system takes a real human face image as input, converts it to a stylized avatar, then draws it with a robotic arm.
Robotics Graphics
1 code implementation • 16 Dec 2019 • Yong Liu, Dongyang Wang, Shichuan Xue, Anqi Huang, Xiang Fu, Xiaogang Qiang, Ping Xu, He-Liang Huang, Mingtang Deng, Chu Guo, Xuejun Yang, Junjie Wu
We demonstrate our method by performing numerical simulations for the tomography of the ground state of a one-dimensional quantum spin chain, using a variational quantum circuit simulator.
no code implementations • 13 Jan 2020 • Shanlin Sun, Yang Liu, Narisu Bai, Hao Tang, Xuming Chen, Qian Huang, Yong liu, Xiaohui Xie
Organs-at-risk (OAR) delineation in computed tomography (CT) is an important step in Radiation Therapy (RT) planning.
1 code implementation • 20 Feb 2020 • Yilin Kang, Yong liu, Ben Niu, Xin-Yi Tong, Likun Zhang, Weiping Wang
By adding noise to the original training data and training with the `perturbed data', we achieve ($\epsilon$,$\delta$)-differential privacy on the final model, along with some kind of privacy on the original data.
no code implementations • 20 Feb 2020 • Yilin Kang, Jian Li, Yong liu, Weiping Wang
Traditionally, the random noise is equally injected when training with different data instances in the field of differential privacy (DP).
no code implementations • 28 Feb 2020 • Jian Li, Yong liu, Weiping Wang
Recently, non-stationary spectral kernels have drawn much attention, owing to its powerful feature representation ability in revealing long-range correlations and input-dependent characteristics.
no code implementations • 4 Mar 2020 • Jun Chen, Yong liu, Hao Zhang, Shengnan Hou, Jian Yang
Meanwhile, we propose a M-bit Inputs and N-bit Weights Network (MINW-Net) trained by AQE, a quantized neural network with 1-3 bits weights and activations.
no code implementations • 9 Mar 2020 • Yong Liu, Lizhong Ding, Weiping Wang
However, the studies on learning theory for general loss functions and hypothesis spaces remain limited.
no code implementations • 9 Mar 2020 • Yong Liu, Lizhong Ding, Weiping Wang
In this paper, we study the statistical properties of kernel $k$-means and obtain a nearly optimal excess clustering risk bound, substantially improving the state-of-art bounds in the existing clustering risk analyses.
1 code implementation • 16 Mar 2020 • Chunfang Deng, Mengmeng Wang, Liang Liu, Yong liu
Small object detection remains an unsolved challenge because it is hard to extract information of small objects with only a few pixels.
no code implementations • 29 Mar 2020 • Xianfang Zeng, Yusu Pan, Mengmeng Wang, Jiangning Zhang, Yong liu
On the one hand, we adopt the deforming autoencoder to disentangle identity and pose representations.
2 code implementations • CVPR 2020 • Liang Liu, Jiangning Zhang, Ruifei He, Yong liu, Yabiao Wang, Ying Tai, Donghao Luo, Chengjie Wang, Jilin Li, Feiyue Huang
Unsupervised learning of optical flow, which leverages the supervision from view synthesis, has emerged as a promising alternative to supervised methods.
Ranked #2 on Optical Flow Estimation on KITTI 2012 unsupervised
1 code implementation • 6 Apr 2020 • Jun Chen, Liang Liu, Yong liu, Xianfang Zeng
Furthermore, we also design a shift vector processing element (SVPE) array to replace all 16-bit multiplications with SHIFT operations in convolution operation on FPGAs.
1 code implementation • 12 Apr 2020 • Shaohua Li, Xiuchao Sui, Jie Fu, Yong liu, Rick Siow Mong Goh
To make CNNs more invariant to transformations, we propose "Feature Lenses", a set of ad-hoc modules that can be easily plugged into a trained model (referred to as the "host model").
no code implementations • 24 Apr 2020 • Yong liu, Susen Yang, Yinan Zhang, Chunyan Miao, Zaiqing Nie, Juyong Zhang
Therefore, they may not be effective in capturing the global dependency between words, and tend to be easily biased by noise review information.
no code implementations • 24 Apr 2020 • Susen Yang, Yong liu, Yonghui Xu, Chunyan Miao, Min Wu, Juyong Zhang
Graph neural networks (GNN) have recently been applied to exploit knowledge graph (KG) for recommendation.
1 code implementation • EMNLP 2020 • Peixiang Zhong, Chen Zhang, Hao Wang, Yong liu, Chunyan Miao
To this end, we propose a new task towards persona-based empathetic conversations and present the first empirical study on the impact of persona on empathetic responding.
3 code implementations • 30 Apr 2020 • Jiangning Zhang, Liang Liu, Zhu-Cun Xue, Yong liu
Audio-guided face reenactment aims at generating photorealistic faces using audio information while maintaining the same facial movement as when speaking to a real person.
no code implementations • 18 May 2020 • Jiangning Zhang, Liang Liu, Chao Xu, Yong liu
Recent works in the person re-identification task mainly focus on the model accuracy while ignore factors related to the efficiency, e. g. model size and latency, which are critical for practical application.
no code implementations • 20 May 2020 • Licheng Wen, Jiaqing Yan, Xuemeng Yang, Yong liu, Yong Gu
We apply a numerical optimization method in the back-end to generate the trajectory.
