Search Results for author: Stan Z. Li

Found 192 papers, 96 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

Short-Long Convolutions Help Hardware-Efficient Linear Attention to Focus on Long Sequences

no code implementations12 Jun 2024 Zicheng Liu, Siyuan Li, Li Wang, Zedong Wang, Yunfan Liu, Stan Z. Li

To mitigate the computational complexity in the self-attention mechanism on long sequences, linear attention utilizes computation tricks to achieve linear complexity, while state space models (SSMs) popularize a favorable practice of using non-data-dependent memory pattern, i. e., emphasize the near and neglect the distant, to processing sequences.

Language Modelling

Gentle-CLIP: Exploring Aligned Semantic In Low-Quality Multimodal Data With Soft Alignment

no code implementations9 Jun 2024 Zijia Song, Zelin Zang, Yelin Wang, Guozheng Yang, Jiangbin Zheng, Kaicheng Yu, Wanyu Chen, Stan Z. Li

Multimodal fusion breaks through the barriers between diverse modalities and has already yielded numerous impressive performances.

Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions

1 code implementation9 Jun 2024 Cheng Tan, Dongxin Lyu, Siyuan Li, Zhangyang Gao, Jingxuan Wei, Siqi Ma, Zicheng Liu, Stan Z. Li

Large Language Models (LLMs) have demonstrated wide-ranging applications across various fields and have shown significant potential in the academic peer-review process.

Review Generation

GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models

1 code implementation1 Jun 2024 Zicheng Liu, Jiahui Li, Siyuan Li, Zelin Zang, Cheng Tan, Yufei Huang, Yajing Bai, Stan Z. Li

The Genomic Foundation Model (GFM) paradigm is expected to facilitate the extraction of generalizable representations from massive genomic data, thereby enabling their application across a spectrum of downstream applications.


Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoning

no code implementations31 May 2024 Cheng Tan, Jingxuan Wei, Linzhuang Sun, Zhangyang Gao, Siyuan Li, Bihui Yu, Ruifeng Guo, Stan Z. Li

Large language models equipped with retrieval-augmented generation (RAG) represent a burgeoning field aimed at enhancing answering capabilities by leveraging external knowledge bases.

Answer Generation Multimodal Reasoning +1

UniIF: Unified Molecule Inverse Folding

no code implementations29 May 2024 Zhangyang Gao, Jue Wang, Cheng Tan, Lirong Wu, Yufei Huang, Siyuan Li, Zhirui Ye, Stan Z. Li

We do such unification in two levels: 1) Data-Level: We propose a unified block graph data form for all molecules, including the local frame building and geometric feature initialization.

Drug Discovery Protein Design

Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning

1 code implementation16 May 2024 Lirong Wu, Yijun Tian, Haitao Lin, Yufei Huang, Siyuan Li, Nitesh V Chawla, Stan Z. Li

Protein-protein bindings play a key role in a variety of fundamental biological processes, and thus predicting the effects of amino acid mutations on protein-protein binding is crucial.

VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling

no code implementations13 May 2024 Siyuan Li, Zedong Wang, Zicheng Liu, Di wu, Cheng Tan, Jiangbin Zheng, Yufei Huang, Stan Z. Li

In this paper, we introduce VQDNA, a general-purpose framework that renovates genome tokenization from the perspective of genome vocabulary learning.


LongVQ: Long Sequence Modeling with Vector Quantization on Structured Memory

no code implementations17 Apr 2024 Zicheng Liu, Li Wang, Siyuan Li, Zedong Wang, Haitao Lin, Stan Z. Li

Transformer models have been successful in various sequence processing tasks, but the self-attention mechanism's computational cost limits its practicality for long sequences.

Computational Efficiency Language Modelling +1

Advances of Deep Learning in Protein Science: A Comprehensive Survey

no code implementations8 Mar 2024 Bozhen Hu, Cheng Tan, Lirong Wu, Jiangbin Zheng, Jun Xia, Zhangyang Gao, Zicheng Liu, Fandi Wu, Guijun Zhang, Stan Z. Li

Protein representation learning plays a crucial role in understanding the structure and function of proteins, which are essential biomolecules involved in various biological processes.

Drug Discovery Protein Function Prediction +2

A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-Distillation

1 code implementation6 Mar 2024 Lirong Wu, Haitao Lin, Zhangyang Gao, Guojiang Zhao, Stan Z. Li

As a result, TGS enjoys the benefits of graph topology awareness in training but is free from data dependency in inference.

