Search Results for author: Yang shen

Found 41 papers, 21 papers with code

Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-Aware Learning

1 code implementation30 Nov 2023 Tengjin Weng, Yang shen, Zhidong Zhao, Zhiming Cheng, Shuai Wang

The training framework of the MPNN is constructed by a teacher-student architecture to learn segmentation from clean pixels and noisy pixels.

Denoising Segmentation

Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval

no code implementations21 Nov 2023 Xiu-Shen Wei, Yang shen, Xuhao Sun, Peng Wang, Yuxin Peng

Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i. e., the same sub-category labels) highest based on the fine-grained details in the query.

Deep Hashing Image Reconstruction +1

FMRT: Learning Accurate Feature Matching with Reconciliatory Transformer

no code implementations20 Oct 2023 Xinyu Zhang, Li Wang, Zhiqiang Jiang, Kun Dai, Tao Xie, Lei Yang, Wenhao Yu, Yang shen, Jun Li

However, these methods only integrate long-range context information among keypoints with a fixed receptive field, which constrains the network from reconciling the importance of features with different receptive fields to realize complete image perception, hence limiting the matching accuracy.

Homography Estimation Pose Estimation +1

Hawkeye: A PyTorch-based Library for Fine-Grained Image Recognition with Deep Learning

1 code implementation14 Oct 2023 Jiabei He, Yang shen, Xiu-Shen Wei, Ye Wu

However, the absence of a unified open-source software library covering various paradigms in FGIR poses a significant challenge for researchers and practitioners in the field.

Fine-Grained Image Recognition

Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy

no code implementations26 Jul 2023 Zhenxiao Yin, Xiaobing Dai, Zewen Yang, Yang shen, Georges Hattab, Hang Zhao

The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs).


Geometric Pooling: maintaining more useful information

no code implementations21 Jun 2023 Hao Xu, Jia Liu, Yang shen, Kenan Lou, Yanxia Bao, Ruihua Zhang, Shuyue Zhou, Hongsen Zhao, Shuai Wang

However, by analyzing the statistical characteristic of activated units after pooling, we found that a large number of units dropped by sorting pooling are negative-value units that contain useful information and can contribute considerably to the final decision.

Node Classification

Enhancing Point Annotations with Superpixel and Confidence Learning Guided for Improving Semi-Supervised OCT Fluid Segmentation

no code implementations5 Jun 2023 Tengjin Weng, Yang shen, Kai Jin, Zhiming Cheng, Yunxiang Li, Gewen Zhang, Shuai Wang, Yaqi Wang

Specifically, we use points to annotate fluid regions in unlabeled OCT images and the Superpixel-Guided Pseudo-Label Generation (SGPLG) module generates pseudo-labels and pixel-level label trust maps from the point annotations.

Denoising Pseudo Label +1

Equiangular Basis Vectors

2 code implementations CVPR 2023 Yang shen, Xuhao Sun, Xiu-Shen Wei

The learning objective of these methods can be summarized as mapping the learned feature representations to the samples' label space.

Metric Learning

A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models

no code implementations18 Mar 2023 Junjie Ye, Xuanting Chen, Nuo Xu, Can Zu, Zekai Shao, Shichun Liu, Yuhan Cui, Zeyang Zhou, Chao Gong, Yang shen, Jie zhou, Siming Chen, Tao Gui, Qi Zhang, Xuanjing Huang

GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on, have gained considerable attention due to their exceptional natural language processing capabilities.

Natural Language Understanding

Delving Deep into Simplicity Bias for Long-Tailed Image Recognition

no code implementations7 Feb 2023 Xiu-Shen Wei, Xuhao Sun, Yang shen, Anqi Xu, Peng Wang, Faen Zhang

Simplicity Bias (SB) is a phenomenon that deep neural networks tend to rely favorably on simpler predictive patterns but ignore some complex features when applied to supervised discriminative tasks.