Robotics
no code implementations • 18 Jun 2020 • Jian Li, Yong liu, Jiankun Liu, Weiping Wang
The encoder and the decoder belong to a graph VAE, mapping architectures between continuous representations and network architectures.
no code implementations • ECCV 2020 • Ran Chen, Yong liu, Mengdan Zhang, Shu Liu, Bei Yu, Yu-Wing Tai
Anchor free methods have defined the new frontier in state-of-the-art object detection researches where accurate bounding box estimation is the key to the success of these methods.
2 code implementations • 29 Jul 2020 • Jiajun Lv, Jinhong Xu, Kewei Hu, Yong liu, Xingxing Zuo
Sensor calibration is the fundamental block for a multi-sensor fusion system.
Robotics
1 code implementation • ECCV 2020 • Jiangning Zhang, Chao Xu, Liang Liu, Mengmeng Wang, Xia Wu, Yong liu, Yunliang Jiang
The proposed DTVNet consists of two submodules: \emph{Optical Flow Encoder} (OFE) and \emph{Dynamic Video Generator} (DVG).
no code implementations • 17 Aug 2020 • Xingxing Zuo, Yulin Yang, Patrick Geneva, Jiajun Lv, Yong liu, Guoquan Huang, Marc Pollefeys
Only the tracked planar points belonging to the same plane will be used for plane initialization, which makes the plane extraction efficient and robust.
Robotics
1 code implementation • 26 Aug 2020 • Xin Kong, Xuemeng Yang, Guangyao Zhai, Xiangrui Zhao, Xianfang Zeng, Mengmeng Wang, Yong liu, Wanlong Li, Feng Wen
First, we propose a novel semantic graph representation for the point cloud scenes by reserving the semantic and topological information of the raw point cloud.
no code implementations • 21 Oct 2020 • Xuemeng Zhang, Shutang You, Yong liu, Yilu Liu
Solar photovoltaic (PV) generation is growing rapidly around the world.
no code implementations • 22 Oct 2020 • Hao Zou, Jinhao Cui, Xin Kong, Chujuan Zhang, Yong liu, Feng Wen, Wanlong Li
A main challenge in 3D single object tracking is how to reduce search space for generating appropriate 3D candidates.
no code implementations • 23 Oct 2020 • Yong liu, Susen Yang, Chenyi Lei, Guoxin Wang, Haihong Tang, Juyong Zhang, Aixin Sun, Chunyan Miao
Side information of items, e. g., images and text description, has shown to be effective in contributing to accurate recommendations.
1 code implementation • 25 Oct 2020 • Jiangning Zhang, Xianfang Zeng, Chao Xu, Jun Chen, Yong liu, Yunliang Jiang
Audio-guided face reenactment aims to generate a photorealistic face that has matched facial expression with the input audio.
1 code implementation • 1 Nov 2020 • Licheng Wen, Zhen Zhang, Zhe Chen, Xiangrui Zhao, Yong liu
In this paper, we give a mathematical formalization of Multi-Agent Path Finding for Car-Like robots (CL-MAPF) problem.
Robotics Multiagent Systems
no code implementations • 4 Nov 2020 • Shanqi Liu, Licheng Wen, Jinhao Cui, Xuemeng Yang, Junjie Cao, Yong liu
We also deploy and validate our method in a real world scenario.
Robotics Multiagent Systems
no code implementations • 5 Nov 2020 • Shanqi Liu, Junjie Cao, Wenzhou Chen, Licheng Wen, Yong liu
In this work, we propose a new imitation learning approach called Hierarchical Imitation Learning from Observation(HILONet), which adopts a hierarchical structure to choose feasible sub-goals from demonstrated observations dynamically.
no code implementations • 9 Nov 2020 • Senrong You, Yong liu, Baiying Lei, Shuqiang Wang
Specifically, FP-GANs firstly divides an MR image into low-frequency global approximation and high-frequency anatomical texture in wavelet domain.
1 code implementation • 21 Nov 2020 • Mengmeng Kuang, Yong liu, Lufei Gao
This paper proposed a novel and straightforward approach to improve the accuracy of progressive multiple protein sequence alignment method.
Ranked #1 on Multiple Sequence Alignment on OXBench
1 code implementation • 14 Dec 2020 • Xiaoyang Lyu, Liang Liu, Mengmeng Wang, Xin Kong, Lina Liu, Yong liu, Xinxin Chen, Yi Yuan
To obtainmore accurate depth estimation in large gradient regions, itis necessary to obtain high-resolution features with spatialand semantic information.
Ranked #7 on Unsupervised Monocular Depth Estimation on KITTI-C
no code implementations • 14 Dec 2020 • Guangyao Zhai, Xin Kong, Jinhao Cui, Yong liu, Zhen Yang
Most end-to-end Multi-Object Tracking (MOT) methods face the problems of low accuracy and poor generalization ability.
no code implementations • 15 Dec 2020 • Lina Liu, Xibin Song, Xiaoyang Lyu, Junwei Diao, Mengmeng Wang, Yong liu, Liangjun Zhang
Then, a refined depth map is further obtained using a residual learning strategy in the coarse-to-fine stage with a coarse depth map and color image as input.