Knowledge Distillation

PPFlow: Target-aware Peptide Design with Torsional Flow Matching

no code implementations5 Mar 2024 Haitao Lin, Odin Zhang, Huifeng Zhao, Dejun Jiang, Lirong Wu, Zicheng Liu, Yufei Huang, Stan Z. Li

Therapeutic peptides have proven to have great pharmaceutical value and potential in recent decades.

Drug Discovery

Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks

1 code implementation3 Mar 2024 Tianyu Fan, Lirong Wu, Yufei Huang, Haitao Lin, Cheng Tan, Zhangyang Gao, Stan Z. Li

In this paper, we identify two important collaborative processes for this topic: (1) select: how to select an optimal task combination from a given task pool based on their compatibility, and (2) weigh: how to weigh the selected tasks based on their importance.

Graph Representation Learning

FGBERT: Function-Driven Pre-trained Gene Language Model for Metagenomics

no code implementations24 Feb 2024 Chenrui Duan, Zelin Zang, Yongjie Xu, Hang He, Zihan Liu, Zijia Song, Ju-Sheng Zheng, Stan Z. Li

Metagenomic data, comprising mixed multi-species genomes, are prevalent in diverse environments like oceans and soils, significantly impacting human health and ecological functions.

Contrastive Learning Language Modelling

MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding

1 code implementation22 Feb 2024 Lirong Wu, Yijun Tian, Yufei Huang, Siyuan Li, Haitao Lin, Nitesh V Chawla, Stan Z. Li

In addition, microenvironments defined in previous work are largely based on experimentally assayed physicochemical properties, for which the "vocabulary" is usually extremely small.

Computational Efficiency

Switch EMA: A Free Lunch for Better Flatness and Sharpness

2 code implementations14 Feb 2024 Siyuan Li, Zicheng Liu, Juanxi Tian, Ge Wang, Zedong Wang, Weiyang Jin, Di wu, Cheng Tan, Tao Lin, Yang Liu, Baigui Sun, Stan Z. Li

Exponential Moving Average (EMA) is a widely used weight averaging (WA) regularization to learn flat optima for better generalizations without extra cost in deep neural network (DNN) optimization.

Attribute Image Classification +7

PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction

1 code implementation13 Feb 2024 Lirong Wu, Yufei Huang, Cheng Tan, Zhangyang Gao, Bozhen Hu, Haitao Lin, Zicheng Liu, Stan Z. Li

Compound-Protein Interaction (CPI) prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery.

Drug Discovery

A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer

1 code implementation4 Feb 2024 Zhangyang Gao, Daize Dong, Cheng Tan, Jun Xia, Bozhen Hu, Stan Z. Li

(4) The edge-centric pretraining framework GraphsGPT demonstrates its efficacy in graph domain tasks, excelling in both representation and generation.

Decoder Graph Classification +2

MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning

no code implementations CVPR 2024 Zhe Li, Laurence T. Yang, Bocheng Ren, Xin Nie, Zhangyang Gao, Cheng Tan, Stan Z. Li

The scarcity of annotated data has sparked significant interest in unsupervised pre-training methods that leverage medical reports as auxiliary signals for medical visual representation learning.

Contrastive Learning Image Classification +5

DCS-Net: Pioneering Leakage-Free Point Cloud Pretraining Framework with Global Insights

no code implementations3 Feb 2024 Zhe Li, Zhangyang Gao, Cheng Tan, Stan Z. Li, Laurence T. Yang

Experimental results demonstrate that our method enhances the expressive capacity of existing point cloud models and effectively addresses the issue of information leakage.

Must: Maximizing Latent Capacity of Spatial Transcriptomics Data

1 code implementation15 Jan 2024 Zelin Zang, Liangyu Li, Yongjie Xu, Chenrui Duan, Kai Wang, Yang You, Yi Sun, Stan Z. Li

MuST integrates the multi-modality information contained in the ST data effectively into a uniform latent space to provide a foundation for all the downstream tasks.

Deep Manifold Transformation for Protein Representation Learning

no code implementations12 Jan 2024 Bozhen Hu, Zelin Zang, Cheng Tan, Stan Z. Li

Protein representation learning is critical in various tasks in biology, such as drug design and protein structure or function prediction, which has primarily benefited from protein language models and graph neural networks.

