Search Results for author: Stan Z. Li

Found 135 papers, 55 papers with code

Beyond 3DMM Space: Towards Fine-grained 3D Face Reconstruction

1 code implementation ECCV 2020 Xiangyu Zhu, Fan Yang, Di Huang, Chang Yu, Hao Wang, Jianzhu Guo, Zhen Lei, Stan Z. Li

However, most of their training data is constructed by 3D Morphable Model, whose space spanned is only a small part of the shape space.

3D Face Reconstruction

Lightweight Contrastive Protein Structure-Sequence Transformation

no code implementations19 Mar 2023 Jiangbin Zheng, Ge Wang, Yufei Huang, Bozhen Hu, Siyuan Li, Cheng Tan, Xinwen Fan, Stan Z. Li

In this work, we introduce a novel unsupervised protein structure representation pretraining with a robust protein language model.

Language Modelling Masked Language Modeling

CVT-SLR: Contrastive Visual-Textual Transformation for Sign Language Recognition with Variational Alignment

no code implementations10 Mar 2023 Jiangbin Zheng, Yile Wang, Cheng Tan, Siyuan Li, Ge Wang, Jun Xia, Yidong Chen, Stan Z. Li

In this work, we propose a novel contrastive visual-textual transformation for SLR, CVT-SLR, to fully explore the pretrained knowledge of both the visual and language modalities.

Sign Language Recognition

PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding

no code implementations14 Feb 2023 Zhangyang Gao, Yuqi Hu, Cheng Tan, Stan Z. Li

Is there a unified model for generating molecules considering different conditions, such as binding pockets and chemical properties?

Multi-Task Learning

Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions

1 code implementation8 Feb 2023 Yun Luo, Zihan Liu, Stan Z. Li, Yue Zhang

(Dis)agreement detection aims to identify the authors' attitudes or positions (\textit{{agree, disagree, neutral}}) towards a specific text.

Knowledge Graph Embedding Language Modelling

Characterization and Generation of 3D Realistic Geological Particles with Metaball Descriptor based on X-Ray Computed Tomography

no code implementations5 Feb 2023 Yifeng Zhao, Xiangbo Gao, Pei Zhang, Liang Lei, S. A. Galindo-Torres, Stan Z. Li

This algorithm can capture the main contour of parental particles with a series of non-overlapping spheres and refine surface-texture details through gradient search.

Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoy

no code implementations5 Feb 2023 Yufei Huang, Lirong Wu, Haitao Lin, Jiangbin Zheng, Ge Wang, Stan Z. Li

Learning meaningful protein representation is important for a variety of biological downstream tasks such as structure-based drug design.

Generative Tertiary Structure-based RNA Design

no code implementations25 Jan 2023 Cheng Tan, Zhangyang Gao, Stan Z. Li

Analogous to protein design, RNA design is also an important topic in synthetic biology, which aims to generate RNA sequences by given structures.

Protein Structure Prediction

DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraints

no code implementations22 Jan 2023 Zhangyang Gao, Cheng Tan, Stan Z. Li

Have you ever been troubled by the complexity and computational cost of SE(3) protein structure modeling and been amazed by the simplicity and power of language modeling?

Denoising Language Modelling

Explaining Graph Neural Networks via Non-parametric Subgraph Matching

no code implementations7 Jan 2023 Fang Wu, Siyuan Li, Lirong Wu, Dragomir Radev, Yinghui Jiang, Xurui Jin, Zhangming Niu, Stan Z. Li

The great success in graph neural networks (GNNs) provokes the question about explainability: Which fraction of the input graph is the most determinant of the prediction?

Graph Sampling

Surveillance Face Anti-spoofing

no code implementations3 Jan 2023 Hao Fang, Ajian Liu, Jun Wan, Sergio Escalera, Chenxu Zhao, Xu Zhang, Stan Z. Li, Zhen Lei

In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks.

Contrastive Learning Face Anti-Spoofing +2

A Survey on Protein Representation Learning: Retrospect and Prospect

1 code implementation31 Dec 2022 Lirong Wu, Yufei Huang, Haitao Lin, Stan Z. Li

To pave the way for AI researchers with little bioinformatics background, we present a timely and comprehensive review of PRL formulations and existing PRL methods from the perspective of model architectures, pretext tasks, and downstream applications.

Representation Learning

Federated Learning for Inference at Anytime and Anywhere

no code implementations8 Dec 2022 Zicheng Liu, Da Li, Javier Fernandez-Marques, Stefanos Laskaridis, Yan Gao, Łukasz Dudziak, Stan Z. Li, Shell Xu Hu, Timothy Hospedales

Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication cost, robustness to heterogeneous data, and support for diverse device capabilities.

Federated Learning

RFold: Towards Simple yet Effective RNA Secondary Structure Prediction

1 code implementation2 Dec 2022 Cheng Tan, Zhangyang Gao, Stan Z. Li

The secondary structure of ribonucleic acid (RNA) is more stable and accessible in the cell than its tertiary structure, making it essential in functional prediction.

