Dr. Fuxin Li

Address:	2077 Kelley Engineering Center Oregon State University Corvallis 97331, Oregon USA
Phone:	(+1) 541-737-5987 (office)
E-Mail:	lif (a) eecs.oregonstate.edu
Google Scholar Profile	CV

Bio

I am an associate professor in the School of Electrical Engineering and Computer Science at the Oregon State University. My research direction is machine learning and computer vision, with a major interest in using and designing new machine learning algorithms to attack the structural data in images and videos, especially big data originating from videos.

Before joining OSU, I spent 4.5 years in Georgia Tech, first as a postdoc researcher supervised by Dr. Guy Lebanon, then as a research scientist working with Dr. James M. Rehg. I have worked on problems in natural language processing, recommender systems, as well as many computer vision problems such as image and video segmentation, object recognition, multi-target tracking, semantic reconstruction, etc.

Before Georgia Tech, I was working as a research scientist in the Sminchisescu group, INS, University of Bonn from 2008 to 2010. In Bonn I worked on machine learning and computer vision, looking to develop and use learning methods properly to solve conceptual and practical problems. Notably, I'm a member of the BONN-SVRSEGM team which participated in the PASCAL VOC Segmentation Challenge and (co-)won the 2009-2012 challenges.

Even before that, I got my bachelor degree on 2001 in Zhejiang University, and my Ph.D. degree on 2008 in the Institute of Automation, Chinese Academy of Sciences, with a dissertation on Euclidean Metric Learning. My advisor is Jue Wang, a pioneer of artificial intelligence in China (not to be confused with Dr. Jue Wang from Megvii). Besides machine learning, I have also done some proteomics algorithms and software during my Ph.D., collaborating with Professor Youhe Gao from Chinese Academy of Medical Sciences.

Research Interests

I am broadly interested in many machine learning algorithms and applications. Most recently, I have been mainly working on 4 topics:

1) Object Segmentation and Tracking. I have been working on video object segmentation since 2013, with publications in CVPR 2015 as well as placing 4th place in the DAVIS 2017 Video Object Segmentation Challenge. On the related problem of multi-target tracking, I have been working on the MHT-DAM algorithm and most recently we have proposed MHT-bLSTM which proposes a novel formulation of LSTM for solving the multi-target tracking problem.

2) Point Clouds. We recently proposed PointConv, an approach to construct exact convolutional neural networks on point clouds, as in 2D images.

3) Understanding Deep Neural Networks. We have proposed the XNN, which explains neural network decisions with several learned concepts that are visualized to human. Most recently, we proposed I-GOS, which visualizes the important parts a deep network is looking when it makes a classification. I-GOS is "causal" in that the CNN is often capable of classifying the object from only a very small highlighted region, and no longer classifying the object if the region is removed!

4) Uncertainty and Robust Deep Neural Networks. Recently we have proposed HyperGAN, which generates complete neural networks with a GAN that does not require additional training. One can easily sample 100 or 1000 diverse, well-trained networks from a trained hyperGAN and that improves our capability of estimating uncertainty and adversarial robustness. We also have prior work on adversarial defense by convolutional filter statistics and bilateral filtering.

Previously, I have been working for several years on the semantic image segmentation problem. Collaborating with Joao Carreira and Cristian Sminchisescu, our object segmentation/recognition system won the PASCAL VOC 2009-2012 Segmentation Challenge (with Adrian Ion as well in 2012). I mainly work in the recognition part of the system, on how to correctly classify and generate the final segmentation from a pool of initial figure-ground segmentations. The composite statistical modeling framework I proposed with colleagues is supposed to unseat pairwise CRF approaches for higher-order inference in fully-connected graphical models.

Publishing under the name of Li Fuxin starting from 2019

This is a Chinese name, where Fuxin (pronounced FOO-SHEEN) is the first name and Li (pronounced LEE) is the last name. Call me Fuxin when we meet.

AI/Robotics in OSU

I am a proud member of the new CoRIS institute in Oregon State University. We are a large and very strong research group in artificial intelligence, machine learning, computer vision, natural language processing and robotics. As of 2018, OSU is ranked 43th overall in the USA by the most up-to-date Computer Science Rankings and 22nd in AI/ML/CV/NLP. At least one ranking puts our robotics program as high as #4 in the USA. If you are using USNews rankings to guide your grad school search, take a read at this article.