Representation Learning

Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding

no code implementations12 Jan 2024 Bozhen Hu, Zelin Zang, Jun Xia, Lirong Wu, Cheng Tan, Stan Z. Li

Representing graph data in a low-dimensional space for subsequent tasks is the purpose of attributed graph embedding.

Graph Embedding

Graph-level Protein Representation Learning by Structure Knowledge Refinement

no code implementations5 Jan 2024 Ge Wang, Zelin Zang, Jiangbin Zheng, Jun Xia, Stan Z. Li

The mainstream method is utilizing contrastive learning to facilitate graph feature extraction, known as Graph Contrastive Learning (GCL).

Contrastive Learning Property Prediction +1

General Point Model Pretraining with Autoencoding and Autoregressive

1 code implementation CVPR 2024 Zhe Li, Zhangyang Gao, Cheng Tan, Bocheng Ren, Laurence T. Yang, Stan Z. Li

Compared to models like Point-BERT MaskPoint and PointMAE our GPM achieves superior performance in point cloud understanding tasks.

Decoder Language Modelling +3

Masked Modeling for Self-supervised Representation Learning on Vision and Beyond

1 code implementation31 Dec 2023 Siyuan Li, Luyuan Zhang, Zedong Wang, Di wu, Lirong Wu, Zicheng Liu, Jun Xia, Cheng Tan, Yang Liu, Baigui Sun, Stan Z. Li

As the deep learning revolution marches on, self-supervised learning has garnered increasing attention in recent years thanks to its remarkable representation learning ability and the low dependence on labeled data.

Representation Learning Self-Supervised Learning

Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models

no code implementations7 Dec 2023 Yijie Zhang, Zhangyang Gao, Cheng Tan, Stan Z. Li

Predicting protein stability changes induced by single-point mutations has been a persistent challenge over the years, attracting immense interest from numerous researchers.

Computational Efficiency

Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency Training

1 code implementation23 Nov 2023 Cheng Tan, Jingxuan Wei, Zhangyang Gao, Linzhuang Sun, Siyuan Li, Xihong Yang, Stan Z. Li

Remarkably, we show that even smaller base models, when equipped with our proposed approach, can achieve results comparable to those of larger models, illustrating the potential of our approach in harnessing the power of rationales for improved multimodal reasoning.

Multimodal Reasoning Science Question Answering +1

Segment Anything in Defect Detection

no code implementations17 Nov 2023 Bozhen Hu, Bin Gao, Cheng Tan, Tongle Wu, Stan Z. Li

Defect detection plays a crucial role in infrared non-destructive testing systems, offering non-contact, safe, and efficient inspection capabilities.

Defect Detection

General Point Model with Autoencoding and Autoregressive

no code implementations25 Oct 2023 Zhe Li, Zhangyang Gao, Cheng Tan, Stan Z. Li, Laurence T. Yang

This model is versatile, allowing fine-tuning for downstream point cloud representation tasks, as well as unconditional and conditional generation tasks.

Decoder Language Modelling +3

Revisiting the Temporal Modeling in Spatio-Temporal Predictive Learning under A Unified View

no code implementations9 Oct 2023 Cheng Tan, Jue Wang, Zhangyang Gao, Siyuan Li, Lirong Wu, Jun Xia, Stan Z. Li

In this paper, we re-examine the two dominant temporal modeling approaches within the realm of spatio-temporal predictive learning, offering a unified perspective.

Self-Supervised Learning

CONVERT:Contrastive Graph Clustering with Reliable Augmentation

2 code implementations17 Aug 2023 Xihong Yang, Cheng Tan, Yue Liu, Ke Liang, Siwei Wang, Sihang Zhou, Jun Xia, Stan Z. Li, Xinwang Liu, En Zhu

To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT).

Clustering Contrastive Learning +4

Reinforcement Graph Clustering with Unknown Cluster Number

2 code implementations13 Aug 2023 Yue Liu, Ke Liang, Jun Xia, Xihong Yang, Sihang Zhou, Meng Liu, Xinwang Liu, Stan Z. Li

To enable the deep graph clustering algorithms to work without the guidance of the predefined cluster number, we propose a new deep graph clustering method termed Reinforcement Graph Clustering (RGC).

Clustering Graph Clustering +1

Enhancing Human-like Multi-Modal Reasoning: A New Challenging Dataset and Comprehensive Framework

1 code implementation24 Jul 2023 Jingxuan Wei, Cheng Tan, Zhangyang Gao, Linzhuang Sun, Siyuan Li, Bihui Yu, Ruifeng Guo, Stan Z. Li

Multimodal reasoning is a critical component in the pursuit of artificial intelligence systems that exhibit human-like intelligence, especially when tackling complex tasks.