Protein Language Models and Structure Prediction: Connection and Progression

1 code implementation30 Nov 2022 Bozhen Hu, Jun Xia, Jiangbin Zheng, Cheng Tan, Yufei Huang, Yongjie Xu, Stan Z. Li

The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding.

Language Modelling Protein Folding +1

SimVP: Towards Simple yet Powerful Spatiotemporal Predictive Learning

1 code implementation22 Nov 2022 Cheng Tan, Zhangyang Gao, Stan Z. Li

Without introducing any extra tricks and strategies, SimVP can achieve superior performance on various benchmark datasets.

Video Prediction

Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier

no code implementations21 Nov 2022 Zelin Zang, Lei Shang, Senqiao Yang, Baigui Sun, Stan Z. Li

SAN includes a novel data-augmentation-based CL loss, which is used to improve the representational capability, and a more human-intuitive classifier, which is used to improve the new class discovery capability.

Contrastive Learning Data Augmentation +3

EVNet: An Explainable Deep Network for Dimension Reduction

no code implementations21 Nov 2022 Zelin Zang, Shenghui Cheng, Linyan Lu, Hanchen Xia, Liangyu Li, Yaoting Sun, Yongjie Xu, Lei Shang, Baigui Sun, Stan Z. Li

The proposed techniques are integrated with a visual interface to help the user to adjust EVNet to achieve better DR performance and explainability.

Data Augmentation Dimensionality Reduction

DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding

no code implementations21 Nov 2022 Haitao Lin, Yufei Huang, Meng Liu, Xuanjing Li, Shuiwang Ji, Stan Z. Li

Previous works usually generate atoms in an auto-regressive way, where element types and 3D coordinates of atoms are generated one by one.

Drug Discovery

Efficient Multi-order Gated Aggregation Network

4 code implementations7 Nov 2022 Siyuan Li, Zedong Wang, Zicheng Liu, Cheng Tan, Haitao Lin, Di wu, ZhiYuan Chen, Jiangbin Zheng, Stan Z. Li

Since the recent success of Vision Transformers (ViTs), explorations toward ViT-style architectures have triggered the resurgence of ConvNets.

 Ranked #1 on Instance Segmentation on COCO test-dev (AP50 metric)

3D Human Pose Estimation Image Classification +5

Leveraging Graph-based Cross-modal Information Fusion for Neural Sign Language Translation

no code implementations1 Nov 2022 Jiangbin Zheng, Siyuan Li, Cheng Tan, Chong Wu, Yidong Chen, Stan Z. Li

Therefore, we propose to introduce additional word-level semantic knowledge of sign language linguistics to assist in improving current end-to-end neural SLT models.

Sign Language Translation Translation

Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings

1 code implementation ACL 2022 Jiangbin Zheng, Yile Wang, Ge Wang, Jun Xia, Yufei Huang, Guojiang Zhao, Yue Zhang, Stan Z. Li

Although contextualized embeddings generated from large-scale pre-trained models perform well in many tasks, traditional static embeddings (e. g., Skip-gram, Word2Vec) still play an important role in low-resource and lightweight settings due to their low computational cost, ease of deployment, and stability.

Word Embeddings

A Systematic Survey of Molecular Pre-trained Models

1 code implementation29 Oct 2022 Jun Xia, Yanqiao Zhu, Yuanqi Du, Stan Z. Li

Deep learning has achieved remarkable success in learning representations for molecules, which is crucial for various biochemical applications, ranging from property prediction to drug design.

Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification

no code implementations5 Oct 2022 Lirong Wu, Jun Xia, Haitao Lin, Zhangyang Gao, Zicheng Liu, Guojiang Zhao, Stan Z. Li

Despite their great academic success, Multi-Layer Perceptrons (MLPs) remain the primary workhorse for practical industrial applications.

Classification Node Classification

PiFold: Toward effective and efficient protein inverse folding

1 code implementation22 Sep 2022 Zhangyang Gao, Cheng Tan, Stan Z. Li

How can we design protein sequences folding into the desired structures effectively and efficiently?

OpenMixup: Open Mixup Toolbox and Benchmark for Visual Representation Learning

1 code implementation11 Sep 2022 Siyuan Li, Zedong Wang, Zicheng Liu, Di wu, Stan Z. Li

With the remarkable progress of deep neural networks in computer vision, data mixing augmentation techniques are widely studied to alleviate problems of degraded generalization when the amount of training data is limited.

Representation Learning Self-Supervised Learning +1

What Does the Gradient Tell When Attacking the Graph Structure

no code implementations26 Aug 2022 Zihan Liu, Ge Wang, Yun Luo, Stan Z. Li

The message passing of the GNN-based surrogate model leads to the oversmoothing of nodes connected by inter-class edges, preventing attackers from obtaining the distinctiveness of node features.

Are Gradients on Graph Structure Reliable in Gray-box Attacks?

1 code implementation7 Aug 2022 Zihan Liu, Yun Luo, Lirong Wu, Siyuan Li, Zicheng Liu, Stan Z. Li

These errors arise from rough gradient usage due to the discreteness of the graph structure and from the unreliability in the meta-gradient on the graph structure.