Long-tail Learning Self-Supervised Learning

Automatic Check-Out via Prototype-based Classifier Learning from Single-Product Exemplars

4 code implementations The European Conference on Computer Vision (ECCV) 2022 Hao Chen, Xiu-Shen Wei, Faen Zhang, Yang shen, Hui Xu, Liang Xiao

Automatic Check-Out (ACO) aims to accurately predict the presence and count of each category of products in check-out images, where a major challenge is the significant domain gap between training data (single-product exemplars) and test data (check-out images).


Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative

1 code implementation7 Oct 2022 Tianxin Wei, Yuning You, Tianlong Chen, Yang shen, Jingrui He, Zhangyang Wang

This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL).

Contrastive Learning Fairness +1

SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval

4 code implementations28 Sep 2022 Yang shen, Xuhao Sun, Xiu-Shen Wei, Qing-Yuan Jiang, Jian Yang

In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON) to learn binary hash codes for dealing with large-scale fine-grained image retrieval tasks.

Image Retrieval Retrieval

A Channel Mix Method for Fine-Grained Cross-Modal Retrieval

3 code implementations IEEE International Conference on Multimedia and Expo (ICME) 2022 Yang shen, Xuhao Sun, Xiu-Shen Wei, Hanxu Hu, Zhipeng Chen

In this paper, we propose a simple but effective method for dealing with the challenging fine-grained cross-modal retrieval task where it aims to enable flexible retrieval among subor-dinate categories across different modalities.

Cross-Modal Retrieval Retrieval

Cone-constrained Monotone Mean-Variance Portfolio Selection Under Diffusion Models

no code implementations31 May 2022 Yang shen, Bin Zou

We consider monotone mean-variance (MMV) portfolio selection problems with a conic convex constraint under diffusion models, and their counterpart problems under mean-variance (MV) preferences.

Webly-Supervised Fine-Grained Recognition with Partial Label Learning

1 code implementation IJCAI 2022 Yu-Yan Xu, Yang shen, Xiu-Shen Wei, Jian Yang

The task of webly-supervised fne-grained recognition is to boost recognition accuracy of classifying subordinate categories (e. g., different bird species)by utilizing freely available but noisy web data. As the label noises signifcantly hurt the network training, it is desirable to distinguish and eliminate noisy images.

Partial Label Learning

Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations

1 code implementation4 Jan 2022 Yuning You, Tianlong Chen, Zhangyang Wang, Yang shen

Accordingly, we have extended the prefabricated discrete prior in the augmentation set, to a learnable continuous prior in the parameter space of graph generators, assuming that graph priors per se, similar to the concept of image manifolds, can be learned by data generation.

Contrastive Learning Graph Learning

A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval

1 code implementation NeurIPS 2021 Xiu-Shen Wei, Yang shen, Xuhao Sun, Han-Jia Ye, Jian Yang

Specifically, based on the captured visual representations by attention, we develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors from the appearance-specific visual representations without attribute annotations.

Image Retrieval Retrieval

Mean-Variance Portfolio Selection in Contagious Markets

no code implementations18 Oct 2021 Yang shen, Bin Zou

We consider a mean-variance portfolio selection problem in a financial market with contagion risk.

Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How

no code implementations ICLR 2022 Yuning You, Yue Cao, Tianlong Chen, Zhangyang Wang, Yang shen

Optimizing an objective function with uncertainty awareness is well-known to improve the accuracy and confidence of optimization solutions.

Variational Inference

Algorithmic insights on continual learning from fruit flies

1 code implementation15 Jul 2021 Yang shen, Sanjoy Dasgupta, Saket Navlakha

We discovered a two layer neural circuit in the fruit fly olfactory system that addresses this challenge by uniquely combining sparse coding and associative learning.

Continual Learning

Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design

1 code implementation24 Jun 2021 Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, Yang shen

Designing novel protein sequences for a desired 3D topological fold is a fundamental yet non-trivial task in protein engineering.

Protein Design

Graph Contrastive Learning Automated

2 code implementations10 Jun 2021 Yuning You, Tianlong Chen, Yang shen, Zhangyang Wang

Unfortunately, unlike its counterpart on image data, the effectiveness of GraphCL hinges on ad-hoc data augmentations, which have to be manually picked per dataset, by either rules of thumb or trial-and-errors, owing to the diverse nature of graph data.

