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Instance-aware diversity feature generation for unsupervised person re-identification

DISPLAYS 2024年 83卷

作者： Zhang, Xiaowei Dou, Xiao Zhao, Xinpeng Li, Guocong Wang, Zekang Qingdao Univ Sch Comp Sci & Technol Qingdao 266071 Peoples R China Shandong Univ Sch Comp Sci & Technol Qingdao 266237 Peoples R China

Unsupervised person re -identification (Re -ID) methods have made significant progress by exploiting contrastive learning from unlabeled data. However, the previous approaches including cluster -level or instance -level contrast loss, did not fully explore inherent commonality of each identified individual from unlabeled samples, where the divergence of individual cluster and convergence of different clusters leads to a set of noisy pseudo labels which may result in label noise accumulation. To address this issue, we propose an instance -aware diversity feature generation (IDFG) framework, which can form a stable clustering feature space by exhuming diverse counterparts of given exemplars to update memory dictionary of each cluster, so as to reduce the effect of noisy labels. Specifically, we combines instance segmentation and masked auto -encoder to generate foreground -invariant diversity counterparts of given exemplars to reduce inter -class convergence caused by background similarity between different identification instances. Further, we introduce an instance -aware diversity feature mining module, which gradually creates more reliable clusters to generate more robust pseudo labels by exploiting the compactness and independence of clustering to update the memory dictionary. Extensive experiments demonstrate that the proposed IDFG framework achieves impressive performances of 85.6%, 73.7%, and 31.0% mAP on Market1501, DukeMTMC-reID and MSMT17, respectively.

关键词： Contrastive learning Diversity features masked autoencoder Pseudo label Unsupervised re-ID

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Single-Image Super-Resolution Method for Rotating Synthetic Aperture System Using Masking Mechanism

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REMOTE SENSING 2024年第9期16卷 1508页

作者： Sun, Yu Zhi, Xiyang Jiang, Shikai Shi, Tianjun Song, Jiachun Yang, Jiawei Wang, Shengao Zhang, Wei Harbin Inst Technol Res Ctr Space Opt Engn Harbin 150001 Peoples R China Boston Univ Div Syst Engn Boston MA 02215 USA

The emerging technology of rotating synthetic aperture (RSA) presents a promising solution for the development of lightweight, large-aperture, and high-resolution optical remote sensing systems in geostationary orbit. However, the rectangular shape of the primary mirror and the distinctive imaging mechanism involving the continuous rotation of the mirror lead to a pronounced decline in image resolution along the shorter side of the rectangle compared to the longer side. The resolution also exhibits periodic time-varying characteristics. To address these limitations and enhance image quality, we begin by analyzing the imaging mechanism of the RSA system. Subsequently, we propose a single-image super-resolution method that utilizes a rotated varied-size window attention mechanism instead of full attention, based on the Vision Transformer architecture. We employ a two-stage training methodology for the network, where we pre-train it on images masked with stripe-shaped masks along the shorter side of the rectangular pupil. Following that, we fine-tune the network using unmasked images. Through the strip-wise mask sampling strategy, this two-stage training approach effectively circumvents the interference of lower confidence (clarity) information and outperforms training the network from scratch using the unmasked degraded images. Our digital simulation and semi-physical imaging experiments demonstrate that the proposed method achieves satisfactory performance. This work establishes a valuable reference for future space applications of the RSA system.

关键词： optical remote sensing super-resolution (SR) rotating synthetic aperture masked autoencoder vision transformer rectangular pupil

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Efficient sparse-view medical image classification for low radiation and rapid COVID-19 diagnosis

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BIOMEDICAL ENGINEERING LETTERS 2025年

作者： Gwak, Seunghyun Yang, Sooyoung Jeong, Heawon Park, Junhu Kang, Myungjoo Seoul Natl Univ Computat Sci & Technol 1 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ Interdisciplinary Program Artificial Intelligence Gwanak Ro 1 Seoul 08826 South Korea Seoul Natl Univ Dept Math Sci Gwanak Ro 1 Seoul 08826 South Korea Seoul Natl Univ Res Inst Math 1 Gwanak Ro Seoul 08826 South Korea

This study proposes a deep learning-based diagnostic model called the Projection-wise masked autoencoder (ProMAE) for rapid and accurate COVID-19 diagnosis using sparse-view CT images. ProMAE employs a column-wise masking strategy during pre-training to effectively learn critical diagnostic features from sinograms, even under extremely sparse conditions. The trained ProMAE can directly classify sparse-view sinograms without requiring CT image reconstruction. Experiments on sparse-view data with 50%, 75%, 85%, 95%, and 99% sparsity show that ProMAE achieves a diagnostic accuracy of over 95% at all sparsity levels and, in particular, outperforms ResNet, ConvNeXt, and conventional MAE models in COVID-19 diagnosis in environments with 85% or higher sparsity. This capability is especially advantageous for the development of portable and flexible imaging systems during large-scale outbreaks such as COVID-19, as it ensures accurate diagnosis while minimizing radiation exposure, making it a vital tool in resource-limited and high-demand settings.