Selected Recent Research Highlights:  

Jun. 2023 AutoFocusFormer is published in CVPR 2023 and the code is available. AutoFocusFormer proposed adaptive downsampling in 2D images so that one can "focus" on small, faraway objects in the scene. It is the first end-to-end trained network that utilizes adaptive downsampling and local attention for dense prediction tasks such as semantic/instance segmentation.

Jun. 2023 PointConvFormer is published in CVPR 2023 and the code is available. PointConvFormer propose to add attention into the PointConv process. Besides, it significantly improves over the previous PointConv approach in terms of code quality and efficiency. It achieves the best accuracy-runtime tradeoff on the ScanNet dataset.

May. 2021 The code for iGOS++ is available. This siginficantly improves over our previous I-GOS approach, especially under high resolutions (28x28 or 224x224).

Oct. 2020 We proposed Deep Variational Instance Segmentation, a proposal-free instance segmentation approach that directly predict instance labels with a fully convolutional network. Code can be found here.

May. 2020 Code for PointPWCNet is available!

Nov. 2019 We proposed PointPWCNet, a coarse-to-fine approach to compute scene flow from point clouds with great results. Especially, using PointConv to compute cost volume convolution saves a significant amount of computations!

Oct. 2019 Code and Demo for I-GOS available.

Jun. 2019 Code for HyperGAN is available now!

Apr. 2019 We propose I-GOS, an approach to compute heatmaps for CNNs that generates localized heatmaps that actually correlate with the prediction at any resolution.

Apr. 2019 Code for PointConv is available on the Project Page.

Feb. 2019 Fuxin received an Amazon Research Award.

Jan. 2019 We proposed HyperGAN, a generative adversarial network that can directly generate diverse, fully-trained neural networks (all the weights) from a noise vector!

Nov. 2018 We proposed PointConv, which is a complete, scalable CNN (not depthwise or other approximations) on a point cloud. We have achieved the best result on the ScanNet benchmark for a network based only on a 3D point cloud, without volumetric networks. We have also been able to achieve identical performance to CNNs on CIFAR-10 when representing the image as a point cloud.

Sep. 2018 We proposed the bilinear LSTM, which reformed the LSTM formulation according to intuitions derived from a low-rank approximation of the classical recursive least squares. This has significantly improved the capability of training a multi-target tracking network using an LSTM.

Mar. 2018 Fuxin received an NSF CAREER award.

Jul. 2017 Our GaTech-Oregon State team has obtained the 4th place in the DAVIS Video Segmentation Challenge 2017.

Jul. 2017 Our team (MHT-DAM) has obtained the best overall ranking (Avg Rank) in the Multi-Target Tracking challenge (MOT) 2017 .

Sep. 2015 Two papers accepted in ICCV 2015. We beat the state-of-the-art in both object proposals generation and multi-target tracking.

Mar. 2015 An improvement of the video segmentation framework to handle complete occlusions is accepted to CVPR 2015. Congratulations Zhengyang for the impressive achievement to publish a first-authored paper in CVPR as a junior undergraduate student! Check out the Project Website

Jun. 2014 We proposed RIGOR, an algorithm that achieves accuracy slightly better to CPMC/Object proposals, but significantly faster, generating all object proposals in 2-4 seconds on CPU-only code. Check out the Project Page for more information and downloading the code.

Oct. 2013 We exploited semantic segmentation techniques to perform unsupervised video segmentation based on tracking image segments per-frame with 1) automatic object discovery based on segment pool; 2) efficiently learnt appearance models for hundreds of objects with strong appearance features; 3) CSI refinement of objects. Check out Project Webpage, SegTrack v2 Dataset , ICCV paper.

Jun. 2013 We proposed the Composite Statistical Inference (CSI) for inference on real-valued statistics obtained on multiple high-order overlapping variable subsets, with applications in semantic segmentation. Short version, Long version .

