Search Results for author: Li Sun

Found 76 papers, 30 papers with code

OD-DETR: Online Distillation for Stabilizing Training of Detection Transformer

no code implementations9 Jun 2024 Shengjian Wu, Li Sun, Qingli Li

First, the matching relation between object queries and ground truth (GT) boxes in the teacher is employed to guide the student, so queries within the student are not only assigned labels based on their own predictions, but also refer to the matching results from the teacher.

R-ODE: Ricci Curvature Tells When You Will be Informed

no code implementations27 May 2024 Li Sun, Jingbin Hu, Mengjie Li, Hao Peng

Such limitation motivates us to pose the problem of the time-aware personalized information diffusion prediction for the first time, telling the time when the target user will be informed.

Blocking Graph Neural Network

LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering

no code implementations20 May 2024 Li Sun, Zhenhao Huang, Hao Peng, Yujie Wang, Chunyang Liu, Philip S. Yu

DSI is also theoretically presented as a new graph clustering objective, not requiring the predefined cluster number.

Clustering Deep Clustering +1

Relational Prompt-based Pre-trained Language Models for Social Event Detection

no code implementations12 Apr 2024 Pu Li, Xiaoyan Yu, Hao Peng, Yantuan Xian, Linqin Wang, Li Sun, Jingyun Zhang, Philip S. Yu

In this paper, we approach social event detection from a new perspective based on Pre-trained Language Models (PLMs), and present RPLM_SED (Relational prompt-based Pre-trained Language Models for Social Event Detection).

Event Detection Graph Neural Network

RCoCo: Contrastive Collective Link Prediction across Multiplex Network in Riemannian Space

no code implementations4 Mar 2024 Li Sun, Mengjie Li, Yong Yang, Xiao Li, Lin Liu, Pengfei Zhang, Haohua Du

Annotating anchor users is laborious and expensive, and thus it is impractical to work with quantities of anchor users.

Graph Attention Link Prediction

Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarios

no code implementations4 Feb 2024 Yuxin Wang, Zunlei Feng, Haofei Zhang, Yang Gao, Jie Lei, Li Sun, Mingli Song

Due to the inability to receive signals from the Global Navigation Satellite System (GNSS) in extreme conditions, achieving accurate and robust navigation for Unmanned Aerial Vehicles (UAVs) is a challenging task.

DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing

no code implementations23 Jan 2024 Li Sun, Zhenhao Huang, Hua Wu, Junda Ye, Hao Peng, Zhengtao Yu, Philip S. Yu

Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph.

Contrastive Learning Graph structure learning

Temporal Insight Enhancement: Mitigating Temporal Hallucination in Multimodal Large Language Models

no code implementations18 Jan 2024 Li Sun, Liuan Wang, Jun Sun, Takayuki Okatani

This study introduces an innovative method to address event-level hallucinations in MLLMs, focusing on specific temporal understanding in video content.


Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning

1 code implementation2 Jan 2024 Li Sun, Zhenhao Huang, Zixi Wang, Feiyang Wang, Hao Peng, Philip Yu

In light of the issues above, we propose the problem of \emph{Motif-aware Riemannian Graph Representation Learning}, seeking a numerically stable encoder to capture motif regularity in a diverse-curvature manifold without labels.

Contrastive Learning Graph Neural Network +1

Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces

no code implementations2 Jan 2024 Li Sun, Junda Ye, Jiawei Zhang, Yong Yang, Mingsheng Liu, Feiyang Wang, Philip S. Yu

To address the aforementioned issues, we propose a novel Contrastive model for Sequential Interaction Network learning on Co-Evolving RiEmannian spaces, CSINCERE.

Contrastive Learning Recommendation Systems

MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images

no code implementations5 Oct 2023 Yanwu Xu, Li Sun, Wei Peng, Shyam Visweswaran, Kayhan Batmanghelich

This study focuses on two main objectives: (1) the development of a method for creating images based on textual prompts and anatomical components, and (2) the capability to generate new images conditioning on anatomical elements.

Anatomy Image Generation +1

Global-correlated 3D-decoupling Transformer for Clothed Avatar Reconstruction

1 code implementation NeurIPS 2023 Zechuan Zhang, Li Sun, Zongxin Yang, Ling Chen, Yi Yang

Reconstructing 3D clothed human avatars from single images is a challenging task, especially when encountering complex poses and loose clothing.


SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds

no code implementations6 May 2023 Junda Ye, Zhongbao Zhang, Li Sun, Yang Yan, Feiyang Wang, Fuxin Ren

To explore these issues for sequential interaction networks, we propose SINCERE, a novel method representing Sequential Interaction Networks on Co-Evolving RiEmannian manifolds.