Exploring Generative Neural Temporal Point Process

1 code implementation3 Aug 2022 Haitao Lin, Lirong Wu, Guojiang Zhao, Pai Liu, Stan Z. Li

While lots of previous works have focused on `goodness-of-fit' of TPP models by maximizing the likelihood, their predictive performance is unsatisfactory, which means the timestamps generated by models are far apart from true observations.


UDRN: Unified Dimensional Reduction Neural Network for Feature Selection and Feature Projection

no code implementations8 Jul 2022 Zelin Zang, Yongjie Xu, Linyan Lu, Yulan Geng, Senqiao Yang, Stan Z. Li

We propose that the ideal DR approach combines both FS and FP into a unified end-to-end manifold learning framework, simultaneously performing fundamental feature discovery while maintaining the intrinsic relationships between data samples in the latent space.

Data Augmentation

DLME: Deep Local-flatness Manifold Embedding

2 code implementations7 Jul 2022 Zelin Zang, Siyuan Li, Di wu, Ge Wang, Lei Shang, Baigui Sun, Hao Li, Stan Z. Li

To overcome the underconstrained embedding problem, we design a loss and theoretically demonstrate that it leads to a more suitable embedding based on the local flatness.

Contrastive Learning Data Augmentation +1

CoSP: Co-supervised pretraining of pocket and ligand

no code implementations23 Jun 2022 Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li

Can we inject the pocket-ligand interaction knowledge into the pre-trained model and jointly learn their chemical space?

Contrastive Learning Specificity

SimVP: Simpler yet Better Video Prediction

2 code implementations CVPR 2022 Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li

From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies.

Video Prediction

Hyperspherical Consistency Regularization

1 code implementation CVPR 2022 Cheng Tan, Zhangyang Gao, Lirong Wu, Siyuan Li, Stan Z. Li

Though it benefits from taking advantage of both feature-dependent information from self-supervised learning and label-dependent information from supervised learning, this scheme remains suffering from bias of the classifier.

Contrastive Learning Self-Supervised Learning +1

Discovering and Explaining the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions

1 code implementation15 May 2022 Fang Wu, Siyuan Li, Lirong Wu, Dragomir Radev, Stan Z. Li

Graph neural networks (GNNs) mainly rely on the message-passing paradigm to propagate node features and build interactions, and different graph learning tasks require different ranges of node interactions.

graph construction Graph Learning +2

MVP-Human Dataset for 3D Human Avatar Reconstruction from Unconstrained Frames

no code implementations24 Apr 2022 Xiangyu Zhu, Tingting Liao, Jiangjing Lyu, Xiang Yan, Yunfeng Wang, Kan Guo, Qiong Cao, Stan Z. Li, Zhen Lei

In this paper, we consider a novel problem of reconstructing a 3D human avatar from multiple unconstrained frames, independent of assumptions on camera calibration, capture space, and constrained actions.

Camera Calibration

Generative De Novo Protein Design with Global Context

1 code implementation21 Apr 2022 Cheng Tan, Zhangyang Gao, Jun Xia, Bozhen Hu, Stan Z. Li

Thus, we propose the Global-Context Aware generative de novo protein design method (GCA), consisting of local and global modules.

Protein Structure Prediction

DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations

no code implementations19 Apr 2022 Fang Wu, Stan Z. Li

To waive this requirement, we propose a novel model called DiffMD by directly estimating the gradient of the log density of molecular conformations.

Denoising Drug Discovery

STONet: A Neural-Operator-Driven Spatio-temporal Network

no code implementations18 Apr 2022 Haitao Lin, Guojiang Zhao, Lirong Wu, Stan Z. Li

Graph-based spatio-temporal neural networks are effective to model the spatial dependency among discrete points sampled irregularly from unstructured grids, thanks to the great expressiveness of graph neural networks.

Time Series Analysis

Beyond 3DMM: Learning to Capture High-fidelity 3D Face Shape

no code implementations9 Apr 2022 Xiangyu Zhu, Chang Yu, Di Huang, Zhen Lei, Hao Wang, Stan Z. Li

3D Morphable Model (3DMM) fitting has widely benefited face analysis due to its strong 3D priori.

Decoupled Mixup for Data-efficient Learning

1 code implementation21 Mar 2022 Zicheng Liu, Siyuan Li, Ge Wang, Cheng Tan, Lirong Wu, Stan Z. Li

This also leads to an interesting objective design problem for mixup training that we need to focus on both smoothing the decision boundaries and identifying discriminative features.

Data Augmentation Semi-Supervised Image Classification +1

A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications

3 code implementations16 Feb 2022 Jun Xia, Yanqiao Zhu, Yuanqi Du, Stan Z. Li

Pretrained Language Models (PLMs) such as BERT have revolutionized the landscape of Natural Language Processing (NLP).

Drug Discovery Graph Representation Learning

SemiRetro: Semi-template framework boosts deep retrosynthesis prediction

no code implementations12 Feb 2022 Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li

Experimental results show that SemiRetro significantly outperforms both existing TB and TF methods.

Graph Learning

Target-aware Molecular Graph Generation

no code implementations10 Feb 2022 Cheng Tan, Zhangyang Gao, Stan Z. Li

Building on the recent advantages of flow-based molecular generation models, we propose SiamFlow, which forces the flow to fit the distribution of target sequence embeddings in latent space.