Contrastive Learning Representation Learning +1

Mean-Variance Investment and Risk Control Strategies -- A Time-Consistent Approach via A Forward Auxiliary Process

no code implementations11 Jan 2021 Yang shen, Bin Zou

By introducing a deterministic auxiliary process defined forward in time, we formulate an alternative time-consistent problem related to the original MV problem, and obtain the optimal strategy and the value function to the new problem in closed-form.

Bayesian Learning to Optimize: Quantifying the Optimizer Uncertainty

no code implementations1 Jan 2021 Yue Cao, Tianlong Chen, Zhangyang Wang, Yang shen

Optimizing an objective function with uncertainty awareness is well-known to improve the accuracy and confidence of optimization solutions.

Image Classification Variational Inference

Cross-Modality Protein Embedding for Compound-Protein Affinity and Contact Prediction

no code implementations14 Nov 2020 Yuning You, Yang shen

Compound-protein pairs dominate FDA-approved drug-target pairs and the prediction of compound-protein affinity and contact (CPAC) could help accelerate drug discovery.

Drug Discovery

Graph Contrastive Learning with Augmentations

3 code implementations NeurIPS 2020 Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang shen

In this paper, we propose a graph contrastive learning (GraphCL) framework for learning unsupervised representations of graph data.

Contrastive Learning Representation Learning +2

When Does Self-Supervision Help Graph Convolutional Networks?

1 code implementation ICML 2020 Yuning You, Tianlong Chen, Zhangyang Wang, Yang shen

We first elaborate three mechanisms to incorporate self-supervision into GCNs, analyze the limitations of pretraining & finetuning and self-training, and proceed to focus on multi-task learning.

Multi-Task Learning Representation Learning +1

Network-principled deep generative models for designing drug combinations as graph sets

1 code implementation16 Apr 2020 Mostafa Karimi, Arman Hasanzadeh, Yang shen

We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer.

Graph Embedding

L^2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks

2 code implementations CVPR 2020 Yuning You, Tianlong Chen, Zhangyang Wang, Yang shen

Graph convolution networks (GCN) are increasingly popular in many applications, yet remain notoriously hard to train over large graph datasets.

Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts

no code implementations29 Dec 2019 Mostafa Karimi, Di wu, Zhangyang Wang, Yang shen

DeepRelations shows superior interpretability to the state-of-the-art: without compromising affinity prediction, it boosts the AUPRC of contact prediction 9. 5, 16. 9, 19. 3 and 5. 7-fold for the test, compound-unique, protein-unique, and both-unique sets, respectively.

BIG-bench Machine Learning Drug Discovery +2

Energy-based Graph Convolutional Networks for Scoring Protein Docking Models

no code implementations28 Dec 2019 Yue Cao, Yang shen

Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking.

Learning to Optimize in Swarms

1 code implementation NeurIPS 2019 Yue Cao, Tianlong Chen, Zhangyang Wang, Yang shen

Learning to optimize has emerged as a powerful framework for various optimization and machine learning tasks.


Bayesian active learning for optimization and uncertainty quantification in protein docking

1 code implementation31 Jan 2019 Yue Cao, Yang shen

To the best of our knowledge, this study represents the first uncertainty quantification solution for protein docking, with theoretical rigor and comprehensive assessment.

Active Learning Binary Classification

Egocentric Activity Prediction via Event Modulated Attention

no code implementations ECCV 2018 Yang Shen, Bingbing Ni, Zefan Li, Ning Zhuang

Predicting future activities from an egocentric viewpoint is of particular interest in assisted living.

Activity Prediction Event Extraction

Learning Correspondence Structures for Person Re-identification

no code implementations20 Mar 2017 Weiyao Lin, Yang shen, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang, Ke Lu

We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair.

Patch Matching Person Re-Identification

Person Re-identification with Correspondence Structure Learning

1 code implementation ICCV 2015 Yang Shen, Weiyao Lin, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang

This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification.

Patch Matching Person Re-Identification

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