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A Self-Supervised Learning Network for Student Engagement Recognition From Facial Expressions

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第12期34卷 12399-12410页

作者： Zhang, Wen-Long Jia, Rui-Sheng Wang, Hu Che, Cheng-Yue Sun, Hong-Mei Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

Student engagement in online learning is an important indicator for measuring learning effectiveness. Due to the fact that facial video data of students during online learning contains a wider range of information such as time, current research has begun to focus on obtaining student engagement from video data. These studies primarily rely on supervised learning methods and have achieved certain success. However, the longstanding lack of large-scale and high-quality labeled data, as well as the time-consuming and laborious sample labeling work, have to some extent hindered their further improvement. To solve this problem, this paper proposes a self-supervised learning method, Facial masked autoencoder (FMAE), which is used to construct a student engagement recognition model. This method uses a masked autoencoder to process a large number of unlabeled facial videos, and performs self-supervised pre-training by learning masked facial features from the reconstruction process. In order to promote the encoder to better mask learning for the face, a new facial mask strategy and reconstruction module have been proposed. With this method, the model can not only focus on important facial regions, but also obtain more accurate appearance features and spatio-temporal details. Experiments have demonstrated that the proposed method achieves excellent results on DAiSEE and EmotiW datasets, showing its potential in the task of student engagement recognition.

关键词： Face recognition Feature extraction Task analysis Self-supervised learning Facial features Supervised learning Education Online learning student engagement self-supervised learning masked autoencoder

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Deep Closing: Enhancing Topological Connectivity in Medical Tubular Segmentation

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2024年第11期43卷 3990-4003页

作者： Wu, Qian Chen, Yufei Liu, Wei Yue, Xiaodong Zhuang, Xiahai Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Shanghai Univ Artificial Intelligence Inst Shanghai 200444 Peoples R China Fudan Univ Sch Data Sci Shanghai 200433 Peoples R China

Accurately segmenting tubular structures, such as blood vessels or nerves, holds significant clinical implications across various medical applications. However, existing methods often exhibit limitations in achieving satisfactory topological performance, particularly in terms of preserving connectivity. To address this challenge, we propose a novel deep-learning approach, termed Deep Closing, inspired by the well-established classic closing operation. Deep Closing first leverages an autoencoder trained in the masked Image Modeling (MIM) paradigm, enhanced with digital topology knowledge, to effectively learn the inherent shape prior of tubular structures and indicate potential disconnected regions. Subsequently, a Simple Components Erosion module is employed to generate topology-focused outcomes, which refines the preceding segmentation results, ensuring all the generated regions are topologically significant. To evaluate the efficacy of Deep Closing, we conduct comprehensive experiments on 4 datasets: DRIVE, CHASE_DB1, DCA1, and CREMI. The results demonstrate that our approach yields considerable improvements in topological performance compared with existing methods. Furthermore, Deep Closing exhibits the ability to generalize and transfer knowledge from external datasets, showcasing its robustness and adaptability. The code for this paper has been available at: https://***/5k5000/DeepClosing.

关键词： Topology Image segmentation Erosion Biomedical imaging Shape Optimization Image reconstruction Tubular structures topology preserving masked autoencoder deep closing

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Snowed autoencoders are efficient snow removers

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COMPUTERS & GRAPHICS-UK 2023年第1期114卷 73-85页

作者： Liu, Yifan Chen, Jincai Lu, Ping Zhu, Chuanbo Jian, Yugen Sun, Chao Liang, Han Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Engn Res Ctr Data Storage Syst & Technol Key Lab Informat Storage Syst Wuhan 430074 Peoples R China

Snow is a harsh natural phenomenon that greatly affects the performance of advanced computer vision tasks. Recently, most image desnowing methods rely on complex model structures, leading to increased carbon emissions and impossibilities to deploy on lightweight computing devices. Additionally, most deep learning-based methods do not effectively utilize the position information of snow particles, which will limit the performance of the model. To address these issues, considering that snow particles have block-shaped shape distribution and color uniformity similar to a mask, we propose a snowed autoencoder (SAE) desnowing method. Specifically, the SAE is composed of four parts: snowed masking process, SAE encoder, SAE decoder, and prediction stage. First, a snowy image is passed through the snowed masking process to generate a mask that exactly covers the snow particles and output the coordinates of the four vertices of the mask and the index of each segmented image patch. The input image and each mask coordinate and index are passed through the SAE encoder based on the snow particle attention module to generate an image representation for identifying typical features of snow particles. Then, the generated typical features of snow particles are fed into the SAE decoder to reconstruct image patches without snow. Finally, all the patches will be passed through the prediction stage to reconstruct the original size of the snow-free image. A large number of experiments show that the proposed SAE desnowing method achieves the state-of-the-art desnowing performance on three synthetic and one real-world desnowing datasets. The images after desnowing by SAE have better color details and are more consistent with human visual habits. In addition, SAE has faster desnowing speed and fewer parameters. The code is available at https://***/awhitewhale/SAE. & COPY;2023 Elsevier Ltd. All rights reserved.