Drug Discovery Graph Generation +1

SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation

1 code implementation7 Feb 2022 Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, Stan Z. Li

Furthermore, we devise adversarial training scheme, dubbed \textbf{AT-SimGRACE}, to enhance the robustness of graph contrastive learning and theoretically explain the reasons.

Contrastive Learning Data Augmentation +1

AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB

1 code implementation1 Feb 2022 Zhangyang Gao, Cheng Tan, Stan Z. Li

While DeepMind has tentatively solved protein folding, its inverse problem -- protein design which predicts protein sequences from their 3D structures -- still faces significant challenges.

Protein Folding

Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup

1 code implementation30 Nov 2021 Siyuan Li, Zicheng Liu, Di wu, Zihan Liu, Stan Z. Li

Mixup is a popular data-dependent augmentation technique for deep neural networks, which contains two sub-tasks, mixup generation, and classification.

Data Augmentation Image Classification +2

GenURL: A General Framework for Unsupervised Representation Learning

1 code implementation27 Oct 2021 Siyuan Li, Zicheng Liu, Zelin Zang, Di wu, ZhiYuan Chen, Stan Z. Li

Unsupervised representation learning (URL) that learns compact embeddings of high-dimensional data without supervision has achieved remarkable progress recently.

Contrastive Learning Dimensionality Reduction +3

Surrogate Representation Learning with Isometric Mapping for Gray-box Graph Adversarial Attacks

no code implementations20 Oct 2021 Zihan Liu, Yun Luo, Zelin Zang, Stan Z. Li

Gray-box graph attacks aim at disrupting the performance of the victim model by using inconspicuous attacks with limited knowledge of the victim model.

Node Classification Representation Learning

ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning

1 code implementation5 Oct 2021 Jun Xia, Lirong Wu, Ge Wang, Jintao Chen, Stan Z. Li

Contrastive Learning (CL) has emerged as a dominant technique for unsupervised representation learning which embeds augmented versions of the anchor close to each other (positive samples) and pushes the embeddings of other samples (negatives) apart.

Contrastive Learning Representation Learning

Improving Discriminative Visual Representation Learning via Automatic Mixup

no code implementations29 Sep 2021 Siyuan Li, Zicheng Liu, Di wu, Stan Z. Li

In this paper, we decompose mixup into two sub-tasks of mixup generation and classification and formulate it for discriminative representations as class- and instance-level mixup.

Data Augmentation Representation Learning

Beyond Message Passing Paradigm: Training Graph Data with Consistency Constraints

no code implementations29 Sep 2021 Lirong Wu, Stan Z. Li

Specifically, the GCL framework is optimized with three well-designed consistency constraints: neighborhood consistency, label consistency, and class-center consistency.

Co-learning: Learning from Noisy Labels with Self-supervision

1 code implementation5 Aug 2021 Cheng Tan, Jun Xia, Lirong Wu, Stan Z. Li

Noisy labels, resulting from mistakes in manual labeling or webly data collecting for supervised learning, can cause neural networks to overfit the misleading information and degrade the generalization performance.

Learning with noisy labels Self-Supervised Learning

A Data-driven feature selection and machine-learning model benchmark for the prediction of longitudinal dispersion coefficient

no code implementations16 Jul 2021 Yifeng Zhao, Pei Zhang, S. A. Galindo-Torres, Stan Z. Li

Then, a global optimal feature set (the channel width, the flow velocity, the channel slope and the cross sectional area) was proposed through numerical comparison of the distilled local optimums in performance with representative ML models.

Ensemble Learning

Unsupervised Deep Manifold Attributed Graph Embedding

1 code implementation27 Apr 2021 Zelin Zang, Siyuan Li, Di wu, Jianzhu Guo, Yongjie Xu, Stan Z. Li

Unsupervised attributed graph representation learning is challenging since both structural and feature information are required to be represented in the latent space.

Graph Embedding Graph Representation Learning +2

AutoMix: Unveiling the Power of Mixup for Stronger Classifiers

2 code implementations24 Mar 2021 Zicheng Liu, Siyuan Li, Di wu, Zihan Liu, ZhiYuan Chen, Lirong Wu, Stan Z. Li

Specifically, AutoMix reformulates the mixup classification into two sub-tasks (i. e., mixed sample generation and mixup classification) with corresponding sub-networks and solves them in a bi-level optimization framework.

Classification Data Augmentation +3

Towards Robust Graph Neural Networks against Label Noise

no code implementations1 Jan 2021 Jun Xia, Haitao Lin, Yongjie Xu, Lirong Wu, Zhangyang Gao, Siyuan Li, Stan Z. Li

A pseudo label is computed from the neighboring labels for each node in the training set using LP; meta learning is utilized to learn a proper aggregation of the original and pseudo label as the final label.

Learning with noisy labels Meta-Learning +2

Deep Manifold Computing and Visualization Using Elastic Locally Isometric Smoothness

no code implementations1 Jan 2021 Stan Z. Li, Zelin Zang, Lirong Wu

The ability to preserve local geometry of highly nonlinear manifolds in high dimensional spaces and properly unfold them into lower dimensional hyperplanes is the key to the success of manifold computing, nonlinear dimensionality reduction (NLDR) and visualization.