1 code implementation • 15 Dec 2020 • Peixiang Zhong, Yong liu, Hao Wang, Chunyan Miao
We study the problem of imposing conversational goals/keywords on open-domain conversational agents, where the agent is required to lead the conversation to a target keyword smoothly and fast.
no code implementations • 16 Dec 2020 • Hao Tang, Xingwei Liu, Kun Han, Shanlin Sun, Narisu Bai, Xuming Chen, Huang Qian, Yong liu, Xiaohui Xie
State-of-the-art CNN segmentation models apply either 2D or 3D convolutions on input images, with pros and cons associated with each method: 2D convolution is fast, less memory-intensive but inadequate for extracting 3D contextual information from volumetric images, while the opposite is true for 3D convolution.
no code implementations • 18 Dec 2020 • Xingxing Zuo, Nathaniel Merrill, Wei Li, Yong liu, Marc Pollefeys, Guoquan Huang
In this work, we present a lightweight, tightly-coupled deep depth network and visual-inertial odometry (VIO) system, which can provide accurate state estimates and dense depth maps of the immediate surroundings.
no code implementations • 24 Dec 2020 • Qinxu Ding, Yong liu, Chunyan Miao, Fei Cheng, Haihong Tang
Previous interactive recommendation methods primarily focus on learning users' personalized preferences on the relevance properties of an item set.
no code implementations • 1 Jan 2021 • Yuzhe Li, Yong liu, Weipinng Wang, Bo Li, Nan Liu
In this paper, we deduce the influence of $\epsilon$ on utility private learning models through strict mathematical derivation, and propose a novel approximate approach for estimating the utility of any $\epsilon$ value.
no code implementations • ICLR 2021 • Yong liu, Jiankun Liu, Shuqiang Wang
In this paper, we study the statistical properties of distributed kernel ridge regression together with random features (DKRR-RF), and obtain optimal generalization bounds under the basic setting, which can substantially relax the restriction on the number of local machines in the existing state-of-art bounds.
no code implementations • 1 Jan 2021 • Jun Chen, Hanwen Chen, Jiangning Zhang, Wenzhou Chen, Yong liu, Yunliang Jiang
Quantized Neural Networks (QNNs) have achieved an enormous step in improving computational efficiency, making it possible to deploy large models to mobile and miniaturized devices.
no code implementations • ICCV 2021 • Tianxin Huang, Hao Zou, Jinhao Cui, Xuemeng Yang, Mengmeng Wang, Xiangrui Zhao, Jiangning Zhang, Yi Yuan, Yifan Xu, Yong liu
The RFE extracts multiple global features from the incomplete point clouds for different recurrent levels, and the FDC generates point clouds in a coarse-to-fine pipeline.
no code implementations • 14 Jan 2021 • Tongyu Zong, Chen Li, Yuanyuan Lei, Guangyu Li, Houwei Cao, Yong liu
In this paper, we propose Cocktail Edge Caching, that tackles the dynamic popularity and heterogeneity through ensemble learning.
no code implementations • 5 Feb 2021 • Jilin Tang, Yi Yuan, Tianjia Shao, Yong liu, Mengmeng Wang, Kun Zhou
In this paper we tackle the problem of pose guided person image generation, which aims to transfer a person image from the source pose to a novel target pose while maintaining the source appearance.
no code implementations • 8 Feb 2021 • Guangming Yao, Yi Yuan, Tianjia Shao, Shuang Li, Shanqi Liu, Yong liu, Mengmeng Wang, Kun Zhou
The paper proposes a novel generative adversarial network for one-shot face reenactment, which can animate a single face image to a different pose-and-expression (provided by a driving image) while keeping its original appearance.
no code implementations • 11 Feb 2021 • Gaoshang Gong, Longmeng Xu, Yuming Bai, Yongqiang Wang, Songliu Yuan, Yong liu, Zhaoming Tian
Non-trivial spin structures in itinerant magnets can give rise to topological Hall effect (THE) due to the interacting local magnetic moments and conductive electrons.
Strongly Correlated Electrons
1 code implementation • 22 Feb 2021 • Kaichao You, Yong liu, Jianmin Wang, Mingsheng Long
In pursuit of a practical assessment method, we propose to estimate the maximum value of label evidence given features extracted by pre-trained models.
Ranked #3 on Transferability on classification benchmark
no code implementations • 24 Feb 2021 • Yong liu, Xinghua Zhu, Jianzong Wang, Jing Xiao
In addition, using the proposed metric, we investigate the influential factors of risk level.
no code implementations • 31 Mar 2021 • Girmaw Abebe Tadesse, Hamza Javed, Yong liu, Jin Liu, Jiyan Chen, Komminist Weldemariam, Tingting Zhu
We propose an end-to-end deep learning approach, DeepMI, to classify MI from normal cases as well as identifying the time-occurrence of MI (defined as acute, recent and old), using a collection of fusion strategies on 12 ECG leads at data-, feature-, and decision-level.
no code implementations • 19 Apr 2021 • Chenyi Lei, Shixian Luo, Yong liu, Wanggui He, Jiamang Wang, Guoxin Wang, Haihong Tang, Chunyan Miao, Houqiang Li
The pre-trained neural models have recently achieved impressive performances in understanding multimodal content.
no code implementations • 7 May 2021 • Yilin Kang, Yong liu, Jian Li, Weiping Wang
Pairwise learning focuses on learning tasks with pairwise loss functions, depends on pairs of training instances, and naturally fits for modeling relationships between pairs of samples.
1 code implementation • Findings (ACL) 2021 • Chujie Zheng, Yong liu, Wei Chen, Yongcai Leng, Minlie Huang
However, existing methods for empathetic response generation usually either consider only one empathy factor or ignore the hierarchical relationships between different factors, leading to a weak ability of empathy modeling.
no code implementations • 18 May 2021 • Tong Zhang, Yong liu, Peixiang Zhong, Chen Zhang, Hao Wang, Chunyan Miao
The chit-chat-based conversational recommendation systems (CRS) provide item recommendations to users through natural language interactions.
