Search Results for author: Kang Li

Found 48 papers, 17 papers with code

ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models

no code implementations30 May 2023 Huahui Yi, Ziyuan Qin, Wei Xu, Miaotian Guo, Kun Wang, Shaoting Zhang, Kang Li, Qicheng Lao

To achieve this, we propose a Concept Embedding Search (ConES) approach by optimizing prompt embeddings -- without the need of the text encoder -- to capture the 'concept' of the image modality through a variety of task objectives.

Instance Segmentation Prompt Engineering +2

Joint Uplink and Downlink Resource Allocation Towards Energy-efficient Transmission for URLLC

no code implementations25 May 2023 Kang Li, Pengcheng Zhu, Yan Wang, Fu-Chun Zheng, Xiaohu You

With the proposed packet delivery mechanism, we jointly optimize bandwidth allocation and power control of uplink and downlink, antenna configuration, and subchannel assignment to minimize the average total power under the constraint of URLLC transmission requirements.

SAM on Medical Images: A Comprehensive Study on Three Prompt Modes

no code implementations28 Apr 2023 Dongjie Cheng, Ziyuan Qin, Zekun Jiang, Shaoting Zhang, Qicheng Lao, Kang Li

As the first promptable foundation model for segmentation tasks, it was trained on a large dataset with an unprecedented number of images and annotations.

Image Segmentation Medical Image Segmentation +1

Understanding Overfitting in Adversarial Training via Kernel Regression

no code implementations13 Apr 2023 Teng Zhang, Kang Li

Adversarial training and data augmentation with noise are widely adopted techniques to enhance the performance of neural networks.

Data Augmentation regression

HD-GCN:A Hybrid Diffusion Graph Convolutional Network

no code implementations31 Mar 2023 Zhi Yang, Kang Li, Haitao Gan, Zhongwei Huang, Ming Shi

HD-GCN utilizes hybrid diffusion by combining information diffusion between neighborhood nodes in the feature space and adjacent nodes in the adjacency matrix.

Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillation

1 code implementation25 Mar 2023 Xiaoxiao He, Chaowei Tan, Bo Liu, Liping Si, Weiwu Yao, Liang Zhao, Di Liu, Qilong Zhangli, Qi Chang, Kang Li, Dimitris N. Metaxas

The supervised learning of the proposed method extracts features from limited labeled data in each client, while the unsupervised data is used to distill both feature and response-based knowledge from a national data repository to further improve the accuracy of the collaborative model and reduce the communication cost.

Federated Learning Few-Shot Learning +1

Towards General Purpose Medical AI: Continual Learning Medical Foundation Model

no code implementations12 Mar 2023 Huahui Yi, Ziyuan Qin, Qicheng Lao, Wei Xu, Zekun Jiang, Dequan Wang, Shaoting Zhang, Kang Li

Therefore, in this work, we further explore the possibility of leveraging pre-trained VLMs as medical foundation models for building general-purpose medical AI, where we thoroughly investigate three machine-learning paradigms, i. e., domain/task-specialized learning, joint learning, and continual learning, for training the VLMs and evaluate their generalization performance on cross-domain and cross-task test sets.

Continual Learning

AST-SED: An Effective Sound Event Detection Method Based on Audio Spectrogram Transformer

no code implementations7 Mar 2023 Kang Li, Yan Song, Li-Rong Dai, Ian McLoughlin, Xin Fang, Lin Liu

In this paper, we propose an effective sound event detection (SED) method based on the audio spectrogram transformer (AST) model, pretrained on the large-scale AudioSet for audio tagging (AT) task, termed AST-SED.

Audio Tagging Event Detection +1

CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation

1 code implementation22 Nov 2022 Ran Gu, Guotai Wang, Jiangshan Lu, Jingyang Zhang, Wenhui Lei, Yinan Chen, Wenjun Liao, Shichuan Zhang, Kang Li, Dimitris N. Metaxas, Shaoting Zhang

First, a disentangle network is proposed to decompose an image into a domain-invariant anatomical representation and a domain-specific style code, where the former is sent to a segmentation model that is not affected by the domain shift, and the disentangle network is regularized by a decoder that combines the anatomical and style codes to reconstruct the input image.