Dimensionality Reduction

Consistent Representation Learning for High Dimensional Data Analysis

no code implementations1 Dec 2020 Stan Z. Li, Lirong Wu, Zelin Zang

In this paper, we propose a novel neural network-based method, called Consistent Representation Learning (CRL), to accomplish the three associated tasks end-to-end and improve the consistencies.

Dimensionality Reduction Representation Learning

Face Forgery Detection by 3D Decomposition

no code implementations CVPR 2021 Xiangyu Zhu, Hao Wang, Hongyan Fei, Zhen Lei, Stan Z. Li

Detecting digital face manipulation has attracted extensive attention due to fake media's potential harms to the public.

Deep Manifold Transformation for Nonlinear Dimensionality Reduction

no code implementations28 Oct 2020 Stan Z. Li, Zelin Zang, Lirong Wu

The LGP constraints constitute the loss for deep manifold learning and serve as geometric regularizers for NLDR network training.

Dimensionality Reduction

Invertible Manifold Learning for Dimension Reduction

1 code implementation7 Oct 2020 Siyuan Li, Haitao Lin, Zelin Zang, Lirong Wu, Jun Xia, Stan Z. Li

Dimension reduction (DR) aims to learn low-dimensional representations of high-dimensional data with the preservation of essential information.

Dimensionality Reduction

Deep Clustering and Representation Learning that Preserves Geometric Structures

no code implementations28 Sep 2020 Lirong Wu, Zicheng Liu, Zelin Zang, Jun Xia, Siyuan Li, Stan Z. Li

To overcome the problem that clusteringoriented losses may deteriorate the geometric structure of embeddings in the latent space, an isometric loss is proposed for preserving intra-manifold structure locally and a ranking loss for inter-manifold structure globally.

Deep Clustering Representation Learning

Clustering Based on Graph of Density Topology

1 code implementation24 Sep 2020 Zhangyang Gao, Haitao Lin, Stan Z. Li

GDT jointly considers the local and global structures of data samples: firstly forming local clusters based on a density growing process with a strategy for properly noise handling as well as cluster boundary detection; and then estimating a GDT from relationship between local clusters in terms of a connectivity measure, givingglobal topological graph.

Boundary Detection

Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation

1 code implementation21 Sep 2020 Lirong Wu, Zicheng Liu, Zelin Zang, Jun Xia, Siyuan Li, Stan Z. Li

Though manifold-based clustering has become a popular research topic, we observe that one important factor has been omitted by these works, namely that the defined clustering loss may corrupt the local and global structure of the latent space.

Deep Clustering Representation Learning

Towards Fast, Accurate and Stable 3D Dense Face Alignment

3 code implementations ECCV 2020 Jianzhu Guo, Xiangyu Zhu, Yang Yang, Fan Yang, Zhen Lei, Stan Z. Li

Firstly, on the basis of a lightweight backbone, we propose a meta-joint optimization strategy to dynamically regress a small set of 3DMM parameters, which greatly enhances speed and accuracy simultaneously.

 Ranked #1 on 3D Face Reconstruction on Florence (Mean NME metric)

3D Face Modelling 3D Face Reconstruction +2

SADet: Learning An Efficient and Accurate Pedestrian Detector

no code implementations26 Jul 2020 Chubin Zhuang, Zhen Lei, Stan Z. Li

Although the anchor-based detectors have taken a big step forward in pedestrian detection, the overall performance of algorithm still needs further improvement for practical applications, \emph{e. g.}, a good trade-off between the accuracy and efficiency.

Human Detection Pedestrian Detection +2

Markov-Lipschitz Deep Learning

2 code implementations15 Jun 2020 Stan Z. Li, Zelin Zang, Lirong Wu

We propose a novel framework, called Markov-Lipschitz deep learning (MLDL), to tackle geometric deterioration caused by collapse, twisting, or crossing in vector-based neural network transformations for manifold-based representation learning and manifold data generation.

Dimensionality Reduction Representation Learning +1

Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review

no code implementations23 Apr 2020 Ajian Liu, Xuan Li, Jun Wan, Sergio Escalera, Hugo Jair Escalante, Meysam Madadi, Yi Jin, Zhuoyuan Wu, Xiaogang Yu, Zichang Tan, Qi Yuan, Ruikun Yang, Benjia Zhou, Guodong Guo, Stan Z. Li

Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing.

Face Anti-Spoofing Face Recognition

Learning Meta Face Recognition in Unseen Domains

1 code implementation CVPR 2020 Jianzhu Guo, Xiangyu Zhu, Chenxu Zhao, Dong Cao, Zhen Lei, Stan Z. Li

Face recognition systems are usually faced with unseen domains in real-world applications and show unsatisfactory performance due to their poor generalization.

Face Recognition Meta-Learning

CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofing

no code implementations11 Mar 2020 Ajian Li, Zichang Tan, Xuan Li, Jun Wan, Sergio Escalera, Guodong Guo, Stan Z. Li

Ethnic bias has proven to negatively affect the performance of face recognition systems, and it remains an open research problem in face anti-spoofing.