1 code implementation • 20 May 2021 • Shaohua Li, Xiuchao Sui, Xiangde Luo, Xinxing Xu, Yong liu, Rick Goh
Medical image segmentation is important for computer-aided diagnosis.
Ranked #1 on Brain Tumor Segmentation on BRATS 2019
1 code implementation • NeurIPS 2021 • Jiangning Zhang, Chao Xu, Jian Li, Wenzhou Chen, Yabiao Wang, Ying Tai, Shuo Chen, Chengjie Wang, Feiyue Huang, Yong liu
Inspired by biological evolution, we explain the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derive that both of them have consistent mathematical representation.
1 code implementation • 1 Jun 2021 • Jianbiao Mei, Mengmeng Wang, Yeneng Lin, Yi Yuan, Yong liu
Recently, Space-Time Memory Network (STM) based methods have achieved state-of-the-art performance in semi-supervised video object segmentation (VOS).
no code implementations • ICLR 2022 • Yong liu, Xiangning Chen, Minhao Cheng, Cho-Jui Hsieh, Yang You
Current methods usually use extensive data augmentation to increase the batch size, but we found the performance gain with data augmentation decreases as batch size increases, and data augmentation will become insufficient after certain point.
no code implementations • 9 Jun 2021 • Yinan Zhang, Boyang Li, Yong liu, Hao Wang, Chunyan Miao
In this work, we propose a new initialization scheme for user and item embeddings called Laplacian Eigenmaps with Popularity-based Regularization for Isolated Data (LEPORID).
no code implementations • 22 Jun 2021 • Lin Li, Xin Kong, Xiangrui Zhao, Wanlong Li, Feng Wen, Hongbo Zhang, Yong liu
LiDAR-based SLAM system is admittedly more accurate and stable than others, while its loop closure detection is still an open issue.
1 code implementation • 1 Jul 2021 • Lin Li, Xin Kong, Xiangrui Zhao, Tianxin Huang, Yong liu
We also present a two-step global semantic ICP to obtain the 3D pose (x, y, yaw) used to align the point cloud to improve matching performance.
Ranked #1 on Visual Place Recognition on KITTI
2 code implementations • 7 Jul 2021 • Xin Zhou, Aixin Sun, Yong liu, Jie Zhang, Chunyan Miao
Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions.
1 code implementation • 10 Jul 2021 • Shaohua Li, Xiuchao Sui, Jie Fu, Huazhu Fu, Xiangde Luo, Yangqin Feng, Xinxing Xu, Yong liu, Daniel Ting, Rick Siow Mong Goh
Thus, the chance of overfitting the annotations is greatly reduced, and the model can perform robustly on the target domain after being trained on a few annotated images.
no code implementations • 19 Jul 2021 • Shaojie Li, Yong liu
the sample size $n$ for ERM and SGD with milder assumptions in convex learning and similar high probability rates of order $\mathcal{O} (1/n)$ in nonconvex learning, rather than in expectation.
no code implementations • journal 2021 • Yuanguo Lin, Shibo Feng, Fan Lin, Wenhua Zeng, Yong liu, Pengcheng Wu
In this paper, we propose a novel course recommendation framework, named Dynamic Attention and hierarchical Reinforcement Learning (DARL), to improve the adaptivity of the recommendation model.
no code implementations • 21 Jul 2021 • Qiankun Zuo, Baiying Lei, Yanyan Shen, Yong liu, Zhiguang Feng, Shuqiang Wang
Then two hypergraphs are constructed from the latent representations and the adversarial network based on graph convolution is employed to narrow the distribution difference of hyperedge features.
no code implementations • 21 Jul 2021 • Junren Pan, Baiying Lei, Yanyan Shen, Yong liu, Zhiguang Feng, Shuqiang Wang
Using multimodal neuroimaging data to characterize brain network is currently an advanced technique for Alzheimer's disease(AD) Analysis.
no code implementations • 21 Jul 2021 • Bowen Hu, Baiying Lei, Yanyan Shen, Yong liu, Shuqiang Wang
Fusing medical images and the corresponding 3D shape representation can provide complementary information and microstructure details to improve the operational performance and accuracy in brain surgery.
no code implementations • 23 Jul 2021 • Bowen Hu, Baiying Lei, Shuqiang Wang, Yong liu, BingChuan Wang, Min Gan, Yanyan Shen
A branching predictor and several hierarchical attention pipelines are constructed to generate point clouds that accurately describe the incomplete images and then complete these point clouds with high quality.
1 code implementation • 25 Jul 2021 • Fuzhao Xue, Ziji Shi, Futao Wei, Yuxuan Lou, Yong liu, Yang You
To achieve better performance with fewer trainable parameters, recent methods are proposed to go shallower by parameter sharing or model compressing along with the depth.
Ranked #663 on Image Classification on ImageNet
2 code implementations • ICCV 2021 • Lina Liu, Xibin Song, Mengmeng Wang, Yong liu, Liangjun Zhang
Meanwhile, to guarantee that the day and night images contain the same information, the domain-separated network takes the day-time images and corresponding night-time images (generated by GAN) as input, and the private and invariant feature extractors are learned by orthogonality and similarity loss, where the domain gap can be alleviated, thus better depth maps can be expected.
2 code implementations • 17 Sep 2021 • Mengmeng Wang, Jiazheng Xing, Yong liu
Moreover, to handle the deficiency of label texts and make use of tremendous web data, we propose a new paradigm based on this multimodal learning framework for action recognition, which we dub "pre-train, prompt and fine-tune".
