Search Results for author: Kang Li

Found 23 papers, 7 papers with code

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

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

1 code implementation28 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

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

SRG-Net: Unsupervised Segmentation for Terracotta Warrior Point Cloud with 3D Pointwise CNN methods

1 code implementation1 Dec 2020 Yao Hu, Guohua Geng, Kang Li, Wei Zhou, Xingxing Hao, Xin Cao

Finally, we combine the SRG algorithm with our improved CNN using a refinement method called SRG-Net to conduct the segmentation tasks on the terracotta warriors.

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 Semantic Segmentation

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 +1

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 Knowledge Distillation

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

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.

Semantic Segmentation Unsupervised Domain Adaptation

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

Temporal Subspace Clustering for Human Motion Segmentation

no code implementations ICCV 2015 Sheng Li, Kang Li, Yun Fu

Subspace clustering is an effective technique for segmenting data drawn from multiple subspaces.

Motion Segmentation Time Series

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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