Recommendation Systems Representation Learning

Contrastive Graph Clustering in Curvature Spaces

no code implementations5 May 2023 Li Sun, Feiyang Wang, Junda Ye, Hao Peng, Philip S. Yu

On the other hand, contrastive learning boosts the deep graph clustering but usually struggles in either graph augmentation or hard sample mining.

Clustering Contrastive Learning +1

DrasCLR: A Self-supervised Framework of Learning Disease-related and Anatomy-specific Representation for 3D Medical Images

no code implementations21 Feb 2023 Ke Yu, Li Sun, Junxiang Chen, Max Reynolds, Tigmanshu Chaudhary, Kayhan Batmanghelich

Extensive experiments on large-scale computer tomography (CT) datasets of lung images show that our method improves the performance of many downstream prediction and segmentation tasks.

Anatomy Contrastive Learning +2

MVKT-ECG: Efficient Single-lead ECG Classification on Multi-Label Arrhythmia by Multi-View Knowledge Transferring

no code implementations28 Jan 2023 Yuzhen Qin, Li Sun, Hui Chen, Wei-Qiang Zhang, Wenming Yang, Jintao Fei, Guijin Wang

However, it is challenging to develop a single-lead-based ECG interpretation model for multiple diseases diagnosis due to the lack of some key disease information.

ECG Classification Knowledge Distillation

Measuring tail risk at high-frequency: An $L_1$-regularized extreme value regression approach with unit-root predictors

no code implementations3 Jan 2023 Julien Hambuckers, Li Sun, Luca Trapin

Studying the high-frequency extreme losses of nine large liquid U. S. stocks using 42 liquidity and volatility predictors, we find the severity of extreme losses to be well predicted by low levels of price impact in period of high volatility of liquidity and volatility.

How To Prevent the Continuous Damage of Noises To Model Training?

no code implementations CVPR 2023 Xiaotian Yu, Yang Jiang, Tianqi Shi, Zunlei Feng, Yuexuan Wang, Mingli Song, Li Sun

To address this problem, the proposed GSS alleviates the damage by switching the current gradient direction of each sample to a new direction selected from a gradient direction pool, which contains all-class gradient directions with different probabilities.

Learning with noisy labels

DGFont++: Robust Deformable Generative Networks for Unsupervised Font Generation

1 code implementation30 Dec 2022 Xinyuan Chen, Yangchen Xie, Li Sun, Yue Lu

Moreover, we introduce contrastive self-supervised learning to learn a robust style representation for fonts by understanding the similarity and dissimilarities of fonts.

Font Generation Self-Supervised Learning +1

Towards Long-term Autonomy: A Perspective from Robot Learning

no code implementations24 Dec 2022 Zhi Yan, Li Sun, Tomas Krajnik, Tom Duckett, Nicola Bellotto

In the future, service robots are expected to be able to operate autonomously for long periods of time without human intervention.

Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces

no code implementations30 Nov 2022 Li Sun, Junda Ye, Hao Peng, Feiyang Wang, Philip S. Yu

On the one hand, existing methods work with the zero-curvature Euclidean space, and largely ignore the fact that curvature varies over the coming graph sequence.

Graph Learning

CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels

1 code implementation25 Nov 2022 Siyuan Li, Li Sun, Qingli Li

The key idea is to fully exploit the cross-modal description ability in CLIP through a set of learnable text tokens for each ID and give them to the text encoder to form ambiguous descriptions.

Image Classification Language Modelling +2

A Dual-scale Lead-seperated Transformer With Lead-orthogonal Attention And Meta-information For Ecg Classification

no code implementations23 Nov 2022 Yang Li, Guijin Wang, Zhourui Xia, Wenming Yang, Li Sun

Auxiliary diagnosis of cardiac electrophysiological status can be obtained through the analysis of 12-lead electrocardiograms (ECGs).

ECG Classification

IoU-Enhanced Attention for End-to-End Task Specific Object Detection

2 code implementations21 Sep 2022 Jing Zhao, Shengjian Wu, Li Sun, Qingli Li

Without densely tiled anchor boxes or grid points in the image, sparse R-CNN achieves promising results through a set of object queries and proposal boxes updated in the cascaded training manner.

Object object-detection +1

A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning

no code implementations30 Aug 2022 Li Sun, Junda Ye, Hao Peng, Philip S. Yu

To bridge this gap, we make the first attempt to study the problem of self-supervised temporal graph representation learning in the general Riemannian space, supporting the time-varying curvature to shift among hyperspherical, Euclidean and hyperbolic spaces.