Ranked #2 on Action Recognition In Videos on Kinetics-400
no code implementations • 22 Sep 2021 • Yuanguo Lin, Yong liu, Fan Lin, Lixin Zou, Pengcheng Wu, Wenhua Zeng, Huanhuan Chen, Chunyan Miao
To understand the challenges and relevant solutions, there should be a reference for researchers and practitioners working on RL-based recommender systems.
1 code implementation • 23 Sep 2021 • Xuemeng Yang, Hao Zou, Xin Kong, Tianxin Huang, Yong liu, Wanlong Li, Feng Wen, Hongbo Zhang
Specifically, the network takes a raw point cloud as input, and merges the features from the segmentation branch into the completion branch hierarchically to provide semantic information.
Ranked #4 on 3D Semantic Scene Completion on SemanticKITTI
no code implementations • IEEE International Workshop on Intelligent Robots and Systems (IROS) 2021 • Hao Zou, Xuemeng Yang, Tianxin Huang, Chujuan Zhang, Yong liu, Wanlong Li, Feng Wen, Hongbo Zhang
An efficient 3D scene perception algorithm is a vital component for autonomous driving and robotics systems.
Ranked #6 on 3D Semantic Scene Completion on SemanticKITTI
no code implementations • 29 Sep 2021 • Jun Chen, Tianxin Huang, Wenzhou Chen, Yong liu
The Ricci flow is a method of manifold surgery, which can trim manifolds to more regular.
no code implementations • 29 Sep 2021 • Jun Chen, Hanwen Chen, Jiangning Zhang, Yuang Liu, Tianxin Huang, Yong liu
Quantized Neural Networks (QNNs) aim at replacing full-precision weights $\boldsymbol{W}$ with quantized weights $\boldsymbol{\hat{W}}$, which make it possible to deploy large models to mobile and miniaturized devices easily.
no code implementations • 29 Sep 2021 • Ling Cheng, Wei Wei, Feida Zhu, Yong liu, Chunyan Miao
However, those fusion-based models, they are still criticized for the lack of geometry information for inter and intra attention refinement.
no code implementations • 29 Sep 2021 • Yong liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, Yang You
Large-batch training is an important direction for distributed machine learning, which can improve the utilization of large-scale clusters and therefore accelerate the training process.
no code implementations • 29 Sep 2021 • Bowei Zhu, Yong liu
Influence functions are classic techniques from robust statistics based on first-order Taylor approximations that have been widely used in the machine learning community to estimate small perturbations of datasets accurately to the model.
no code implementations • 29 Sep 2021 • Jiechao Guan, Zhiwu Lu, Yong liu
In particular, we identify that when the number of training task is large, utilizing a prior generated from an informative hyperposterior can achieve the same order of PAC-Bayes-kl bound as that obtained through setting a localized distribution-dependent prior for a novel task.
no code implementations • ICLR 2022 • Shaojie Li, Yong liu
In this paper, we provide improved generalization analyses for almost all existing generalization measures of minimax problems, which enables the minimax problems to establish sharper bounds of order $\mathcal{O}\left( 1/n \right)$, significantly, with high probability.
1 code implementation • 1 Oct 2021 • Meng Liu, Yong liu
Therefore, we propose a new inductive network representation learning method called MNCI by mining neighborhood and community influences in temporal networks.
no code implementations • 12 Oct 2021 • Qiankun Zuo, Baiying Lei, Shuqiang Wang, Yong liu, BingChuan Wang, Yanyan Shen
The proposed model can evaluate characteristics of abnormal brain connections at different stages of Alzheimer's disease, which is helpful for cognitive disease study and early treatment.
no code implementations • 12 Oct 2021 • Junren Pan, Baiying Lei, Shuqiang Wang, BingChuan Wang, Yong liu, Yanyan Shen
In this work, a novel decoupling generative adversarial network (DecGAN) is proposed to detect abnormal neural circuits for AD.
no code implementations • 20 Oct 2021 • Yidan Hu, Yong liu, Chunyan Miao, Gongqi Lin, Yuan Miao
In this paper, we propose a novel explanation generation framework, named Hierarchical Aspect-guided explanation Generation (HAG), for explainable recommendation.
1 code implementation • 20 Oct 2021 • Kaichao You, Yong liu, Ziyang Zhang, Jianmin Wang, Michael I. Jordan, Mingsheng Long
(2) The best ranked PTM can either be fine-tuned and deployed if we have no preference for the model's architecture or the target PTM can be tuned by the top $K$ ranked PTMs via a Bayesian procedure that we propose.
no code implementations • 28 Oct 2021 • Mengmeng Wang, Xiaoqian Yang, Yong liu
Visual object tracking performance has been dramatically improved in recent years, but some severe challenges remain open, like distractors and occlusions.
no code implementations • 4 Nov 2021 • WeiFu Fu, Congchong Nie, Ting Sun, Jun Liu, Tianliang Zhang, Yong liu
Our method focuses on the problem in following two aspects: the long-tail distribution and the segmentation quality of mask and boundary.