关键词： Single image snow removal masked autoencoder Vision transformer

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Research on image classification of power inspection using less sample learning technique

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 2119-2126页

作者： Li, Qiang Zhuang, Li Wang, Qiulin Song, Lihua Wang, Yanrong State Grid Informat & Commun Ind Grp Co LTD Beijing 102211 Peoples R China Fujian Yirong Informat Technol Co LTD Fuzhou 350001 Peoples R China

This study proposes a few-sample self-supervised power inspection image classification method based on Vision Transformer and masked autoencoders (MAE). Initially, an MAE is employed to pretrain an extensive collection of unlabeled power inspection images, leveraging the masking technique to encourage the model to acquire an understanding of the overarching features within these images. Then, the pretrained encoder part is combined with the visual transformer to further extract image features. For the subsequent classification task, the encoder's pretrained weights are frozen and subsequently incorporated into a novel classification network. By fine-tuning this network with a limited amount of labeled data, experimental findings indicate a substantial enhancement in the classification accuracy of power inspection images, particularly in scenarios with limited sample sizes. By introducing visual transformers and MAE, this article provides an efficient and reliable solution for intelligent classification of power inspection images, which has important practical application value and broad market prospects.

关键词： power inspection visual transformer masked autoencoder few samples self-supervision

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A Step-Wise Domain Adaptation Detection Transformer for Object Detection under Poor Visibility Conditions

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REMOTE SENSING 2024年第15期16卷 2722页

作者： Zhang, Gege Wang, Luping Chen, Zengping Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen 518107 Peoples R China

To address the performance degradation of cross-domain object detection under various illumination conditions and adverse weather scenarios, this paper introduces a novel method a called Step-wise Domain Adaptation DEtection TRansformer (SDA-DETR). Our approach decomposes the adaptation process into three sequential steps, progressively transferring knowledge from a labeled dataset to an unlabeled one using the DETR (DEtection TRansformer) architecture. Each step precisely reduces domain discrepancy, thereby facilitating effective transfer learning. In the initial step, a target-like domain is constructed as an auxiliary to the source domain to reduce the domain gap at the image level. Then, we adaptively align the source domain and target domain features at both global and local levels. To further mitigate model bias towards the source domain, we develop a token-masked autoencoder (t-MAE) to enhance target domain features at the semantic level. Comprehensive experiments demonstrate that the SDA-DETR outperforms several popular cross-domain object detection methods on three challenging public driving datasets.

关键词： domain adaptation detection transformer target-like domain masked autoencoder

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scDMAE: A Generative Denoising Model Adopted Mask Strategy for scRNA-Seq Data Recovery

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第6期28卷 3772-3780页

作者： Liu, Wei Pan, Youze Teng, Zhijie Xu, Junlin Xiangtan Univ Sch Comp Sci Xiangtan 411105 Peoples R China Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430065 Peoples R China

The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized gene expression studies at the single-cell level. However, the presence of technical noise and data sparsity in scRNA-seq often undermines the accuracy of subsequent analyses. Existing methods for denoising and imputing scRNA-seq data often rely on stringent assumptions about data distribution, limiting the effectiveness of data recovery. In this study, we propose the scDMAE model for denoising and recovery of scRNA-seq data. First, the model fuses gene expression features and topological features to discern the primary expression patterns of genes in cells. Then, an autoencoder with a masking strategy is used to model dropout events and separate potential noise in the data. Finally, the model incorporates the original raw data to recover the true biological expression value. By conducting experiments on various types of scRNA-Seq datasets, scDMAE demonstrates superior performance compared to other comparative methods based on six distinct evaluation metrics in downstream analysis. The scDMAE method can accurately cluster similar cell populations, identify differential genes and infer cell trajectories.

关键词： Single-cell RNA sequencing data masked autoencoder denoising data recovery

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Privacy-Preserving Split Learning for Large-Scaled Vision Pre-Training

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2023年 18卷 1539-1553页

作者： Wang, Zhousheng Yang, Geng Dai, Hua Rong, Chunming Nanjing Univ Posts & Telecommun Sch Telecommun & Informat Engn Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Peoples R China Jiangsu Key Lab Big data Secur & Intelligent Proc Nanjing 210023 Peoples R China Univ Stavanger Dept Elect Engn & Comp Sci N-4036 Stavanger Norway

The growing concerns about data privacy in society lead to restrictions on the computer vision research gradually. Several collaboration-based vision learning methods have recently emerged, e.g., federated learning and split learning. These methods protect user data from leaving local devices, and make training performed only by uploading gradients, parameters, or activations, etc. However, there is little research on collaborative learning based on state-of-the-art and large-scaled models, mainly due to the high computation or communication overheads of the latest models. Training these models may be still unrealized for users' terminals. In this paper, we make a first attempt at the sensitive image pre-training with large-scaled models in the collaborative learning scenario, and propose a new lightweight framework for split learning based on mask, masked Split Learning (MaskSL). We further ensure its security by differential privacy. Besides, we model the computation and communication overheads of several collaborative learning approaches by deduction to illustrate advantages of our scheme. Finally, we design and conduct a series of experiments on real-world datasets, e.g., in face recognition and medical image classification tasks, to demonstrate the performance of MaskSL.