Apr. 2013 We proposed an analytic approximation to the chi-square kernel with geometric convergence and derived from elementary methods. It's more straightforward and converges faster over previous approaches. Preprint

Group (Deep Machine Vision) :

Postdoc:

Chanho Kim

Ph.D. students:

Hung Nguyen

Ziwen Chen

Skand (co-advised with Stefan Lee)

Wesley Khademi

David Smerkous

M.S. students:

Mingqi Jiang

Venkat Kalyanakumar

Alumni:

Ph.D. and Postdocs:

Jialin Yuan (Ph.D. Graduated Nov. 2023, Meta)

Amin Ullah (Postdoctoral Associate 2021-2023, Boeing)

Saeed Khorram (Ph.D. Graduated May 2023, Apple)

Wenxuan Wu (Ph.D. Graduated Apr. 2022, Momenta)

Robert DeBortoli (co-advised with Geoffrey Hollinger) (Ph.D. Graduated Dec. 2021, Agility Robotics)

Neale Ratzlaff (Ph.D. Graduated Jul. 2021, HRL Laboratories)

Xingyi Li (co-advised with Xiaoli Fern) (Ph.D. Graduate Dec. 2020, Dexterity Inc.)

Zhongang Qi (Postdoctoral Associate 2016 - 2019, Tencent Research)

Rahul Sawhney (Ph.D. Graduate April. 2018, Microsoft Research Redmond)

M.S. students:

Tim Player (co-advised with Geoffrey Hollinger) (M.S. Graduated Dec. 2022)

Michael Lowell (M.S. Graduated Dec. 2022)

Mazen Alotaibi (M.S. Graduated Aug. 2022)

Ali Behnoudfar (M.S. Graduated Jun. 2022)

Jay Patravali (M.S. Graduated Nov. 2021, Microsoft)

Damanpreet Kaur (M.S. Graduated May. 2021, Microsoft)

Zehuan Chen (M.S. Graduate Apr. 2019, Amazon)

Lawrence Neal (co-advised with Xiaoli Fern) (founded SleepGlad)

Xinyao Wang (M.S. Graduate Dec. 2018, JD Digits)

Alrik Firl (M.S. Graduate Jul. 2018, General Electrics)

Xin Li (M.S. Graduate Sept. 2016, NVIDIA)

Zheng Zhou (M.S. Graduate Jun. 2017, Tencent AI Lab)

Teaching:  

Winter: CS 535 Deep Learning

Winter: CS 261 Data Structures

Spring: CS 637 Computer Vision II

Publications:  

Preprints

Jialin Yuan, Jay Patravali, Hung Nguyen, Chanho Kim, Li Fuxin. Maximal Cliques on Multi-Frame Proposal Graph for Unsupervised Video Object Segmentation. arXiv:2301.12352

Mingqi Jiang, Saeed Khorram, Li Fuxin. Examining the Difference Among Transformers and CNNs with Explanation Methods. arXiv:2212.06872

Hung Nguyen, Chanho Kim, Fuxim Li. Space Time Recurrent Memory Network. arXiv:2109.06474.

Saeed Khorram, Xiao Fu, Mohammed Danesh, Zhongang Qi, Li Fuxin. Stochastic Block-ADMM for Training Deep Networks . arXiv:2105.00339

Erich Merill III, Stefan Lee, Li Fuxin, Thomas Dietterich, Alan Fern. Deep Convolution for Irregularly Sampled Temporal Point Clouds. arXiv:2105.00137

Neale Ratzlaff, LI Fuxin. Unifying Bilateral Filtering and Adversarial Training for Robust Neural Networks. . arXiv:1804.01635.

Point Clouds

Chanho Kim, Li Fuxin. Object Dynamics Modeling with Hierarchical Point Cloud-based Representations. CVPR, 2024.

Skand Peri, Iain Lee, Chanho Kim, Fuxin Li, Tucker Hermans, Stefan Lee. Point Cloud Models Improve Visual Robustness in Robotic Learners. ICRA, 2024.

Wesley Khademi, Li Fuxin. Diverse Shape Completion via Style Modulated Generative Adversarial Networks. NeuRIPS, 2023.