Graph Learning Graph Neural Network +2

Cross Attention Based Style Distribution for Controllable Person Image Synthesis

1 code implementation1 Aug 2022 Xinyue Zhou, Mingyu Yin, Xinyuan Chen, Li Sun, Changxin Gao, Qingli Li

In this paper, we propose a cross attention based style distribution module that computes between the source semantic styles and target pose for pose transfer.

Pose Transfer Virtual Try-on

Federated Selective Aggregation for Knowledge Amalgamation

1 code implementation27 Jul 2022 Donglin Xie, Ruonan Yu, Gongfan Fang, Jie Song, Zunlei Feng, Xinchao Wang, Li Sun, Mingli Song

The goal of FedSA is to train a student model for a new task with the help of several decentralized teachers, whose pre-training tasks and data are different and agnostic.

QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation

2 code implementations CVPR 2022 Xueqi Hu, Xinyue Zhou, Qiusheng Huang, Zhengyi Shi, Li Sun, Qingli Li

By constraining features from the same location to be closer than those from different ones, it implicitly ensures the result to take content from the source.

Contrastive Learning Translation

Style Transformer for Image Inversion and Editing

1 code implementation CVPR 2022 Xueqi Hu, Qiusheng Huang, Zhengyi Shi, Siyuan Li, Changxin Gao, Li Sun, Qingli Li

Existing GAN inversion methods fail to provide latent codes for reliable reconstruction and flexible editing simultaneously.

Attribute Image-to-Image Translation

A Self-supervised Mixed-curvature Graph Neural Network

no code implementations10 Dec 2021 Li Sun, Zhongbao Zhang, Junda Ye, Hao Peng, Jiawei Zhang, Sen Su, Philip S. Yu

Instead of working on one single constant-curvature space, we construct a mixed-curvature space via the Cartesian product of multiple Riemannian component spaces and design hierarchical attention mechanisms for learning and fusing the representations across these component spaces.

Contrastive Learning Graph Neural Network +1

Bootstrapping ViTs: Towards Liberating Vision Transformers from Pre-training

1 code implementation CVPR 2022 Haofei Zhang, Jiarui Duan, Mengqi Xue, Jie Song, Li Sun, Mingli Song

Recently, vision Transformers (ViTs) are developing rapidly and starting to challenge the domination of convolutional neural networks (CNNs) in the realm of computer vision (CV).

Attribute-specific Control Units in StyleGAN for Fine-grained Image Manipulation

1 code implementation25 Nov 2021 Rui Wang, Jian Chen, Gang Yu, Li Sun, Changqian Yu, Changxin Gao, Nong Sang

Image manipulation with StyleGAN has been an increasing concern in recent years. Recent works have achieved tremendous success in analyzing several semantic latent spaces to edit the attributes of the generated images. However, due to the limited semantic and spatial manipulation precision in these latent spaces, the existing endeavors are defeated in fine-grained StyleGAN image manipulation, i. e., local attribute translation. To address this issue, we discover attribute-specific control units, which consist of multiple channels of feature maps and modulation styles.

Attribute Image Manipulation

Artificial Neural Network and Its Application Research Progress in Chemical Process

no code implementations18 Oct 2021 Li Sun, Fei Liang, Wutai Cui

Most chemical processes, such as distillation, absorption, extraction, and catalytic reactions, are extremely complex processes that are affected by multiple factors.

Chemical Process

Can contrastive learning avoid shortcut solutions?

1 code implementation NeurIPS 2021 Joshua Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, Suvrit Sra

However, we observe that the contrastive loss does not always sufficiently guide which features are extracted, a behavior that can negatively impact the performance on downstream tasks via "shortcuts", i. e., by inadvertently suppressing important predictive features.

Contrastive Learning

Tree-Like Decision Distillation

no code implementations CVPR 2021 Jie Song, Haofei Zhang, Xinchao Wang, Mengqi Xue, Ying Chen, Li Sun, DaCheng Tao, Mingli Song

Knowledge distillation pursues a diminutive yet well-behaved student network by harnessing the knowledge learned by a cumbersome teacher model.

Decision Making Knowledge Distillation

DG-Font: Deformable Generative Networks for Unsupervised Font Generation

1 code implementation CVPR 2021 Yangchen Xie, Xinyuan Chen, Li Sun, Yue Lu

Font generation is a challenging problem especially for some writing systems that consist of a large number of characters and has attracted a lot of attention in recent years.