Face Anti-Spoofing Face Recognition

Static and Dynamic Fusion for Multi-modal Cross-ethnicity Face Anti-spoofing

no code implementations5 Dec 2019 Ajian Liu, Zichang Tan, Xuan Li, Jun Wan, Sergio Escalera, Guodong Guo, Stan Z. Li

Regardless of the usage of deep learning and handcrafted methods, the dynamic information from videos and the effect of cross-ethnicity are rarely considered in face anti-spoofing.

Face Anti-Spoofing

Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

10 code implementations CVPR 2020 Shifeng Zhang, Cheng Chi, Yongqiang Yao, Zhen Lei, Stan Z. Li

In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them.

object-detection Object Detection

WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild

no code implementations25 Sep 2019 Shifeng Zhang, Yiliang Xie, Jun Wan, Hansheng Xia, Stan Z. Li, Guodong Guo

To narrow this gap and facilitate future pedestrian detection research, we introduce a large and diverse dataset named WiderPerson for dense pedestrian detection in the wild.

Ranked #3 on Object Detection on WiderPerson (mMR metric)

Object Detection Pedestrian Detection

Relational Learning for Joint Head and Human Detection

no code implementations24 Sep 2019 Cheng Chi, Shifeng Zhang, Junliang Xing, Zhen Lei, Stan Z. Li, Xudong Zou

Head and human detection have been rapidly improved with the development of deep convolutional neural networks.

Head Detection Human Detection +1

PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes

no code implementations15 Sep 2019 Cheng Chi, Shifeng Zhang, Junliang Xing, Zhen Lei, Stan Z. Li, Xudong Zou

Pedestrian detection in crowded scenes is a challenging problem, because occlusion happens frequently among different pedestrians.

Data Augmentation Occlusion Handling +2

RefineFace: Refinement Neural Network for High Performance Face Detection

no code implementations10 Sep 2019 Shifeng Zhang, Cheng Chi, Zhen Lei, Stan Z. Li

To improve the classification ability for high recall efficiency, STC first filters out most simple negatives from low level detection layers to reduce search space for subsequent classifier, then SML is applied to better distinguish faces from background at various scales and FSM is introduced to let the backbone learn more discriminative features for classification.

Classification Face Detection +2

CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing

no code implementations28 Aug 2019 Shifeng Zhang, Ajian Liu, Jun Wan, Yanyan Liang, Guogong Guo, Sergio Escalera, Hugo Jair Escalante, Stan Z. Li

To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities.

Face Anti-Spoofing Face Recognition

ChaLearn Looking at People: IsoGD and ConGD Large-scale RGB-D Gesture Recognition

no code implementations29 Jul 2019 Jun Wan, Chi Lin, Longyin Wen, Yunan Li, Qiguang Miao, Sergio Escalera, Gholamreza Anbarjafari, Isabelle Guyon, Guodong Guo, Stan Z. Li

The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than $200$ teams round the world.

Gesture Recognition

Improved Selective Refinement Network for Face Detection

no code implementations20 Jan 2019 Shifeng Zhang, Rui Zhu, Xiaobo Wang, Hailin Shi, Tianyu Fu, Shuo Wang, Tao Mei, Stan Z. Li

With the availability of face detection benchmark WIDER FACE dataset, much of the progresses have been made by various algorithms in recent years.

Data Augmentation Face Detection +1

Improving Face Anti-Spoofing by 3D Virtual Synthesis

no code implementations2 Jan 2019 Jianzhu Guo, Xiangyu Zhu, Jinchuan Xiao, Zhen Lei, Genxun Wan, Stan Z. Li

Specifically, we consider a printed photo as a flat surface and mesh it into a 3D object, which is then randomly bent and rotated in 3D space.

Face Anti-Spoofing Face Recognition

A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing

2 code implementations CVPR 2019 Shifeng Zhang, Xiaobo Wang, Ajian Liu, Chenxu Zhao, Jun Wan, Sergio Escalera, Hailin Shi, Zezheng Wang, Stan Z. Li

To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities.

Face Anti-Spoofing Face Recognition

Selective Refinement Network for High Performance Face Detection

3 code implementations7 Sep 2018 Cheng Chi, Shifeng Zhang, Junliang Xing, Zhen Lei, Stan Z. Li, Xudong Zou

In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module.

Face Detection General Classification +1

Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd

no code implementations ECCV 2018 Shifeng Zhang, Longyin Wen, Xiao Bian, Zhen Lei, Stan Z. Li

Pedestrian detection in crowded scenes is a challenging problem since the pedestrians often gather together and occlude each other.

Ranked #8 on Pedestrian Detection on Caltech (using extra training data)

Pedestrian Detection

Large-scale Bisample Learning on ID Versus Spot Face Recognition

no code implementations8 Jun 2018 Xiangyu Zhu, Hao liu, Zhen Lei, Hailin Shi, Fan Yang, Dong Yi, Guo-Jun Qi, Stan Z. Li

In this paper, we propose a deep learning based large-scale bisample learning (LBL) method for IvS face recognition.