关键词： Computed tomography Image classificiation COVID-19 diagnosis Sparse-view sinogram masked autoencoder

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Self-supervised learning with self-distillation on COVID-19 medical image classification

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024年 243卷 107876-107876页

作者： Tan, Zhiyong Yu, Yuhai Meng, Jiana Liu, Shuang Li, Wei Dalian Minzu Univ Sch Comp Sci & Engn Dalian 116600 Liaoning Peoples R China

Background and objective: Currently, COVID-19 is a highly infectious disease that can be clinically diagnosed based on diagnostic radiology. Deep learning is capable of mining the rich information implied in inpatient imaging data and accomplishing the classification of different stages of the disease process. However, a large amount of training data is essential to train an excellent deep-learning model. Unfortunately, due to factors such as privacy and labeling difficulties, annotated data for COVID-19 is extremely scarce, which encourages us to propose a more effective deep learning model that can effectively assist specialist physicians in COVID-19 diagnosis. Methods: In this study,we introduce masked autoencoder (MAE) for pre-training and fine-tuning directly on small-scale target datasets. Based on this, we propose Self-Supervised Learning with Self-Distillation on COVID19 medical image classification (SSSD-COVID). In addition to the reconstruction loss computation on the masked image patches, SSSD-COVID further performs self-distillation loss calculations on the latent representation of the encoder and decoder outputs. The additional loss calculation can transfer the knowledge from the global attention of the decoder to the encoder which acquires only local attention. Results: Our model achieves 97.78 % recognition accuracy on the SARS-COV-CT dataset containing 2481 images and is further validated on the COVID-CT dataset containing 746 images, which achieves 81.76 % recognition accuracy. Further introduction of external knowledge resulted in experimental accuracies of 99.6% and 95.27 % on these two datasets, respectively. Conclusions: SSSD-COVID can obtain good results on the target dataset alone, and when external information is introduced, the performance of the model can be further improved to significantly outperform other models. Overall, the experimental results show that our method can effectively mine COVID-19 features from rare data and can assist pr

关键词： COVID-19 masked autoencoder Self-supervised learning Chest CT Self-distillation

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masked Graph Convolutional Network for Small Sample Classification of Hyperspectral Images

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REMOTE SENSING 2023年第7期15卷 1869-1869页

作者： Liu, Wenkai Liu, Bing He, Peipei Hu, Qingfeng Gao, Kuiliang Li, Hui North China Univ Water Resources & Elect Power Coll Surveying & Geoinformat Zhengzhou 450046 Peoples R China PLA Strateg Support Force Informat Engn Univ Inst Geospatial Informat Zhengzhou 450002 Peoples R China

The deep learning method has achieved great success in hyperspectral image classification, but the lack of labeled training samples still restricts the development and application of deep learning methods. In order to deal with the problem of small samples in hyperspectral image classification, a novel small sample classification method based on rotation-invariant uniform local binary pattern (RULBP) features and a graph-based masked autoencoder is proposed in this paper. Firstly, the RULBP features of hyperspectral images are extracted, and then the k-nearest neighbor method is utilized to construct the graph. Furthermore, self-supervised learning is conducted on the constructed graph so that the model can learn to extract features more suitable for small sample classification. Since the self-supervised training mainly adopts the masked autoencoder method, only unlabeled samples are needed to complete the training. After training, only a small number of samples are used to fine-tune the graph convolutional network, so as to complete the classification of all nodes in the graph. A large number of classification experiments on three commonly used hyperspectral image datasets show that the proposed method could achieve higher classification accuracy with fewer labeled samples.

关键词： hyperspectral image classification graph convolutional network masked autoencoder rotation-invariant uniform local binary pattern

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DeepVATS: Deep Visual Analytics for Time Series

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KNOWLEDGE-BASED SYSTEMS 2023年第1期277卷

作者： Rodriguez-Fernandez, Victor Montalvo-Garcia, David Piccialli, Francesco Nalepa, Grzegorz J. Camacho, David Univ Politecn Madrid Sch Comp Syst Engn Calle Alan Turing Madrid 28038 Spain Univ Politecn Madrid Escuela Tecn Super Ingn Telecomunicac Ave Complutense 30 Madrid 28040 Spain Univ Naples Federico II Dept Math & Applicat R Caccioppoli Naples Italy Jagiellonian Univ Inst Appl Comp Sci Jagiellonian Human Ctr Artificial Intelligence La PL-30348 Krakow Poland

The field of Deep Visual Analytics (DVA) has recently arisen from the idea of developing Visual Interactive Systems supported by deep learning, in order to provide them with large-scale data processing capabilities and to unify their implementation across different data and domains. In this paper we present DeepVATS, an open-source tool that brings the field of DVA into time series data. DeepVATS trains, in a self-supervised way, a masked time series autoencoder that reconstructs patches of a time series, and projects the knowledge contained in the embeddings of that model in an interactive plot, from which time series patterns and anomalies emerge and can be easily spotted. The tool includes a back-end for data processing pipeline and model training, as well as a front-end with an interactive user interface. We report on results that validate the utility of DeepVATS, running experiments on both synthetic and real datasets. The code is publicly available on https: //***/vrodriguezf/deepvats.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Deep learning Visual analytics Time series masked autoencoder