Ranked #3 on Instance Segmentation on LVIS v1.0 val
no code implementations • 9 Nov 2021 • Shaojie Li, Yong liu
We first successfully establish learning rates for these algorithms in a general nonconvex setting, where the analysis sheds insights on the trade-off between optimization and generalization and the role of early-stopping.
no code implementations • 14 Nov 2021 • Shihao Shao, Yong liu, Qinghua Cui
Here we presented a layer-stress deep learning framework (x-NN) which implemented automatic and wise depth decision on shallow or deep feature map in a deep network through firstly designing enough number of layers and then trading off them by Multi-Head Attention Block.
no code implementations • 16 Nov 2021 • Jun Chen, Tianxin Huang, Wenzhou Chen, Yong liu
During the training process of the neural network, we observe that its metric will also regularly converge to the linearly nearly Euclidean metric, which is consistent with the convergent behavior of linearly nearly Euclidean metrics under the Ricci-DeTurck flow.
no code implementations • 17 Nov 2021 • Yulan Hu, Yong liu
Benefiting from the strong ability of the pre-trained model, the research on Chinese Word Segmentation (CWS) has made great progress in recent years.
no code implementations • 21 Nov 2021 • Zizhang Li, Mengmeng Wang, Jianbiao Mei, Yong liu
Referring image segmentation is a typical multi-modal task, which aims at generating a binary mask for referent described in given language expressions.
Ranked #1 on Referring Expression Segmentation on G-Ref test B
no code implementations • 25 Nov 2021 • Wen Yu, Baiying Lei, Yanyan Shen, Shuqiang Wang, Yong liu, Zhiguang Feng, Yong Hu, Michael K. Ng
In this work, a novel Multidirectional Perception Generative Adversarial Network (MP-GAN) is proposed to visualize the morphological features indicating the severity of AD for patients of different stages.
no code implementations • NeurIPS 2021 • Yong liu
In this paper, we study the statistical properties of kernel $k$-means and Nystr\"{o}m-based kernel $k$-means, and obtain optimal clustering risk bounds, which improve the existing risk bounds.
no code implementations • NeurIPS 2021 • Shaogao Lv, Junhui Wang, Jiankun Liu, Yong liu
In this paper, we provide theoretical results of estimation bounds and excess risk upper bounds for support vector machine (SVM) with sparse multi-kernel representation.
no code implementations • NeurIPS 2021 • Shaojie Li, Yong liu
In the smoothness scenario, we provide generalization bounds that are not only a logarithmic dependency on the label set cardinality but a faster convergence rate of order $\mathcal{O}(\frac{1}{n})$ on the sample size $n$.
no code implementations • 3 Dec 2021 • Jie Zhu, Huabin Huang, Banghuai Li, Yong liu, Leye Wang
Inspired by the generated sharp edges of superpixel blocks, we employ superpixel to guide the information passing within feature map.
1 code implementation • NeurIPS 2021 • Chenxin Tao, Zizhang Li, Xizhou Zhu, Gao Huang, Yong liu, Jifeng Dai
In this paper, we propose Parameterized AP Loss, where parameterized functions are introduced to substitute the non-differentiable components in the AP calculation.
no code implementations • 20 Dec 2021 • Xianfang Zeng, Jiangning Zhang, Liang Liu, Guangzhong Tian, Yong liu
To tackle this problem, we propose a novel domain-adaptive degradation network for face super-resolution in the wild.
no code implementations • 21 Dec 2021 • Jun Chen, Yuang Liu, Xiangrui Zhao, Mengmeng Wang, Yong liu
As a result, we prove that, if initial metrics have an $L^2$-norm perturbation which deviates from the Hyperbolic metric on the Poincar\'e ball, the scaled Ricci-DeTurck flow of such metrics smoothly and exponentially converges to the Hyperbolic metric.
no code implementations • CVPR 2022 • Huayi Tang, Yong liu
However, we observe that learning from data with more views is not guaranteed to achieve better clustering performance than from data with fewer views.
2 code implementations • 5 Jan 2022 • Gongxin Yao, YiWei Chen, Yong liu, Xiaomin Hu, Yu Pan
Single-photon light detection and ranging (LiDAR) has been widely applied to 3D imaging in challenging scenarios.
2 code implementations • 7 Jan 2022 • YiWei Chen, Gongxin Yao, Yong liu, Hongye Su, Xiaomin Hu, Yu Pan
Photon-efficient imaging with the single-photon light detection and ranging (LiDAR) captures the three-dimensional (3D) structure of a scene by only a few detected signal photons per pixel.
no code implementations • 12 Jan 2022 • Jiangning Zhang, Chao Xu, Jian Li, Yue Han, Yabiao Wang, Ying Tai, Yong liu
In the practical application of restoring low-resolution gray-scale images, we generally need to run three separate processes of image colorization, super-resolution, and dows-sampling operation for the target device.
no code implementations • 16 Jan 2022 • Fan Wang, Chaofan Zhang, Fulin Tang, Hongkui Jiang, Yihong Wu, Yong liu
In this paper, we present a novel lightweight object-level mapping and localization method with high accuracy and robustness.
1 code implementation • 14 Feb 2022 • Xi Jiang, Guoyang Xie, Jinbao Wang, Yong liu, Chengjie Wang, Feng Zheng, Yaochu Jin
In this survey, we are the first one to provide a comprehensive review of visual sensory AD and category into three levels according to the form of anomalies.
1 code implementation • 18 Feb 2022 • Ying Chen, Dihe Huang, Shang Xu, Jianlin Liu, Yong liu
Local image feature matching under large appearance, viewpoint, and distance changes is challenging yet important.
1 code implementation • 1 Mar 2022 • Yufei Liang, Jiangning Zhang, Shiwei Zhao, Runze Wu, Yong liu, Shuwen Pan
Density-based and classification-based methods have ruled unsupervised anomaly detection in recent years, while reconstruction-based methods are rarely mentioned for the poor reconstruction ability and low performance.