Disentanglement Domain Generalization +3

Domain-incremental Cardiac Image Segmentation with Style-oriented Replay and Domain-sensitive Feature Whitening

no code implementations9 Nov 2022 Kang Li, Lequan Yu, Pheng-Ann Heng

Particularly, we first present a style-oriented replay module to enable structure-realistic and memory-efficient reproduction of past data, and then incorporate the replayed past data to jointly optimize the model with current data to alleviate catastrophic forgetting.

Image Segmentation Incremental Learning +2

Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study

1 code implementation30 Sep 2022 Ziyuan Qin, Huahui Yi, Qicheng Lao, Kang Li

The large-scale pre-trained vision language models (VLM) have shown remarkable domain transfer capability on natural images.

PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation

1 code implementation19 Aug 2022 Guotai Wang, Xiangde Luo, Ran Gu, Shuojue Yang, Yijie Qu, Shuwei Zhai, Qianfei Zhao, Kang Li, Shaoting Zhang

Existing toolkits mainly focus on fully supervised segmentation and require full and accurate pixel-level annotations that are time-consuming and difficult to acquire for segmentation tasks, which makes learning from imperfect labels highly desired for reducing the annotation cost.

Image Segmentation Medical Image Segmentation +2

Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures

1 code implementation18 Aug 2022 Ran Gu, Jingyang Zhang, Guotai Wang, Wenhui Lei, Tao Song, Xiaofan Zhang, Kang Li, Shaoting Zhang

To solve this problem, we propose Contrastive Semi-supervised learning for Cross Anatomy Domain Adaptation (CS-CADA) that adapts a model to segment similar structures in a target domain, which requires only limited annotations in the target domain by leveraging a set of existing annotated images of similar structures in a source domain.

Anatomy Contrastive Learning +4

PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge Distillation

1 code implementation11 Aug 2022 Shuwei Zhai, Guotai Wang, Xiangde Luo, Qiang Yue, Kang Li, Shaoting Zhang

The success of Convolutional Neural Networks (CNNs) in 3D medical image segmentation relies on massive fully annotated 3D volumes for training that are time-consuming and labor-intensive to acquire.

Brain Tumor Segmentation Image Segmentation +3

FlowX: Towards Explainable Graph Neural Networks via Message Flows

1 code implementation26 Jun 2022 Shurui Gui, Hao Yuan, Jie Wang, Qicheng Lao, Kang Li, Shuiwang Ji

We investigate the explainability of graph neural networks (GNNs) as a step towards elucidating their working mechanisms.


HMRNet: High and Multi-Resolution Network with Bidirectional Feature Calibration for Brain Structure Segmentation in Radiotherapy

1 code implementation7 Jun 2022 Hao Fu, Guotai Wang, Wenhui Lei, Wei Xu, Qianfei Zhao, Shichuan Zhang, Kang Li, Shaoting Zhang

Accurate segmentation of Anatomical brain Barriers to Cancer spread (ABCs) plays an important role for automatic delineation of Clinical Target Volume (CTV) of brain tumors in radiotherapy.

Recursive 3D Segmentation of Shoulder Joint with Coarse-scanned MR Image

1 code implementation13 Mar 2022 Xiaoxiao He, Chaowei Tan, Virak Tan, Kang Li

For diagnosis of shoulder illness, it is essential to look at the morphology deviation of scapula and humerus from the medical images that are acquired from Magnetic Resonance (MR) imaging.

Counterfactual Regret Minimization for Anti-jamming Game of Frequency Agile Radar

no code implementations21 Feb 2022 Huayue Li, Zhaowei Han, Wenqiang Pu, Liangqi Liu, Kang Li, Bo Jiu

Numerical simulations demonstrates the effectiveness of deep CFR algorithm for approximately finding NE and obtaining the best response strategy.

Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach

no code implementations24 Nov 2021 Yang Li, Kang Li, Zhen Yang, Yang Yu, Runnan Xu, Miaosen Yang

In order to solve this model, this research combines Jaya algorithm and interior point method (IPM) to develop a hybrid analysis-heuristic solution method called Jaya-IPM, where the lower- and upper- levels are respectively addressed by the IPM and the Jaya, and the scheduling scheme is obtained via iterations between the two levels.


WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image

2 code implementations3 Nov 2021 Xiangde Luo, Wenjun Liao, Jianghong Xiao, Jieneng Chen, Tao Song, Xiaofan Zhang, Kang Li, Dimitris N. Metaxas, Guotai Wang, Shaoting Zhang

Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation.

Image Segmentation Medical Image Segmentation +3

CTFN: Hierarchical Learning for Multimodal Sentiment Analysis Using Coupled-Translation Fusion Network

no code implementations ACL 2021 Jiajia Tang, Kang Li, Xuanyu Jin, Andrzej Cichocki, Qibin Zhao, Wanzeng Kong

In this work, the coupled-translation fusion network (CTFN) is firstly proposed to model bi-direction interplay via couple learning, ensuring the robustness in respect to missing modalities.

Multimodal Sentiment Analysis Translation

Unsupervised Segmentation for Terracotta Warrior with Seed-Region-Growing CNN(SRG-Net)

no code implementations28 Jul 2021 Yao Hu, Guohua Geng, Kang Li, Wei Zhou, Xingxing Hao, Xin Cao

Then we present a supervised segmentation and unsupervised reconstruction networks to learn the characteristics of 3D point clouds.

Coverage-based Scene Fuzzing for Virtual Autonomous Driving Testing

no code implementations2 Jun 2021 Zhisheng Hu, Shengjian Guo, Zhenyu Zhong, Kang Li

Simulation-based virtual testing has become an essential step to ensure the safety of autonomous driving systems.

Autonomous Driving

Self-paced Resistance Learning against Overfitting on Noisy Labels

1 code implementation7 May 2021 Xiaoshuang Shi, Zhenhua Guo, Kang Li, Yun Liang, Xiaofeng Zhu

They might significantly deteriorate the performance of convolutional neural networks (CNNs), because CNNs are easily overfitted on corrupted labels.


SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching

1 code implementation12 Apr 2021 Xiangde Luo, Tao Song, Guotai Wang, Jieneng Chen, Yinan Chen, Kang Li, Dimitris N. Metaxas, Shaoting Zhang

To overcome these problems, we propose a 3D sphere representation-based center-points matching detection network that is anchor-free and automatically predicts the position, radius, and offset of nodules without the manual design of nodule/anchor parameters.

Lung Nodule Detection

Detecting Localized Adversarial Examples: A Generic Approach using Critical Region Analysis

no code implementations10 Feb 2021 Fengting Li, Xuankai Liu, Xiaoli Zhang, Qi Li, Kun Sun, Kang Li

Particularly, the localized adversarial examples only perturb a small and contiguous region of the target object, so that they are robust and effective in both digital and physical worlds.

Face Recognition Image Classification

On Explainability of Graph Neural Networks via Subgraph Explorations

1 code implementation9 Feb 2021 Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, Shuiwang Ji

To make the tree search more effective, we propose to use Shapley values as a measure of subgraph importance, which can also capture the interactions among different subgraphs.

Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentation

1 code implementation7 Jan 2021 Kang Li, Shujun Wang, Lequan Yu, Pheng-Ann Heng

In this way, the dual teacher models would transfer acquired inter- and intra-domain knowledge to the student model for further integration and exploitation.

Cardiac Segmentation Domain Adaptation +1

Unsupervised Segmentation for Terracotta Warrior Point Cloud (SRG-Net)

1 code implementation1 Dec 2020 Yao Hu, Guohua Geng, Kang Li, Wei Zhou

Then we present a supervised segmentation and unsupervised reconstruction networks to learn the characteristics of 3D point clouds.


DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets

no code implementations13 Oct 2020 Shujun Wang, Lequan Yu, Kang Li, Xin Yang, Chi-Wing Fu, Pheng-Ann Heng

Our DoFE framework dynamically enriches the image features with additional domain prior knowledge learned from multi-source domains to make the semantic features more discriminative.

Domain Generalization Image Segmentation +1

CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation

no code implementations EMNLP 2020 Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, Ed Chi

Experiments on real-world NLP datasets demonstrate that our method can generate more diverse and fluent adversarial texts, compared to many existing adversarial text generation approaches.

Adversarial Text Sentiment Analysis +2

Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation

no code implementations4 Oct 2020 Kang Li, Lequan Yu, Shujun Wang, Pheng-Ann Heng

Considering multi-modality data with the same anatomic structures are widely available in clinic routine, in this paper, we aim to exploit the prior knowledge (e. g., shape priors) learned from one modality (aka., assistant modality) to improve the segmentation performance on another modality (aka., target modality) to make up annotation scarcity.