关键词： Computational modeling Training Federated learning Privacy Data models Transformers Task analysis Split learning self pre-training differential privacy masked autoencoder

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Mixup Feature: A Pretext Task Self-Supervised Learning Method for Enhanced Visual Feature Learning

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IEEE ACCESS 2023年 11卷 82400-82409页

作者： Xu, Jiashu Stirenko, Sergii Natl Tech Univ Ukraine Comp Engn Dept Igor Sikorsky Kyiv Polytech Inst UA-03056 Kiev Ukraine

Self-supervised learning has emerged as an increasingly popular research topic within the field of computer vision. In this study, we propose a novel self-supervised learning approach based on Mixup features as pretext tasks. The proposed method aims to learn visual representations by predicting the MixupFeature of a masked image, which serves as a proxy for higher-level semantic information. Specifically, we investigate the efficacy of Mixup features as the prediction target for self-supervised learning. By setting the hyperparameter ? through Mixup operations, pairwise combinations of Sobel edge feature maps, HOG feature maps, and LBP feature maps are created. We employ the vision transformer as the backbone network, drawing inspiration from masked autoencoders (MAE). We evaluate the proposed method on three benchmark datasets, namely Cifar-10, Cifar-100, and STL-10, and compare it with other state-ofthe-art self-supervised learning approaches. The experiments demonstrate that mixed HOG-Sobel feature maps after Mixup achieve the best results in fine-tuning experiments on Cifar-10 and STL-10. Furthermore, compared to contrastive learning-based self-supervised learning methods, our approach proves to be more efficient, with shorter training durations and no reliance on data augmentation. When compared to generative self-supervised learning approaches based on MAE, the average performance improvement is 0.4%. Overall, the proposed self-supervised learning method based on Mixup features offers a promising direction for future research in the computer vision domain and has the potential to enhance performance across various downstream tasks. Our code will be published in GitHub.

关键词： Computer vision mixup feature self-supervised learning masked autoencoder

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Rethinking Polyp Segmentation from An Out-ofdistribution Perspective

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Machine Intelligence Research 2024年第4期21卷 631-639页

作者： Ge-Peng Ji Jing Zhang Dylan Campbell Huan Xiong Nick Barnes Australian National University Canberra 8105Australia Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi 999041UAE

Unlike existing fully-supervised approaches,we rethink colorectal polyp segmentation from an out-of-distribution perspective with a simple but effective self-supervised learning *** leverage the ability of masked autoencoders-self-supervised vision transformers trained on a reconstruction task-to learn in-distribution representations,here,the distribution of healthy colon *** then perform out-of-distribution reconstruction and inference,with feature space standardisation to align the latent distribution of the diverse abnormal samples with the statistics of the healthy *** generate per-pixel anomaly scores for each image by calculating the difference between the input and reconstructed images and use this signal for out-of-distribution(i.e.,polyp)*** results on six benchmarks show that our model has excellent segmentation performance and generalises across *** code is publicly available at https://***/GewelsJI/Polyp-OOD.

关键词： Polyp segmentation anomaly segmentation out-of-distribution segmentation masked autoencoder abdomen.

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masked Embedding Modeling With Rapid Domain Adjustment for Few-Shot Image Classification

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 4907-4920页

作者： Walsh, Reece Osman, Islam Shehata, Mohamed S. Univ British Columbia Dept Comp Sci Kelowna BC V6T 1Z4 Canada

In few-shot classification, performing well on a testing dataset is a challenging task due to the restricted amount of labelled data available and the unknown distribution. Many previously proposed techniques rely on prototypical representations of the support set in order to classify a query set. Although this approach works well with a large, in-domain support set, accuracy suffers when transitioning to an out-of-domain setting, especially when using small support sets. To address out-of-domain performance degradation with small support sets, we propose masked Embedding Modeling for Few-Shot Learning (MEM-FS), a novel, self-supervised, generative technique that reinforces few-shot-classification accuracy for a prototypical backbone model. MEM-FS leverages the data completion capabilities of a masked autoencoder to expand a given embedded support set. To further increase out-of-domain performance, we also introduce Rapid Domain Adjustment (RDA), a novel, self-supervised process for quickly conditioning MEM-FS to a new domain. We show that masked support embeddings generated by MEM-FS+RDA can significantly improve backbone performance on both out-of-domain and in-domain datasets. Our experiments demonstrate that applying the proposed technique to an inductive classifier achieves state-of-the-art performance on mini-imagenet, the CVPR L2ID Classification Challenge, and a newly proposed dataset, IKEA-FS. We provide code for this work at https://***/Brikwerk/MEM-FS

关键词： Few-shot classification self-supervised generative masked autoencoder

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