Font Generation Image-to-Image Translation

Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs

no code implementations6 Apr 2021 Li Sun, Zhongbao Zhang, Jiawei Zhang, Feiyang Wang, Hao Peng, Sen Su, Philip S. Yu

To model the uncertainty, we devise a hyperbolic graph variational autoencoder built upon the proposed TGNN to generate stochastic node representations of hyperbolic normal distributions.

Graph Neural Network

Introspective Visuomotor Control: Exploiting Uncertainty in Deep Visuomotor Control for Failure Recovery

no code implementations22 Mar 2021 Chia-Man Hung, Li Sun, Yizhe Wu, Ioannis Havoutis, Ingmar Posner

To recover from high uncertainty cases, the robot monitors its uncertainty along a trajectory and explores possible actions in the state-action space to bring itself to a more certain state.

Imitation Learning Robot Manipulation

ID-Unet: Iterative Soft and Hard Deformation for View Synthesis

2 code implementations CVPR 2021 Mingyu Yin, Li Sun, Qingli Li

View synthesis is usually done by an autoencoder, in which the encoder maps a source view image into a latent content code, and the decoder transforms it into a target view image according to the condition.


Adaptive Random Bandwidth for Inference in CAViaR Models

no code implementations2 Feb 2021 Alain Hecq, Li Sun

This paper investigates the size performance of Wald tests for CAViaR models (Engle and Manganelli, 2004).

Density Estimation

Context Matters: Graph-based Self-supervised Representation Learning for Medical Images

1 code implementation11 Dec 2020 Li Sun, Ke Yu, Kayhan Batmanghelich

Experiments on large-scale Computer Tomography (CT) datasets of lung images show that our approach compares favorably to baseline methods that do not account for the context.

Anatomy Representation Learning +1

Content-based Analysis of the Cultural Differences between TikTok and Douyin

no code implementations3 Nov 2020 Li Sun, Haoqi Zhang, Songyang Zhang, Jiebo Luo

Short-form video social media shifts away from the traditional media paradigm by telling the audience a dynamic story to attract their attention.

Object object-detection +1

Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN

1 code implementation5 Aug 2020 Li Sun, Junxiang Chen, Yanwu Xu, Mingming Gong, Ke Yu, Kayhan Batmanghelich

During training, we adopt a hierarchical structure that simultaneously generates a low-resolution version of the image and a randomly selected sub-volume of the high-resolution image.

Data Augmentation Domain Adaptation +4

Progressive Multi-stage Feature Mix for Person Re-Identification

1 code implementation17 Jul 2020 Yan Zhang, Binyu He, Li Sun

In this work, we propose a Progressive Multi-stage feature Mix network (PMM), which enables the model to find out the more precise and diverse features in a progressive manner.

Person Re-Identification

Learning Posterior and Prior for Uncertainty Modeling in Person Re-Identification

no code implementations17 Jul 2020 Yan Zhang, Zhilin Zheng, Binyu He, Li Sun

This paper proposes to learn the sample posterior and the class prior distribution in the latent space, so that not only representative features but also the uncertainty can be built by the model.

Person Re-Identification

Localising Faster: Efficient and precise lidar-based robot localisation in large-scale environments

no code implementations4 Mar 2020 Li Sun, Daniel Adolfsson, Martin Magnusson, Henrik Andreasson, Ingmar Posner, Tom Duckett

More importantly, the Gaussian method (i. e. deep probabilistic localisation) and non-Gaussian method (i. e. MCL) can be integrated naturally via importance sampling.

Improving End-to-End Object Tracking Using Relational Reasoning

no code implementations ICLR 2020 Fabian B. Fuchs, Adam R. Kosiorek, Li Sun, Oiwi Parker Jones, Ingmar Posner

Relational reasoning, the ability to model interactions and relations between objects, is valuable for robust multi-object tracking and pivotal for trajectory prediction.

Multi-Object Tracking Object +2

Disentangling the Spatial Structure and Style in Conditional VAE

no code implementations29 Oct 2019 Ziye Zhang, Li Sun, Zhilin Zheng, Qingli Li

Depending on whether the label is related with the spatial structure, the output $z_s$ from the condition mapping network is used either as a style code or a spatial structure code.


Imagine That! Leveraging Emergent Affordances for 3D Tool Synthesis

no code implementations30 Sep 2019 Yizhe Wu, Sudhanshu Kasewa, Oliver Groth, Sasha Salter, Li Sun, Oiwi Parker Jones, Ingmar Posner

In this paper we explore the richness of information captured by the latent space of a vision-based generative model.