Contrastive Learning Multimodal Reasoning +2

Efficient Prediction of Peptide Self-assembly through Sequential and Graphical Encoding

1 code implementation17 Jul 2023 Zihan Liu, Jiaqi Wang, Yun Luo, Shuang Zhao, Wenbin Li, Stan Z. Li

In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides.


Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?

no code implementations30 Jun 2023 Jun Xia, Lecheng Zhang, Xiao Zhu, Stan Z. Li

Molecular property prediction (MPP) is a crucial task in the drug discovery pipeline, which has recently gained considerable attention thanks to advances in deep neural networks.

Drug Discovery Molecular Property Prediction +1

OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning

2 code implementations NeurIPS 2023 Cheng Tan, Siyuan Li, Zhangyang Gao, Wenfei Guan, Zedong Wang, Zicheng Liu, Lirong Wu, Stan Z. Li

Spatio-temporal predictive learning is a learning paradigm that enables models to learn spatial and temporal patterns by predicting future frames from given past frames in an unsupervised manner.

Weather Forecasting

Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs

1 code implementation9 Jun 2023 Lirong Wu, Haitao Lin, Yufei Huang, Stan Z. Li

To bridge the gaps between topology-aware Graph Neural Networks (GNNs) and inference-efficient Multi-Layer Perceptron (MLPs), GLNN proposes to distill knowledge from a well-trained teacher GNN into a student MLP.

Dink-Net: Neural Clustering on Large Graphs

3 code implementations28 May 2023 Yue Liu, Ke Liang, Jun Xia, Sihang Zhou, Xihong Yang, Xinwang Liu, Stan Z. Li

Subsequently, the clustering distribution is optimized by minimizing the proposed cluster dilation loss and cluster shrink loss in an adversarial manner.

Clustering Graph Clustering +1

Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinement

1 code implementation20 May 2023 Zhangyang Gao, Cheng Tan, Stan Z. Li

After witnessing the great success of pretrained models on diverse protein-related tasks and the fact that recovery is highly correlated with confidence, we wonder whether this knowledge can push the limits of protein design further.

Protein Design Retrieval +1

Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting it into MLPs: An Effective GNN-to-MLP Distillation Framework

1 code implementation18 May 2023 Lirong Wu, Haitao Lin, Yufei Huang, Tianyu Fan, Stan Z. Li

Furthermore, we identified a potential information drowning problem for existing GNN-to-MLP distillation, i. e., the high-frequency knowledge of the pre-trained GNNs may be overwhelmed by the low-frequency knowledge during distillation; we have described in detail what it represents, how it arises, what impact it has, and how to deal with it.

High-dimensional Clustering onto Hamiltonian Cycle

no code implementations27 Apr 2023 Tianyi Huang, Shenghui Cheng, Stan Z. Li, Zhengjun Zhang

Then, the anchors of different clusters are sorted on the optimal Hamiltonian cycle generated by the cluster similarities and mapped on the circumference of a circle.

Clustering Deep Clustering +1

Cross-Gate MLP with Protein Complex Invariant Embedding is A One-Shot Antibody Designer

1 code implementation21 Apr 2023 Cheng Tan, Zhangyang Gao, Lirong Wu, Jun Xia, Jiangbin Zheng, Xihong Yang, Yue Liu, Bozhen Hu, Stan Z. Li

In this paper, we propose a \textit{simple yet effective} model that can co-design 1D sequences and 3D structures of CDRs in a one-shot manner.


InstructBio: A Large-scale Semi-supervised Learning Paradigm for Biochemical Problems

1 code implementation8 Apr 2023 Fang Wu, Huiling Qin, Siyuan Li, Stan Z. Li, Xianyuan Zhan, Jinbo Xu

In the field of artificial intelligence for science, it is consistently an essential challenge to face a limited amount of labeled data for real-world problems.

molecular representation Representation Learning

Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias

1 code implementation29 Mar 2023 Zihan Liu, Yun Luo, Lirong Wu, Zicheng Liu, Stan Z. Li

It has become cognitive inertia to employ cross-entropy loss function in classification related tasks.

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.

Masked Language Modeling Protein Design +1

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

1 code implementation CVPR 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 +1

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.

RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design

1 code implementation25 Jan 2023 Cheng Tan, Yijie Zhang, Zhangyang Gao, Bozhen Hu, Siyuan Li, Zicheng Liu, Stan Z. Li

We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure.

Contrastive Learning Protein Design +2

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

1 code implementation22 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?

Decoder Denoising +1

Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching

1 code implementation7 Jan 2023 Fang Wu, Siyuan Li, Xurui Jin, Yinghui Jiang, Dragomir Radev, Zhangming Niu, Stan Z. Li

It takes advantage of MatchExplainer to fix the most informative portion of the graph and merely operates graph augmentations on the rest less informative part.

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

Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks

1 code implementation7 Dec 2022 Fang Wu, Lirong Wu, Dragomir Radev, Jinbo Xu, Stan Z. Li

Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area.

Protein Interface Prediction Representation Learning

Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective

1 code implementation2 Dec 2022 Cheng Tan, Zhangyang Gao, Hanqun Cao, Xingran Chen, Ge Wang, Lirong Wu, Jun Xia, Jiangbin Zheng, Stan Z. Li

In this work, we reformulate the RNA secondary structure prediction as a K-Rook problem, thereby simplifying the prediction process into probabilistic matching within a finite solution space.

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.

Protein Folding Protein Language Model +1

SimVP: Towards Simple yet Powerful Spatiotemporal Predictive Learning

2 code implementations22 Nov 2022 Cheng Tan, Zhangyang Gao, Siyuan Li, Stan Z. Li

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

Video Prediction

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

EVNet: An Explainable Deep Network for Dimension Reduction

1 code implementation21 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

MogaNet: Multi-order Gated Aggregation Network

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

Notably, MogaNet hits 80. 0\% and 87. 8\% accuracy with 5. 2M and 181M parameters on ImageNet-1K, outperforming ParC-Net and ConvNeXt-L, while saving 59\% FLOPs and 17M parameters, respectively.

3D Human Pose Estimation Image Classification +6

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 Chemical Pre-trained Models

2 code implementations29 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.

molecular representation Property Prediction

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, Pablo Chacón, Stan Z. Li

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

Decoder Protein Design

OpenMixup: A Comprehensive Mixup Benchmark for Visual Classification

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

Data mixing, or mixup, is a data-dependent augmentation technique that has greatly enhanced the generalizability of modern deep neural networks.

Benchmarking Classification +3

A Survey on Generative Diffusion Model

1 code implementation6 Sep 2022 Hanqun Cao, Cheng Tan, Zhangyang Gao, Yilun Xu, Guangyong Chen, Pheng-Ann Heng, Stan Z. Li

Deep generative models are a prominent approach for data generation, and have been used to produce high quality samples in various domains.

Dimensionality Reduction

What Does the Gradient Tell When Attacking the Graph Structure

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

To address this issue, we propose a novel surrogate model with multi-level propagation that preserves the node dissimilarity information.

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.

Computational Efficiency

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 feature selection

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

3 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

1 code implementation24 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 Design 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 Time Series Analysis

Harnessing Hard Mixed Samples with Decoupled Regularizer

1 code implementation NeurIPS 2023 Zicheng Liu, Siyuan Li, Ge Wang, Cheng Tan, Lirong Wu, Stan Z. Li

However, we found that the extra optimizing step may be redundant because label-mismatched mixed samples are informative hard mixed samples for deep models to localize discriminative features.

Data Augmentation

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 Retrosynthesis

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.

Decoder Protein Design +1

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

For example, dimension reduction methods, t-SNE, and UMAP optimize pair-wise data relationships by preserving the global geometric structure, while self-supervised learning, SimCLR, and BYOL focus on mining the local statistics of instances under specific augmentations.

Contrastive Learning Dimensionality Reduction +4

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

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.

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

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 feature selection

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.

Clustering Graph Embedding +3

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

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

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.

Attribute Learning with noisy labels +3

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.

Clustering Dimensionality Reduction +2

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.

Clustering Deep Clustering +1

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 Clustering

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.

Clustering Deep Clustering +1

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

6 code implementations 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

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

11 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 object-detection +1

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

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

1 code implementation24 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 +3

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

3 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

3 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 +2

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 #10 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

Ensemble Soft-Margin Softmax Loss for Image Classification

no code implementations10 May 2018 Xiaobo Wang, Shifeng Zhang, Zhen Lei, Si Liu, Xiaojie Guo, Stan Z. Li

On the other hand, the learned classifier of softmax loss is weak.