Ranked #39 on Anomaly Detection on MVTec AD
1 code implementation • CVPR 2022 • Fan Yang, Kai Wu, Shuyi Zhang, Guannan Jiang, Yong liu, Feng Zheng, Wei zhang, Chengjie Wang, Long Zeng
Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw data utilization.
2 code implementations • CVPR 2022 • Yong liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, Yang You
Recently, Sharpness-Aware Minimization (SAM), which connects the geometry of the loss landscape and generalization, has demonstrated significant performance boosts on training large-scale models such as vision transformers.
no code implementations • CVPR 2022 • Chao Xu, Jiangning Zhang, Miao Hua, Qian He, Zili Yi, Yong liu
This paper presents a novel Region-Aware Face Swapping (RAFSwap) network to achieve identity-consistent harmonious high-resolution face generation in a local-global manner: \textbf{1)} Local Facial Region-Aware (FRA) branch augments local identity-relevant features by introducing the Transformer to effectively model misaligned cross-scale semantic interaction.
1 code implementation • CVPR 2022 • Xiuchao Sui, Shaohua Li, Xue Geng, Yan Wu, Xinxing Xu, Yong liu, Rick Goh, Hongyuan Zhu
This is mainly because the correlation volume, the basis of pixel matching, is computed as the dot product of the convolutional features of the two images.
Ranked #9 on Optical Flow Estimation on KITTI 2015 (train)
no code implementations • 11 Apr 2022 • Yilin Kang, Yong liu, Jian Li, Weiping Wang
To the best of our knowledge, this is the first time to analyze the generalization performance of general minimax paradigm, taking differential privacy into account.
no code implementations • 22 Apr 2022 • Yilin Kang, Yong liu, Jian Li, Weiping Wang
In this paper, by introducing Generalized Bernstein condition, we propose the first $\mathcal{O}\big(\frac{\sqrt{p}}{n\epsilon}\big)$ high probability excess population risk bound for differentially private algorithms under the assumptions $G$-Lipschitz, $L$-smooth, and Polyak-{\L}ojasiewicz condition, based on gradient perturbation method.
no code implementations • 30 Apr 2022 • Shaojie Li, Sheng Ouyang, Yong liu
The theoretical analysis of spectral clustering mainly focuses on consistency, while there is relatively little research on its generalization performance.
1 code implementation • 1 May 2022 • Jian Li, Yong liu, Yingying Zhang
Recent theoretical studies illustrated that kernel ridgeless regression can guarantee good generalization ability without an explicit regularization.
1 code implementation • 25 May 2022 • Yimin Ou, Rui Yang, Lufan Ma, Yong liu, Jiangpeng Yan, Shang Xu, Chengjie Wang, Xiu Li
Existing instance segmentation methods have achieved impressive performance but still suffer from a common dilemma: redundant representations (e. g., multiple boxes, grids, and anchor points) are inferred for one instance, which leads to multiple duplicated predictions.
1 code implementation • 28 May 2022 • Yong liu, Haixu Wu, Jianmin Wang, Mingsheng Long
However, their performance can degenerate terribly on non-stationary real-world data in which the joint distribution changes over time.
no code implementations • 30 May 2022 • Yixin Zhang, Yong liu, Yonghui Xu, Hao Xiong, Chenyi Lei, wei he, Lizhen Cui, Chunyan Miao
Specifically, GCL4SR employs a Weighted Item Transition Graph (WITG), built based on interaction sequences of all users, to provide global context information for each interaction and weaken the noise information in the sequence data.
no code implementations • 6 Jun 2022 • Yuzhe Li, Yong liu, Bo Li, Weiping Wang, Nan Liu
In this paper, we focus our attention on private Empirical Risk Minimization (ERM), which is one of the most commonly used data analysis method.
1 code implementation • 19 Jun 2022 • Jiangning Zhang, Xiangtai Li, Yabiao Wang, Chengjie Wang, Yibo Yang, Yong liu, DaCheng Tao
Motivated by biological evolution, this paper explains the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derives that both have consistent mathematical formulation.
no code implementations • 29 Jun 2022 • Yinan Zhang, Boyang Li, Yong liu, You Yuan, Chunyan Miao
Multi-shot CRS is designed to make recommendations multiple times until the user either accepts the recommendation or leaves at the end of their patience.
2 code implementations • 13 Jul 2022 • Xin Zhou, HongYu Zhou, Yong liu, Zhiwei Zeng, Chunyan Miao, Pengwei Wang, Yuan You, Feijun Jiang
Besides the user-item interaction graph, existing state-of-the-art methods usually use auxiliary graphs (e. g., user-user or item-item relation graph) to augment the learned representations of users and/or items.
1 code implementation • 16 Jul 2022 • Yong liu, Ran Yu, Fei Yin, Xinyuan Zhao, Wei Zhao, Weihao Xia, Yujiu Yang
However, they mainly focus on better matching between the current frame and the memory frames without explicitly paying attention to the quality of the memory.
Ranked #11 on Semi-Supervised Video Object Segmentation on DAVIS 2016 (using extra training data)
1 code implementation • 17 Jul 2022 • Zizhang Li, Mengmeng Wang, Huaijin Pi, Kechun Xu, Jianbiao Mei, Yong liu
However, the redundant parameters within the network structure can cause a large model size when scaling up for desirable performance.