Face Recognition General Classification

Face Synthesis for Eyeglass-Robust Face Recognition

1 code implementation4 Jun 2018 Jianzhu Guo, Xiangyu Zhu, Zhen Lei, Stan Z. Li

A feasible method is to collect large-scale face images with eyeglasses for training deep learning methods.

Face Generation Face Model +2

Face Alignment in Full Pose Range: A 3D Total Solution

2 code implementations2 Apr 2018 Xiangyu Zhu, Xiaoming Liu, Zhen Lei, Stan Z. Li

In this paper, we propose to tackle these three challenges in an new alignment framework termed 3D Dense Face Alignment (3DDFA), in which a dense 3D Morphable Model (3DMM) is fitted to the image via Cascaded Convolutional Neural Networks.

3D Pose Estimation Depth Image Estimation +3

Single-Shot Refinement Neural Network for Object Detection

10 code implementations CVPR 2018 Shifeng Zhang, Longyin Wen, Xiao Bian, Zhen Lei, Stan Z. Li

For object detection, the two-stage approach (e. g., Faster R-CNN) has been achieving the highest accuracy, whereas the one-stage approach (e. g., SSD) has the advantage of high efficiency.

object-detection Object Detection

S3FD: Single Shot Scale-Invariant Face Detector

no code implementations ICCV 2017 Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, Stan Z. Li

This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S3FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces.

Face Detection

FaceBoxes: A CPU Real-time Face Detector with High Accuracy

7 code implementations17 Aug 2017 Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, Stan Z. Li

The MSCL aims at enriching the receptive fields and discretizing anchors over different layers to handle faces of various scales.

Face Detection

S$^3$FD: Single Shot Scale-invariant Face Detector

3 code implementations17 Aug 2017 Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, Stan Z. Li

This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S$^3$FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces.

Face Detection

Learning Efficient Image Representation for Person Re-Identification

no code implementations7 Jul 2017 Yang Yang, Shengcai Liao, Zhen Lei, Stan Z. Li

Then, a robust image representation based on color names is obtained by concatenating the statistical descriptors in each stripe.

Person Re-Identification

Deep Person Re-Identification with Improved Embedding and Efficient Training

1 code implementation9 May 2017 Haibo Jin, Xiaobo Wang, Shengcai Liao, Stan Z. Li

However, to achieve this, existing deep models prefer to adopt image pairs or triplets to form verification loss, which is inefficient and unstable since the number of training pairs or triplets grows rapidly as the number of training data grows.

Person Re-Identification

Multi-Modality Fusion based on Consensus-Voting and 3D Convolution for Isolated Gesture Recognition

no code implementations21 Nov 2016 Jiali Duan, Shuai Zhou, Jun Wan, Xiaoyuan Guo, Stan Z. Li

Recently, the popularity of depth-sensors such as Kinect has made depth videos easily available while its advantages have not been fully exploited.

Gesture Recognition

Embedding Deep Metric for Person Re-identication A Study Against Large Variations

no code implementations1 Nov 2016 Hailin Shi, Yang Yang, Xiangyu Zhu, Shengcai Liao, Zhen Lei, Wei-Shi Zheng, Stan Z. Li

From this point of view, selecting suitable positive i. e. intra-class) training samples within a local range is critical for training the CNN embedding, especially when the data has large intra-class variations.

Person Re-Identification

CRAFT Objects from Images

1 code implementation CVPR 2016 Bin Yang, Junjie Yan, Zhen Lei, Stan Z. Li

They decompose the object detection problem into two cascaded easier tasks: 1) generating object proposals from images, 2) classifying proposals into various object categories.

object-detection Object Detection +1

Efficient PSD Constrained Asymmetric Metric Learning for Person Re-Identification

no code implementations ICCV 2015 Shengcai Liao, Stan Z. Li

We argue that the PSD constraint provides a useful regularization to smooth the solution of the metric, and hence the learned metric is more robust than without the PSD constraint.

Metric Learning Person Re-Identification

Adaptively Unified Semi-Supervised Dictionary Learning With Active Points

no code implementations ICCV 2015 Xiaobo Wang, Xiaojie Guo, Stan Z. Li

In this paper, we present a novel semi-supervised dictionary learning method, which uses the informative coding vectors of both labeled and unlabeled data, and adaptively emphasizes the high confidence coding vectors of unlabeled data to enhance the dictionary discriminative capability simultaneously.

Dictionary Learning

Constrained Deep Metric Learning for Person Re-identification

no code implementations24 Nov 2015 Hailin Shi, Xiangyu Zhu, Shengcai Liao, Zhen Lei, Yang Yang, Stan Z. Li

In this paper, we propose a novel CNN-based method to learn a discriminative metric with good robustness to the over-fitting problem in person re-identification.

Metric Learning Person Re-Identification

Face Alignment Across Large Poses: A 3D Solution

no code implementations CVPR 2016 Xiangyu Zhu, Zhen Lei, Xiaoming Liu, Hailin Shi, Stan Z. Li

Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community.