Cardiac Segmentation Image Segmentation +2

Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images

no code implementations10 Jul 2020 Hui Qu, Pengxiang Wu, Qiaoying Huang, Jingru Yi, Zhennan Yan, Kang Li, Gregory M. Riedlinger, Subhajyoti De, Shaoting Zhang, Dimitris N. Metaxas

To alleviate such tedious and manual effort, in this paper we propose a novel weakly supervised segmentation framework based on partial points annotation, i. e., only a small portion of nuclei locations in each image are labeled.

Weakly supervised segmentation

Dynamic asymptotic dimension for actions of virtually cyclic groups

no code implementations2 Jul 2020 Massoud Amini, Kang Li, Damian Sawicki, Ali Shakibazadeh

We show that the dynamic asymptotic dimension of a minimal free action of an infinite virtually cyclic group on a compact Hausdorff space is always one.

Dynamical Systems Group Theory Operator Algebras Primary: 37C45, Secondary: 37B05, 20F69

Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation

no code implementations13 Aug 2019 Chaowei Tan, Zhennan Yan, Shaoting Zhang, Kang Li, Dimitris N. Metaxas

However, effective and efficient delineation of all the knee articular cartilages in large-sized and high-resolution 3D MR knee data is still an open challenge.

Decision Making

Seeing is Not Believing: Camouflage Attacks on Image Scaling Algorithms

no code implementations USENIX Security Symposium 2019 Qixue Xiao, Yufei Chen, Chao Shen, Yu Chen, Kang Li

We also present an algorithm that can successfully enable attacks against famous cloud-based image services (such as those from Microsoft Azure, Aliyun, Baidu, and Tencent) and cause obvious misclassification effects, even when the details of image processing (such as the exact scaling algorithm and scale dimension parameters) are hidden in the cloud.

Data Poisoning Image Classification

Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation

1 code implementation26 Jun 2019 Shujun Wang, Lequan Yu, Kang Li, Xin Yang, Chi-Wing Fu, Pheng-Ann Heng

The cross-domain discrepancy (domain shift) hinders the generalization of deep neural networks to work on different domain datasets. In this work, we present an unsupervised domain adaptation framework, called Boundary and Entropy-driven Adversarial Learning (BEAL), to improve the OD and OC segmentation performance, especially on the ambiguous boundary regions.

Image Segmentation Semantic Segmentation +1

Automatic Health Problem Detection from Gait Videos Using Deep Neural Networks

1 code implementation4 Jun 2019 Rahil Mehrizi, Xi Peng, Shaoting Zhang, Ruisong Liao, Kang Li

This study presents a starting point toward a powerful tool for automatic classification of gait disorders and can be used as a basis for future applications of Deep Learning in clinical gait analysis.