Ziwen Chen, Kaushik Patnaik, Shuangfei Zhai, Alvin Wan, Zhile Ren, Alex Schwing, Alex Colburn, Li Fuxin. AutoFocusFormer: Image Segmentation off the Grid. CVPR, 2023 Code

Wenxuan Wu, Li Fuxin, Qi Shan. PointConvFormer: Revenge of the Point-Based Convolution. CVPR, 2023. Code

Tim Player, Dongsik Chang, Li Fuxin, Geoffrey Hollinger. Real-Time Generative Grasping with Spatio-Temporal Sparse Convolution. ICRA, 2023

Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin. Improving the Robustness of Point Convolution on K-Nearest Neighbor Neighborhoods with a Viewpoint-Invariant Coordinate Transform. WACV, 2023

Bob DeBortoli, Li Fuxin, Ashish Kapoor, Geoffrey Hollinger. Adversarial Training on Point Clouds for Sim-to-Real 3D Object Detection. IEEE Robotics and Automation Letters (RA-L). 6(4), 2021, 6662-6669. (will also be presented at IROS)

Xianfang Zeng, Wenxuan Wu, Guangzhong Tian, Jun Chen, Fuxin Li, Yong Liu. Deep Superpixel Convolutional Network for Image Recognition. IEEE Signal Processing Letters. Accepted.

Wenxuan Wu, Zhiyuan Wang, Zhuwen Li, Wei Liu, Li Fuxin. PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds. ECCV, 2020 (Spotlight Presentation) Code

Wenxuan Wu, Zhongang Qi, LI Fuxin. PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR, 2019. Project Page Code

R. Sawhney, F. Li, H. Christensen, C. Isbell. Purely Geometric Scene Association and Retrieval - A case for macro-scale geometry. Accepted to ICRA, 2018.

Explainable Deep Learning

Mingqi Jiang, Saeed Khorram, Li Fuxin. Examining the Difference Among Transformers and CNNs with Explanation Methods. CVPR, 2024

Mingqi Jiang, Saeed Khorram, Li Fuxin. Diverse Explanations for Object Detectors with Nesterov-Accelerated iGOS++ . BMVC, 2023.

Saeed Khorram, Li Fuxin. Cycle-Consistent Counterfactuals by Latent Transformations. CVPR, 2022.

(Review Paper) Li Fuxin, Zhongang Qi, Saeed Khorram, Vivswan Shitole, Prasad Tadepalli, Minsuk Kahng, Alan Fern. From Heatmaps to Structured Explanations of Image Classifiers. Applied AI Letters 2(4), e46, 2021.

Matt Olson, Ro Khanna, Lawrence Neal, Fuxin Li, Weng-Keen Wong. Counterfactual State Explanations for Reinforcement Leanring Agents via Generative Deep Learning. Artificial Intelligence 295, 103455, 2021.

Mandana Haidi-Haines, Zhongang Qi, Alan Fern, Fuxin Li, Prasad Tadepalli. User-guided global explanations for deep image recognition: A user study. Applied AI Letters 2(4), e42, 2021.

Vivswan Shitole, Li Fuxin, Minsuk Kahng, Prasad Tadepalli, Alan Fern. Structured Attention Graphs for Understanding Deep Image Classifications. NeuRIPS, 2021.

Saeed Khorram*, Tyler Lawson*, Li Fuxin. iGOS++: Integrated Gradient Optimized Saliency by Bilateral Perturbations ACM Conference on Health, Inference and Learning (CHIL), 2021. (First two authors contributed equally)

Zhongang Qi, Saeed Khorram, Li Fuxin. Embedding Deep Networks into Visual Explanations. Artificial Intelligence, 103435, 2020

Chen Ziwen, Wenxuan Wu, Zhongang Qi, Li Fuxin. Visualizing Point Cloud Classifiers by Curvature Smoothing. BMVC, 2020 Code

Zhongang Qi, Saeed Khorram, LI Fuxin. Visualizing Deep Networks by Optimizing with Integrated Gradients. AAAI , 2020. Code, Demo

Zhongang Qi, Fuxin Li. Embedding Deep Networks into Visual Explanations. arXiv:1709.05360. Short (4 pages) version published in NIPS 2017 Workshop on Interpreting, Explaining and Visualizing Deep Learning - Now what?. Code/Data

Uncertainty and Bayesian Deep Learning

Amin Ullah, Taiqing Yan, Li Fuxin. CVAE-SM: A Conditional Variational Autoencoder with Style Modulation for Efficient Uncertainty Quantification. ICRA, 2024.