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Pose Mask: A Model-Based Augmentation Method for 2D Pose Estimation in Classroom Scenes Using Surveillance Images

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SENSORS 2022年第21期22卷 8331-8331页

作者： Liu, Shichang Ma, Miao Li, Haiyang Ning, Hanyang Wang, Min Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Minist Educ Key Lab Modern Teaching Technol Xian 710062 Peoples R China Natl Engn Lab Integrated Aero Space Ground Ocean Xian 710072 Peoples R China

Solid developments have been seen in deep-learning-based pose estimation, but few works have explored performance in dense crowds, such as a classroom scene;furthermore, no specific knowledge is considered in the design of image augmentation for pose estimation. A masked autoencoder was shown to have a non-negligible capability in image reconstruction, where the masking mechanism that randomly drops patches forces the model to build unknown pixels from known pixels. Inspired by this self-supervised learning method, where the restoration of the feature loss induced by the mask is consistent with tackling the occlusion problem in classroom scenarios, we discovered that the transfer performance of the pre-trained weights could be used as a model-based augmentation to overcome the intractable occlusion in classroom pose estimation. In this study, we proposed a top-down pose estimation method that utilized the natural reconstruction capability of missing information of the MAE as an effective occluded image augmentation in a pose estimation task. The difference with the original MAE was that instead of using a 75% random mask ratio, we regarded the keypoint distribution probabilistic heatmap as a reference for masking, which we named Pose Mask. To test the performance of our method in heavily occluded classroom scenes, we collected a new dataset for pose estimation in classroom scenes named Class Pose and conducted many experiments, the results of which showed promising performance.

关键词： pose estimation masked autoencoder model-based augmentation classroom scenes

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基于蛋白质组学数据的小样本食管癌诊断方法

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智能计算机与应用 2024年第12期14卷 145-150页

作者：韦少钗杨晓帆洪程铭李舜音广东潮州卫生健康职业学院广东潮州521000

基于蛋白质组学数据的食管癌分析与诊断面临样本规模小、序列长度大等问题,影响分析方法的泛化性和准确性。针对该问题,本文提出一种面向小样本学习的食管癌诊断方法。该方法在Transformer的基础上,首先为其引入局部窗口机制,以缓解序... 详细信息

基于蛋白质组学数据的食管癌分析与诊断面临样本规模小、序列长度大等问题,影响分析方法的泛化性和准确性。针对该问题,本文提出一种面向小样本学习的食管癌诊断方法。该方法在Transformer的基础上,首先为其引入局部窗口机制,以缓解序列长度过大引起的组合爆炸,并利用特征洗牌操作增大窗口感受野;再借鉴masked autoencoder思想,设计非对称的特征编码器和解码器,同时在解码器中加入分类任务,提升模型所学习的特征质量;最后通过编码器创建食管癌和癌旁非肿瘤组织的原型表示,实现小样本分类。实验结果表明,所提方法在新数据集中仅需学习6对配对样本,占总样本的10%,即可获得93.45%和95.19%的精确率和召回率,可为食管癌的智能化诊断提供思路。

关键词：食管癌小样本蛋白质组学数据 Transformer masked autoencoder

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Text-augmented long-term relation dependency learning for knowledge graph representation

High-Confidence Computing

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High-Confidence Computing 2025年

作者： Quntao Zhu Mengfan Li Yuanjun Gao Yao Wan Xuanhua Shi Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

Knowledge graph (KG) representation learning aims to map entities and relations into a low-dimensional representation space, showing significant potential in many tasks. Existing approaches follow two categories: (1) Graph-based approaches encode KG elements into vectors using structural score functions. (2) Text-based approaches embed text descriptions of entities and relations via pre-trained language models (PLMs), further fine-tuned with triples. We argue that graph-based approaches struggle with sparse data, while text-based approaches face challenges with complex relations. To address these limitations, we propose a unified Text-Augmented Attention-based Recurrent Network, bridging the gap between graph and natural language. Specifically, we employ a graph attention network based on local influence weights to model local structural information and utilize a PLM based prompt learning to learn textual information, enhanced by a mask-reconstruction strategy based on global influence weights and textual contrastive learning for improved robustness and generalizability. Besides, to effectively model multi-hop relations, we propose a novel semantic-depth guided path extraction algorithm and integrate cross-attention layers into recurrent neural networks to facilitate learning the long-term relation dependency and offer an adaptive attention mechanism for varied-length information. Extensive experiments demonstrate that our model exhibits superiority over existing models across KG completion and question-answering tasks.

关键词： Knowledge graph representation Graph attention network Pre-trained language model Attention-based recurrent network masked autoencoder Contrastive learning

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