Median Stable Clustering and Global Distance Classification for Cross-Domain Person Re-Identification

Pang, Zhiqi; Guo, Jifeng; Ma, Zhiqiang; Sun, Wenbo; Xiao, Yanbang

doi:10.1109/tcsvt.2021.3103753

Cited by 22 publications

(6 citation statements)

References 51 publications

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“…An unsupervised cross-domain ReID method based on median stable clustering (MSC) and global distance classification (GDC) is proposed [ 22 ]. The MSC method uses a measurement method that considers the similarity between clusters, the number of samples in a cluster, and the combined similarity within a cluster.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Robust adversarial uncertainty quantification for deep learning fine-tuning

Ahmed

Lin

2023

J Supercomput

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This paper proposes a deep learning model that is robust and capable of handling highly uncertain inputs. The model is divided into three phases: creating a dataset, creating a neural network based on the dataset, and retraining the neural network to handle unpredictable inputs. The model utilizes entropy values and a non-dominant sorting algorithm to identify the candidate with the highest entropy value from the dataset. This is followed by merging the training set with adversarial samples, where a mini-batch of the merged dataset is used to update the dense network parameters. This method can improve the performance of machine learning models, categorization of radiographic images, risk of misdiagnosis in medical imaging, and accuracy of medical diagnoses. To evaluate the efficacy of the proposed model, two datasets, MNIST and COVID, were used with pixel values and without transfer learning. The results showed an increase of accuracy from 0.85 to 0.88 for MNIST and from 0.83 to 0.85 for COVID, which suggests that the model successfully classified images from both datasets without using transfer learning techniques.

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Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Robust adversarial uncertainty quantification for deep learning fine-tuning

Ahmed

Lin

2023

J Supercomput

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“…As time went by, some algorithms have been improved to achieve specific tasks. Pang et al [ 21 , 22 ] proposed an unsupervised cross-domain ReID method based on median stable clustering (MSC) and global distance classification (GDC) to improve the performance of cross-domain person reidentification (ReID). Patel et al [ 23 ] proposed the Dimension-Based Generic Convolution Block (DBGC), which can be used with any CNN to make the architecture generic and provide a dimensionwise selection of various height, width, and depth kernels.…”

Section: Related Workmentioning

confidence: 99%

Multiscale Traffic Sign Detection Method in Complex Environment Based on YOLOv4

Wang

Bai

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Traffic sign detection is a challenging problem in the field of unmanned driving, particularly important in complex environments. We propose a method, based on the improved You only look once (YOLO) v4, to detect and recognize multiscale traffic signs in complex environments. This method employs an image preprocessing module that can classify and denoize images of complex environments and then input the images into the improved YOLOv4. We also design an improved feature pyramid structure to replace the original feature pyramid of YOLOv4. This structure uses an adaptive feature fusion module and a multiscale feature transfer mechanism to reduce putative information loss in the feature map generation process and improve the information transfer between deep and shallow features, enhancing the representation ability of feature pyramids. Finally, we use EIOU LOSS and Cluster-NMS to further improve the model performance. The experimental results on the fusion of Tsinghua-Tencent 100 K and our collected dataset show that the proposed method achieves an mAP of 81.78%. Compared to existing methods, our method demonstrates its superiority with regard to traffic sign detection.

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“…With the development of modern medical technology and artificial intelligence [4], machine learning-based methods for analyzing and studying brain magnetic resonance imaging (MRI) of patients with ASD have achieved excellent results [5]. A study by Dekhil et al [6] converted the time series to power spectral density for 34 independent sets of components to analyze spatial graphs and used sparse autoencoders to reduce the input dimensionality for input into a support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

Multimodal Autism Spectrum Disorder Diagnosis Method Based on DeepGCN

Wang,

Guo,

Wang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Multimodal data play an important role in the diagnosis of brain diseases. This study constructs a whole-brain functional connectivity network based on functional MRI data, uses non-imaging data with demographic information to complement the classification task for diagnosing subjects, and proposes a multimodal and across-site WL-DeepGCN-based method for classification to diagnose autism spectrum disorder (ASD). This method is used to resolve the existing problem that deep learning ASD identification cannot efficiently utilize multimodal data. In the WL-DeepGCN, a weight-learning network is used to represent the similarity of non-imaging data in the latent space, introducing a new approach for constructing population graph edge weights, and we find that it is beneficial and robust to define pairwise associations in the latent space rather than the input space. We propose a graph convolutional neural network residual connectivity approach to reduce the information loss due to convolution operations by introducing residual units to avoid gradient disappearance and gradient explosion. Furthermore, an EdgeDrop strategy makes the node connections sparser by randomly dropping edges in the raw graph, and its introduction can alleviate the overfitting and oversmoothing problems in the DeepGCN training process. We compare the WL-DeepGCN model with competitive models based on the same topics and nested 10-fold cross-validation show that our method achieves 77.27% accuracy and 0.83 AUC for ASD identification, bringing substantial performance gains.

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Median Stable Clustering and Global Distance Classification for Cross-Domain Person Re-Identification

Cited by 22 publications

References 51 publications

RETRACTED ARTICLE: Robust adversarial uncertainty quantification for deep learning fine-tuning

RETRACTED ARTICLE: Robust adversarial uncertainty quantification for deep learning fine-tuning

Multiscale Traffic Sign Detection Method in Complex Environment Based on YOLOv4

Multimodal Autism Spectrum Disorder Diagnosis Method Based on DeepGCN

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