Wesley Khademi, Li Fuxin. Diverse Shape Completion via Style Modulated Generative Adversarial Networks. NeuRIPS, 2023. (also listed under Point Clouds)

Neale Ratzlaff*, Qinxun Bai*, Li Fuxin, Wei Xu. Generative Particle Variational Inference via Estimation of Functional Gradients. ICML, 2021. (first two authors contributed equally)

Neale Ratzlaff, Qinxun Bai, Li Fuxin, Wei Xu. Implicit Generative Modeling for Efficient Exploration. ICML, 2020

Neale Ratzlaff, LI Fuxin. HyperGAN: A Generative Model for Diverse, Performant Neural Networks. ICML, 2019. Code

Lawrence Neal, Matthew Olson, Xiaoli Fern, Weng-Keen Wong, Fuxin Li. Open Set Learning with Counterfactual Images. ECCV, 2018. Code

Xin Li, Fuxin Li. Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics . ICCV 2017.

Segmentation and Tracking

Yixuan Huang, Jialin Yuan, Chanho Kim, Pupul Pradhan, Bryan Chen, Li Fuxin, Tucker Hermans. Out of Sight, Still in Mind: Reasoning and Planning about Unobserved Objects with Video Tracking Enabled Memory Models. ICRA, 2024.

Ziwen Chen, Kaushik Patnaik, Shuangfei Zhai, Alvin Wan, Zhile Ren, Alex Schwing, Alex Colburn, Li Fuxin. AutoFocusFormer: Image Segmentation off the Grid. CVPR, 2023 (also listed under Point Clouds)

Ye Yu, Jialin Yuan, Gaurav Mittal, Li Fuxin, Mei Chen. BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation. ECCV , 2022 (Oral presentation)

Chanho Kim, Li Fuxin, Mazen Alotaibi, James Rehg. Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking. CVPR , 2021

Xiaoling Hu, Yusu Wang, Li Fuxin, Dimitris Samaras, Chao Chen. Topology-Aware Segmentation Using Discrete Morse Theory. ICLR, 2021 (Spotlight Presentation)

Jialin Yuan, Chao Chen, Li Fuxin. Deep Variational Instance Segmentation. NeuRIPS, 2020 Code

Xiaoling Hu, LI Fuxin, Dimitris Samaras, Chao Chen. Topology-Preserving Deep Image Segmentation. NeuRIPS, 2019.

Chanho Kim, Fuxin Li, James Rehg. Multi-object Tracking with Neural Gating Using Bilinear LSTM. ECCV, 2018.

Chanho Kim, Fuxin Li, Arridhana Ciptadi, James M. Rehg. Multiple Hypothesis Tracking Revisited. ICCV 2015 (Oral Presentation).

Ahmad Humayun, Fuxin Li, James M. Rehg. The Middle Child Problem: Revisiting Parametric Min-cut for Robust Object Proposals. ICCV 2015.

Zhengyang Wu, Fuxin Li, Rahul Sukthankar, James M. Rehg. Robust Video Segment Proposals with Painless Occlusion Handling. CVPR 2015. Project Website

Ahmad Humayun, Fuxin Li, James M. Rehg. RIGOR: Reusing Inference in Graph Cuts for Generating Object Regions. CVPR 2014. Project Page + Code

Fuxin Li, Taeyoung Kim, Ahmad Humayun, David Tsai, James M. Rehg. Video Segmentation by Tracking Many Figure-Ground Segments. In IEEE International Conference on Computer Vision (ICCV), 2013.
Project Webpage, SegTrack v2 Dataset
(There were a few notation typos around Eq. (7) in the official IEEE version, please use the version on this website).

Fuxin Li, Joao Carreira, Guy Lebanon, Cristian Sminchisescu. Composite Statistical Inference for Semantic Segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013. Technical Report

João Carreira, Fuxin Li, Cristian Sminchisescu. Object Recognition as Ranking Holistic Figure-Ground Hypotheses. International Journal of Computer Vision (IJCV), (First two authors contributed equally), 98(3):243-262, 2012.