Ranked #4 on Video Reconstruction on UVG
1 code implementation • CVPR 2023 • Dihe Huang, Ying Chen, Shang Xu, Yong liu, Wenlong Wu, Yikang Ding, Chengjie Wang, Fan Tang
The detector-free feature matching approaches are currently attracting great attention thanks to their excellent performance.
1 code implementation • 22 Jul 2022 • Xin Zhou, Donghui Lin, Yong liu, Chunyan Miao
Specifically, these models usually aggregate all layer embeddings for node updating and achieve their best recommendation performance within a few layers because of over-smoothing.
no code implementations • 31 Jul 2022 • Guangyao Zhai, Yu Zheng, Ziwei Xu, Xin Kong, Yong liu, Benjamin Busam, Yi Ren, Nassir Navab, Zhengyou Zhang
In this paper, we introduce DA$^2$, the first large-scale dual-arm dexterity-aware dataset for the generation of optimal bimanual grasping pairs for arbitrary large objects.
1 code implementation • 3 Aug 2022 • Xiangrui Zhao, Sheng Yang, Tianxin Huang, Jun Chen, Teng Ma, Mingyang Li, Yong liu
To repetitively extract them as features and perform association between discrete LiDAR frames for registration, we propose the first learning-based feature segmentation and description model for 3D lines in LiDAR point cloud.
no code implementations • Knowledge-Based Systems 2022 • Changan Yi, Haotian Chen, Yonghui Xu, Yong liu, Lei Jiang, Haishu Tan
Accordingly, ATPL will use the pseudo-labeled information to improve the adversarial training process, which can guarantee the feature transferability by generating adversarial data to fill in the domain gap.
no code implementations • 21 Aug 2022 • Yongwei Wang, Yong liu, Zhiqi Shen
However, there still lack efforts to evaluate the robustness of such CF systems in deployment.
1 code implementation • 29 Aug 2022 • Yifeng Zhou, Chuming Lin, Donghao Luo, Yong liu, Ying Tai, Chengjie Wang, Mingang Chen
Although some Unsupervised Degradation Prediction (UDP) methods are proposed to bypass this problem, the \textit{inconsistency} between degradation embedding and SR feature is still challenging.
no code implementations • 31 Aug 2022 • Jianlin Liu, Zhuofei Huang, Dihe Huang, Shang Xu, Ying Chen, Yong liu
3D object detection from monocular image(s) is a challenging and long-standing problem of computer vision.
no code implementations • 13 Sep 2022 • Zhenfeng Xue, Jiandang Yang, Jie Ren, Yong liu
This method can be viewed as a hybrid of exemplar-based and learning-based method, and it decouples the colorization process and learning process so as to generate various color styles for the same gray image.
no code implementations • 20 Sep 2022 • Dihe Huang, Ying Chen, Yikang Ding, Jinli Liao, Jianlin Liu, Kai Wu, Qiang Nie, Yong liu, Chengjie Wang, Zhiheng Li
In MDRNet, the Spatial-aware Dimensionality Reduction (SDR) is designed to dynamically focus on the valuable parts of the object during voxel-to-BEV feature transformation.
1 code implementation • 25 Sep 2022 • Xiaofeng Lei, Shaohua Li, Xinxing Xu, Huazhu Fu, Yong liu, Yih-Chung Tham, Yangqin Feng, Mingrui Tan, Yanyu Xu, Jocelyn Hui Lin Goh, Rick Siow Mong Goh, Ching-Yu Cheng
Therefore, localization has its unique challenges different from segmentation or detection.
no code implementations • 29 Sep 2022 • Yunliang Jiang, Chenyang Gu, Zhenfeng Xue, Xiongtao Zhang, Yong liu
As a special case of common object removal, image person removal is playing an increasingly important role in social media and criminal investigation domains.
no code implementations • 30 Sep 2022 • Li Zhang, Yong liu, Shaoteng Liu, Tianshu Yang, Yexin Wang, Xinpeng Zhang, Hanzhou Wu
Intellectual property protection of deep neural networks is receiving attention from more and more researchers, and the latest research applies model watermarking to generative models for image processing.
3 code implementations • 5 Oct 2022 • Haixu Wu, Tengge Hu, Yong liu, Hang Zhou, Jianmin Wang, Mingsheng Long
TimesBlock can discover the multi-periodicity adaptively and extract the complex temporal variations from transformed 2D tensors by a parameter-efficient inception block.
no code implementations • 6 Oct 2022 • Chen Li, Xiaoyu Wang, Tongyu Zong, Houwei Cao, Yong liu
Edge caching plays an increasingly important role in boosting user content retrieval performance while reducing redundant network traffic.
1 code implementation • 11 Oct 2022 • Yong liu, Ran Yu, Jiahao Wang, Xinyuan Zhao, Yitong Wang, Yansong Tang, Yujiu Yang
Besides, we empirically find low frequency feature should be enhanced in encoder (backbone) while high frequency for decoder (segmentation head).
1 code implementation • 5 Nov 2022 • Xin Zhou, Jinglong Wang, Yong liu, Xingyu Wu, Zhiqi Shen, Cyril Leung
Providing accurate estimated time of package delivery on users' purchasing pages for e-commerce platforms is of great importance to their purchasing decisions and post-purchase experiences.
no code implementations • 1 Dec 2022 • Meng Wang, Kai Yu, Chun-Mei Feng, Ke Zou, Yanyu Xu, Qingquan Meng, Rick Siow Mong Goh, Yong liu, Huazhu Fu
Specifically, aiming at improving the model's ability to learn the complex pathological features of retinal edema lesions in OCT images, we develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module of our newly designed.