Feature Engineering General Classification +2

Evidence for $Z_{c}^{\pm}$ decays into the $ρ^{\pm} η_{c}$ final state

no code implementations3 Jun 2019 M. Ablikim, M. N. Achasov, S. Ahmed, M. Albrecht, M. Alekseev, A. Amoroso, F. F. An, Q. An, Y. Bai, O. Bakina, R. Baldini Ferroli, Y. Ban, K. Begzsuren, D. W. Bennett, J. V. Bennett, N. Berger, M. Bertani, D. Bettoni, F. Bianchi, E. Boger, I. Boyko, R. A. Briere, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, S. A. Cetin, J. Chai, J. F. Chang, W. L. Chang, G. Chelkov, G. Chen, H. S. Chen, J. C. Chen, M. L. Chen, P. L. Chen, S. J. Chen, X. R. Chen, Y. B. Chen, W. Cheng, X. K. Chu, G. Cibinetto, F. Cossio, H. L. Dai, J. P. Dai, A. Dbeyssi, D. Dedovich, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. DeMori, Y. Ding, C. Dong, J. Dong, L. Y. Dong, M. Y. Dong, Z. L. Dou, S. X. Du, P. F. Duan, J. Fang, S. S. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, M. Fritsch, C. D. Fu, Q. Gao, X. L. Gao, Y. Gao, Y. G. Gao, Z. Gao, B. Garillon, I. Garzia, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, M. Greco, L. M. Gu, M. H. Gu, Y. T. Gu, A. Q. Guo, L. B. Guo, R. P. Guo, Y. P. Guo, A. Guskov, Z. Haddadi, S. Han, X. Q. Hao, F. A. Harris, K. L. He, F. H. Heinsius, T. Held, Y. K. Heng, Z. L. Hou, H. M. Hu, J. F. Hu, T. Hu, Y. Hu, G. S. Huang, J. S. Huang, X. T. Huang, X. Z. Huang, Z. L. Huang, T. Hussain, W. Ikegami Andersson, M. Irshad, Q. Ji, Q. P. Ji, X. B. Ji, X. L. Ji, H. L. Jiang, X. S. Jiang, X. Y. Jiang, J. B. Jiao, Z. Jiao, D. P. Jin, S. Jin, Y. Jin, T. Johansson, A. Julin, N. Kalantar-Nayestanaki, X. S. Kang, M. Kavatsyuk, B. C. Ke, I. K. Keshk, T. Khan, A. Khoukaz, P. Kiese, R. Kiuchi, R. Kliemt, L. Koch, O. B. Kolcu, B. Kopf, M. Kuemmel, M. Kuessner, A. Kupsc, M. Kurth, W. Kühn, J. S. Lange, P. Larin, L. Lavezzi, S. Leiber, H. Leithoff, C. Li, Cheng Li, D. M. Li, F. Li, F. Y. Li, G. Li, H. B. Li, H. J. Li, J. C. Li, J. W. Li, K. J. Li, Kang Li, Ke Li, Lei LI, P. L. Li, P. R. Li, Q. Y. Li, T. Li, W. D. Li, W. G. Li, X. L. Li, X. N. Li, X. Q. Li, Z. B. Li, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. Z. Liao, J. Libby, C. X. Lin, D. X. Lin, B. Liu, B. J. Liu, C. X. Liu, D. Liu, D. Y. Liu, F. H. Liu, Fang Liu, Feng Liu, H. B. Liu, H. L. Liu, H. M. Liu, Huanhuan Liu, Huihui Liu, J. B. Liu, J. Y. Liu, K. Y. Liu, Ke Liu, L. D. Liu, Q. Liu, S. B. Liu, X. Liu, Y. B. Liu, Z. A. Liu, Zhiqing Liu, Y. F. Long, X. C. Lou, H. J. Lu, J. G. Lu, Y. Lu, Y. P. Lu, C. L. Luo, M. X. Luo, P. W. Luo, T. Luo, X. L. Luo, S. Lusso, X. R. Lyu, F. C. Ma, H. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, X. N. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, M. Maggiora, S. Maldaner, Q. A. Malik, A. Mangoni, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, J. Min, T. J. Min, R. E. Mitchell, X. H. Mo, Y. J. Mo, C. Morales Morales, N. Yu. Muchnoi, H. Muramatsu, A. Mustafa, S. Nakhoul, Y. Nefedov, F. Nerling, I. B. Nikolaev, Z. Ning, S. Nisar, S. L. Niu, X. Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, Y. Pan, M. Papenbrock, P. Patteri, M. Pelizaeus, J. Pellegrino, H. P. Peng, Z. Y. Peng, K. Peters, J. Pettersson, J. L. Ping, R. G. Ping, A. Pitka, R. Poling, V. Prasad, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, C. F. Qiao, N. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, S. Q. Qu, K. H. Rashid, C. F. Redmer, M. Richter, M. Ripka, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, A. Sarantsev, M. Savrié, K. Schoenning, W. Shan, X. Y. Shan, M. Shao, C. P. Shen, P. X. Shen, X. Y. Shen, H. Y. Sheng, X. Shi, J. J. Song, W. M. Song, X. Y. Song, S. Sosio, C. Sowa, S. Spataro, F. F. Sui, G. X. Sun, J. F. Sun, L. Sun, S. S. Sun, X. H. Sun, Y. J. Sun, Y. K Sun, Y. Z. Sun, Z. J. Sun, Z. T. Sun, Y. T Tan, C. J. Tang, G. Y. Tang, X. Tang, M. Tiemens, B. Tsednee, I. Uman, B. Wang, B. L. Wang, C. W. Wang, D. Wang, D. Y. Wang, Dan Wang, H. H. Wang, K. Wang, L. L. Wang, L. S. Wang, M. Wang, Meng Wang, P. Wang, P. L. Wang, W. P. Wang, X. F. Wang, Y. Wang, Y. F. Wang, Z. Wang, Z. G. Wang, Z. Y. Wang, Zongyuan Wang, T. Weber, D. H. Wei, P. Weidenkaff, S. P. Wen, U. Wiedner, M. Wolke, L. H. Wu, L. J. Wu, Z. Wu, L. Xia, X. Xia, Y. Xia, D. Xiao, Y. J. Xiao, Z. J. Xiao, Y. G. Xie, Y. H. Xie, X. A. Xiong, Q. L. Xiu, G. F. Xu, J. J. Xu, L. Xu, Q. J. Xu, X. P. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, Y. H. Yan, H. J. Yang, H. X. Yang, L. Yang, R. X. Yang, S. L. Yang, Y. H. Yang, Y. X. Yang, Yifan Yang, Z. Q. Yang, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, J. S. Yu, C. Z. Yuan, Y. Yuan, A. Yuncu, A. A. Zafar, Y. Zeng, B. X. Zhang, B. Y. Zhang, C. C. Zhang, D. H. Zhang, H. H. Zhang, H. Y. Zhang, J. Zhang, J. L. Zhang, J. Q. Zhang, J. W. Zhang, J. Y. Zhang, J. Z. Zhang, K. Zhang, L. Zhang, S. F. Zhang, T. J. Zhang, X. Y. Zhang, Y. Zhang, Y. H. Zhang, Y. T. Zhang, Yang Zhang, YaoZ hang, Yu Zhang, Z. H. Zhang, Z. P. Zhang, Z. Y. Zhang, G. Zhao, J. W. Zhao, J. Y. Zhao, J. Z. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, Q. Zhao, S. J. Zhao, T. C. Zhao, Y. B. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, L. Zhou, Q. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Xiaoyu Zhou, Xu Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, S. Zhu, S. H. Zhu, X. L. Zhu, Y. C. Zhu, Y. S. Zhu, Z. A. Zhu, J. Zhuang, B. S. Zou, J. H. Zou