Fuxin Li, João Carreira, Cristian Sminchisescu. Object Recognition as Ranking Holistic Figure-Ground Hypotheses. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2010 (First two authors contributed equally). Per-class accuracies for our VOC 2009 final results (37.24%)

Other Machine Learning and Computer Vision

Saeed Khorram, Mingqi Jiang, Mohamad Shahbazi, Mohamad Hosein Danesh, Li Fuxin. Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconfiditional Training at Lower Resolutions. CVPR, 2024.

Jay Patravali*, Gaurav Mittal*, Ye Yu, Li Fuxin, Mei Chen. Unsupervised Few-Shot Action Recognition via Action-Appearance Aligned Meta-Adaptation. ICCV, 2021. (Oral Presentation), (first two authors contributed equally)

Jun Li, Li Fuxin, Sinisa Todorovic. Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform. ICLR, 2020

Xingyi Li, Zhongang Qi, Xiaoli Fern, LI Fuxin. ScaleNet - Improve CNNs through Recursively Scaling Objects. AAAI, 2020.

Xinyao Wang, Liefeng Bo, LI Fuxin. Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression. ICCV, 2019. Code

Robert DeBortoli, LI Fuxin, Geoffrey Hollinger. Elevatenet: A Convolutional Neural Network for Estimating the Missing Dimension in 2D Underwater Sonar Images. IROS, 2019.

R. DeBortoli, A. Nicolai, F. Li, G. Hollinger. Realtime Underwater 3D Reconstruction Using Global Context and Active Labeling. Proceedings of the International Conference on Robotics and Automation (ICRA) 2018.

Peng Lei, Fuxin Li, Sinisa Todorovic. Boundary Flow: A Siamese Network that Predicts Boundary Motion without Training on Motion . CVPR 2018.

Juan Liu, Zhengyang Wu, Fuxin Li. Ranking Video Segments with LSTM and Determinantal Point Processes. ICIP 2017.

Xingyi Li, Fuxin Li, Xiaoli Fern, Raviv Raich. Filter Shaping for Convolutional Networks. ICLR 2017.

Zhaoyang Lv, Chris Beall, Pablo F. Alcantarilla, Fuxin Li, Zsolt Kira, Frank Dellaert. A Continuous Optimization Approach for Efficient and Accurate Scene Flow. ECCV 2016.

Yu-Ying Liu, Shuang Li, Fuxin Li, Le Song, James M. Rehg. Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression. NIPS 2015.

Abhijit Kundu, Yin Li, Frank Dellaert, Fuxin Li, James M. Rehg. Joint Semantic Segmentation and 3D Reconstruction from Monocular Video . ECCV 2014.

Tucker Hermans, Fuxin Li, James M. Rehg, Aaron F. Bobick. Learning Contact Locations for Pushing and Orienting Unknown Objects . In IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2013.

Fuxin Li, Guy Lebanon, Christian Sminchisescu. A Linear Approximation to the chi^2 Kernel with Geometric Convergence. arXiv:1206.4074. [cs.LG]

Tucker Hermans, Fuxin Li, James M. Rehg, Aaron F. Bobick. Learning Stable Pushing Locations. In IEEE International Conference on Development and Learning (ICDL), 2013.

Seungyeon Kim, Fuxin Li, Guy Lebanon, Irfan Essa. Beyond Sentiment: The Manifold of Human Emotions. In Proceedings of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS), 2013.

Mingxuan Sun, Fuxin Li, Joonseok Lee, Ke Zhou, Guy Lebanon, Hongyuan Zha. Learning Multiple-Question Decision Trees for Cold-Start Recommendation. In ACM International Conference on Web Search and Data Mining (WSDM), 2013 (Spotlight presentation).

Eduards G. Bazavan, Fuxin Li, Cristian Sminchisescu. Fourier Kernel Learning. In European Conference on Computer Vision (ECCV), 2012 (Oral presentation).