3D Face Reconstruction Face Alignment +2

High-Fidelity Pose and Expression Normalization for Face Recognition in the Wild

no code implementations CVPR 2015 Xiangyu Zhu, Zhen Lei, Junjie Yan, Dong Yi, Stan Z. Li

Pose and expression normalization is a crucial step to recover the canonical view of faces under arbitrary conditions, so as to improve the face recognition performance.

Face Recognition

JOTS: Joint Online Tracking and Segmentation

no code implementations CVPR 2015 Longyin Wen, Dawei Du, Zhen Lei, Stan Z. Li, Ming-Hsuan Yang

We present a novel Joint Online Tracking and Segmentation (JOTS) algorithm which integrates the multi-part tracking and segmentation into a unified energy optimization framework to handle the video segmentation task.

Video Segmentation Video Semantic Segmentation

Convolutional Channel Features

1 code implementation ICCV 2015 Bin Yang, Junjie Yan, Zhen Lei, Stan Z. Li

With the combination of CNN features and boosting forest, CCF benefits from the richer capacity in feature representation compared with channel features, as well as lower cost in computation and storage compared with end-to-end CNN methods.

Edge Detection Face Detection +2

When Face Recognition Meets with Deep Learning: an Evaluation of Convolutional Neural Networks for Face Recognition

no code implementations9 Apr 2015 Guosheng Hu, Yongxin Yang, Dong Yi, Josef Kittler, William Christmas, Stan Z. Li, Timothy Hospedales

In this work, we conduct an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a common ground to make our work easily reproducible.

Face Recognition Metric Learning

Learning Face Representation from Scratch

13 code implementations28 Nov 2014 Dong Yi, Zhen Lei, Shengcai Liao, Stan Z. Li

The current situation in the field of face recognition is that data is more important than algorithm.

Face Recognition

Learn Convolutional Neural Network for Face Anti-Spoofing

2 code implementations24 Aug 2014 Jianwei Yang, Zhen Lei, Stan Z. Li

Moreover, the nets trained using combined data from two datasets have less biases between two datasets.

Face Anti-Spoofing

Open-set Person Re-identification

no code implementations5 Aug 2014 Shengcai Liao, Zhipeng Mo, Jianqing Zhu, Yang Hu, Stan Z. Li

Person re-identification is becoming a hot research for developing both machine learning algorithms and video surveillance applications.

Metric Learning Person Re-Identification

Deep Metric Learning for Practical Person Re-Identification

no code implementations18 Jul 2014 Dong Yi, Zhen Lei, Stan Z. Li

Compared to existing researches, a more practical setting is studied in the experiments that is training and test on different datasets (cross dataset person re-identification).

Metric Learning Person Re-Identification

Aggregate channel features for multi-view face detection

no code implementations15 Jul 2014 Bin Yang, Junjie Yan, Zhen Lei, Stan Z. Li

Face detection has drawn much attention in recent decades since the seminal work by Viola and Jones.

Face Detection Re-Ranking

Person Re-identification by Local Maximal Occurrence Representation and Metric Learning

1 code implementation CVPR 2015 Shengcai Liao, Yang Hu, Xiangyu Zhu, Stan Z. Li

In this paper, we propose an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA).

Metric Learning Person Re-Identification

Shared Representation Learning for Heterogeneous Face Recognition

no code implementations5 Jun 2014 Dong Yi, Zhen Lei, Shengcai Liao, Stan Z. Li

For NIR-VIS problem, we produce new state-of-the-art performance on the CASIA HFB and NIR-VIS 2. 0 databases.

Face Recognition Heterogeneous Face Recognition +1

A Probabilistic Framework for Multitarget Tracking with Mutual Occlusions

no code implementations CVPR 2014 Menglong Yang, Yiguang Liu, Longyin Wen, Zhisheng You, Stan Z. Li

Mutual occlusions among targets can cause track loss or target position deviation, because the observation likelihood of an occluded target may vanish even when we have the estimated location of the target.

The Fastest Deformable Part Model for Object Detection

no code implementations CVPR 2014 Junjie Yan, Zhen Lei, Longyin Wen, Stan Z. Li

Three prohibitive steps in cascade version of DPM are accelerated, including 2D correlation between root filter and feature map, cascade part pruning and HOG feature extraction.

Face Detection object-detection +1

Robust Multi-resolution Pedestrian Detection in Traffic Scenes

no code implementations CVPR 2013 Junjie Yan, Xucong Zhang, Zhen Lei, Shengcai Liao, Stan Z. Li

The model contains resolution aware transformations to map pedestrians in different resolutions to a common space, where a shared detector is constructed to distinguish pedestrians from background.

Pedestrian Detection

Towards Pose Robust Face Recognition

no code implementations CVPR 2013 Dong Yi, Zhen Lei, Stan Z. Li

In this paper, we propose a novel method for pose robust face recognition towards practical applications, which is fast, pose robust and can work well under unconstrained environments.

Face Recognition Robust Face Recognition

Fast Matching by 2 Lines of Code for Large Scale Face Recognition Systems

no code implementations28 Feb 2013 Dong Yi, Zhen Lei, Yang Hu, Stan Z. Li

However, the use of this method is very generic and not limited in face recognition, which can be easily generalized to other biometrics as a post-processing module.

Face Recognition

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