Imagine That! Leveraging Emergent Affordances for Tool Synthesis in Reaching Tasks

no code implementations25 Sep 2019 Yizhe Wu, Sudhanshu Kasewa, Oliver Groth, Sasha Salter, Li Sun, Oiwi Parker Jones, Ingmar Posner

In this paper we investigate an artificial agent's ability to perform task-focused tool synthesis via imagination.


Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation

2 code implementations ICCV 2019 Chengchao Shen, Mengqi Xue, Xinchao Wang, Jie Song, Li Sun, Mingli Song

To this end, we introduce a dual-step strategy that first extracts the task-specific knowledge from the heterogeneous teachers sharing the same sub-task, and then amalgamates the extracted knowledge to build the student network.

Disentangling Latent Space for VAE by Label Relevant/Irrelevant Dimensions

1 code implementation CVPR 2019 Zhilin Zheng, Li Sun

But different from CVAE, we present a method for disentangling the latent space into the label relevant and irrelevant dimensions, $\bm{\mathrm{z}}_s$ and $\bm{\mathrm{z}}_u$, for a single input.

Variational Inference

Amalgamating Knowledge towards Comprehensive Classification

1 code implementation7 Nov 2018 Chengchao Shen, Xinchao Wang, Jie Song, Li Sun, Mingli Song

We propose in this paper to study a new model-reusing task, which we term as \emph{knowledge amalgamation}.

Classification General Classification

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Exploring Correlations in Multiple Facial Attributes through Graph Attention Network

1 code implementation22 Oct 2018 Yan Zhang, Li Sun

Estimating multiple attributes from a single facial image gives comprehensive descriptions on the high level semantics of the face.

Attribute Graph Attention +1

Recurrent-OctoMap: Learning State-based Map Refinement for Long-Term Semantic Mapping with 3D-Lidar Data

no code implementations2 Jul 2018 Li Sun, Zhi Yan, Anestis Zaganidis, Cheng Zhao, Tom Duckett

Most existing semantic mapping approaches focus on improving semantic understanding of single frames, rather than 3D refinement of semantic maps (i. e. fusing semantic observations).

Learning monocular visual odometry with dense 3D mapping from dense 3D flow

no code implementations6 Mar 2018 Cheng Zhao, Li Sun, Pulak Purkait, Tom Duckett, Rustam Stolkin

Dense 2D flow and a depth image are generated from monocular images by sub-networks, which are then used by a 3D flow associated layer in the L-VO network to generate dense 3D flow.

Monocular Visual Odometry

Dense RGB-D semantic mapping with Pixel-Voxel neural network

no code implementations30 Sep 2017 Cheng Zhao, Li Sun, Pulak Purkait, Rustam Stolkin

For intelligent robotics applications, extending 3D mapping to 3D semantic mapping enables robots to, not only localize themselves with respect to the scene's geometrical features but also simultaneously understand the higher level meaning of the scene contexts.

3D Reconstruction Scene Understanding +1

3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data

no code implementations30 Sep 2017 Li Sun, Zhi Yan, Sergi Molina Mellado, Marc Hanheide, Tom Duckett

Our approach, T-Pose-LSTM (Temporal 3DOF-Pose Long-Short-Term Memory), is trained using long-term data from real-world robot deployments and aims to learn context-dependent (environment- and time-specific) human activities.

Decoder Human Detection +2

Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting

no code implementations22 Jul 2017 Li Sun, Gerardo Aragon-Camarasa, Simon Rogers, Rustam Stolkin, J. Paul Siebert

Our visual feature is robust to deformable shapes and our approach is able to recognise the category of unknown clothing in unconstrained and random configurations.

Weakly-supervised DCNN for RGB-D Object Recognition in Real-World Applications Which Lack Large-scale Annotated Training Data

1 code implementation19 Mar 2017 Li Sun, Cheng Zhao, Rustam Stolkin

We also propose a novel way to pretrain a DCNN for the depth modality, by training on virtual depth images projected from CAD models.

Object Recognition Weakly-supervised Learning

A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition

no code implementations14 Mar 2017 Cheng Zhao, Li Sun, Rustam Stolkin

We present the results of experiments, in which we trained our system to perform real-time 3D semantic reconstruction for 23 different materials in a real-world application.

3D Reconstruction Material Recognition

Robot Vision Architecture for Autonomous Clothes Manipulation

no code implementations18 Oct 2016 Li Sun, Gerardo Aragon-Camarasa, Simon Rogers, J. Paul Siebert

The experimental results show that the proposed dual-arm flattening using stereo vision system remarkably outperforms the single-arm flattening and widely-cited Kinect-based sensing system for dexterous manipulation tasks.

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