Fuxin Li, Guy Lebanon, Cristian Sminchisescu. Chebyshev Approximations to the Histogram Chi-Square Kernel. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2012. Long version Code (Updated Jan. 4 2013, a few bug fixes especially for Windows)
(Note: This paper is obsolete, please check out our new arXiv (Section 4) which has real geometric convergence rate and better empirical performance: A Linear Approximation to the chi^2 Kernel with Geometric Convergence. arXiv:1206.4074. [cs.LG])

Jaegul Choo, Fuxin Li, Keehyoung Joo, Haesun Park. A Visual Analytics Approach for Protein Disorder Prediction. Expanding the Frontiers of Visual Analytics and Visualization, Springer 2012, pp 163-174.

Catalin Ionescu, Fuxin Li, Cristian Sminchisescu. Latent Structured Models for Human Pose Estimation. In IEEE International Conference on Computer Vision (ICCV), 2011 (Oral presentation).

Fuxin Li, Cristian Sminchisescu. Convex Multiple Instance Learning by Estimating Likelihood Ratio, Advances in Neural Processing Systems (NIPS), 2010. Supplementary Material

Fuxin Li, Catalin Ionescu, Cristian Sminchisescu. Random Fourier approximations for skewed multiplicative histogram kernels. In German Association for Pattern Recognition (Deutsche Arbeitsgemeinschaft für Mustererkennung, DAGM), 2010. DAGM prize paper. Code available

Fuxin Li, Cristian Sminchisescu. The Feature Selection Path in Kernel Methods. In Artificial Intelligence and Statistics (AISTATS), 2010.

Fen Xia, Yanwu Yang, Liang Zhou, Fuxin Li, Min Cai, Daniel D. Zeng: A closed-form reduction of multi-class cost-sensitive learning to weighted multi-class learning. Pattern Recognition 42(7): 1572-1581 (2009).

Fuxin Li, Yunshan Fu, Yu-Hong Dai, Crisitian Sminchisescu, Jue Wang. Kernel Learning by Unconstrained Optimization. In Artificial Intelligence and Statistics (AISTATS), 2009.

Fen Xia, Wensheng Zhang, Fuxin Li, Yanwu Yang. Ranking with Decision Tree. Knowledge and Information Systems. 17(3):381-395 (2008)

for One-Class SVM. ISNN (1) 2008: 645-654

Fuxin Li, Jian Yang, Jue Wang. A Transductive Framework of Distance Metric Learning by Spectral Dimensionality Reduction. In Proceedings of International Conference on Machine Learning (ICML), 2007

Peng Jin, Danqing Zhu, Fuxin Li, Yunfang Wu. PKU: Combining Supervised Classifiers with Features Selection. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), 2007.

Jian Yang, Fuxin Li, Jue Wang. A Better Scaled Local Tangent Space Alignment Algorithm. Proceedings of International Joint Conference on Neural Networks (IJCNN), 2005

Proteomics

Chen Shao, Wei Sun, Fuxin Li, Ruifeng Yang, Ling Zhang, Youhe Gao. Oscore: a combined score to reduce false negative rates for peptide identification in tandem mass spectrometry analysis. Journal of Mass Spectrometry. 2009(14):1, 25-31.

Linjie Wang, Fuxin Li, Wei Sun, Shuzhen Wu, Xiaorong Wang, Ling Zhang, Dexian Zheng, Jue Wang, and Youhe Gao. Concanavalin A-captured Glycoproteins in Healthy Human Urine. Molecular & Cellular Proteomics. 2006(5): 560 - 562

Wei Sun, Fuxin Li, Shuzhen Wu, Xiaorong Wang, Dexian Zheng, Jue Wang, Youhe Gao. Human urine proteome analysis by three separation approaches. Proteomics. 2005(5): 4994-5001

Fuxin Li, Wei Sun, Youhe Gao, Jue Wang. RScore: A Peptide Randomicity Score For Evaluating MS/MS Spectra. Rapid Communications in Mass Spectrometry. 2004(18):14,1655-1659

Wei Sun, Fuxin Li, Jue Wang, Dexian Zheng, Youhe Gao. AMASS: Software for Automatically Validating the Quality of MS/MS Spectrum From SEQUEST Results. Molecular & Cellular Proteomics. 2004(3): 1194-1199