We study $e^{+}e^{-}$ collisions with a $\pi^{+}\pi^{-}\pi^{0}\eta_{c}$ final state using data samples collected with the BESIII detector at center-of-mass energies $\sqrt{s}=4. 226$, $4. 258$, $4. 358$, $4. 416$, and $4. 600$ GeV.

High Energy Physics - Experiment

Effective 3D Humerus and Scapula Extraction using Low-contrast and High-shape-variability MR Data

no code implementations22 Feb 2019 Xiaoxiao He, Chaowei Tan, Yuting Qiao, Virak Tan, Dimitris Metaxas, Kang Li

For the initial shoulder preoperative diagnosis, it is essential to obtain a three-dimensional (3D) bone mask from medical images, e. g., magnetic resonance (MR).

Toward Marker-free 3D Pose Estimation in Lifting: A Deep Multi-view Solution

no code implementations6 Feb 2018 Rahil Mehrizi, Xi Peng, Zhiqiang Tang, Xu Xu, Dimitris Metaxas, Kang Li

The results are also compared with state-of-the-art methods on HumanEva-I dataset, which demonstrates the superior performance of our approach.

3D Pose Estimation

Wolf in Sheep's Clothing - The Downscaling Attack Against Deep Learning Applications

no code implementations21 Dec 2017 Qixue Xiao, Kang Li, Deyue Zhang, Yier Jin

This paper presents a downscaling attack that targets the data scaling process in deep learning applications.

Data Poisoning Image Classification

Recognition of convolutional neural network based on CUDA Technology

no code implementations30 May 2015 Yi-bin Huang, Kang Li, Ge Wang, Min Cao, Pin Li, Yu-jia Zhang

For the problem whether Graphic Processing Unit(GPU), the stream processor with high performance of floating-point computing is applicable to neural networks, this paper proposes the parallel recognition algorithm of Convolutional Neural Networks(CNNs). It adopts Compute Unified Device Architecture(CUDA)technology, definite the parallel data structures, and describes the mapping mechanism for computing tasks on CUDA.

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