检索结果-内蒙古大学图书馆

ClusVPR: Efficient Visual Place recognition with Clustering-based Weighted Transformer

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Xu, Yifan Shamsolmoali, Pourya Yang, Jie The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China The School of Communication and Electrical Engineering East China Normal University Shanghai China

Visual place recognition (VPR) is a highly challenging task that has a wide range of applications, including robot navigation and self-driving vehicles. VPR is particularly difficult due to the presence of duplicate regions and the lack of attention to small objects in complex scenes, resulting in recognition deviations. In this paper, we present ClusVPR, a novel approach that tackles the specific issues of redundant information in duplicate regions and representations of small objects. Different from existing methods that rely on Convolutional Neural Networks (CNNs) for feature map generation, ClusVPR introduces a unique paradigm called Clustering-based Weighted Transformer Network (CWTNet). CWTNet leverages the power of clustering-based weighted feature maps and integrates global dependencies to effectively address visual deviations encountered in large-scale VPR problems. We also introduce the optimized-VLAD (OptLAD) layer that significantly reduces the number of parameters and enhances model efficiency. This layer is specifically designed to aggregate the information obtained from scale-wise image patches. Additionally, our pyramid selfsupervised strategy focuses on extracting representative and diverse information from scale-wise image patches instead of entire images, which is crucial for capturing representative and diverse information in VPR. Extensive experiments on four VPR datasets show our model’s superior performance compared to existing models while being less complex. © 2023, CC BY-SA.

关键词： Convolutional neural networks

Consensus-Based Distributed Kernel One-class Support Vector Machine for Anomaly Detection

学校读者我要写书评

暂无评论

Consensus-Based Distributed Kernel One-class Support Vector ...

International Joint Conference on Neural Networks (IJCNN)

作者： Tianyao Wang Fan He Ruikai Yang Zhixing Ye Xiaolin Huang Department of Automation Ningbo Artificial Intelligence Institute Shanghai Jiao Tong University China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

One-class support vector machine (OCSVM) is one of the most widely used methods for learning from imbalanced data and has been successfully applied to numerous tasks such as anomaly detection. However, the study on decentralized OCSVM is currently limited to linear cases. The main challenge is how to communicate the non-parametric and local-data-dependent decision functions between neighboring nodes. To tackle it, this paper proposes a projection consensus constraint to formulate a decentralized OCSVM, where local solutions are assumed to be the projection of the global optimum on local reproducing kernel Hilbert spaces. A fast non-parametric solving algorithm is then designed based on alternating direction method of multipliers. Experiments on real-world anomaly datasets indicate that our method outperforms the existing distributed OCSVM methods while reducing the communication cost.

关键词：

Distilling Object Detectors with Global Knowledge

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Tang, Sanli Zhang, Zhongyu Cheng, Zhanzhan Lu, Jing Xu, Yunlu Niu, Yi He, Fan Hikvision Research Institute Hangzhou China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

Knowledge distillation learns a lightweight student model that mimics a cumbersome teacher. Existing methods regard the knowledge as the feature of each instance or their relations, which is the instance-level knowledge only from the teacher model, i.e., the local knowledge. However, the empirical studies show that the local knowledge is much noisy in object detection tasks, especially on the blurred, occluded, or small instances. Thus, a more intrinsic approach is to measure the representations of instances w.r.t. a group of common basis vectors in the two feature spaces of the teacher and the student detectors, i.e., global knowledge. Then, the distilling algorithm can be applied as space alignment. To this end, a novel prototype generation module (PGM) is proposed to find the common basis vectors, dubbed prototypes, in the two feature spaces. Then, a robust distilling module (RDM) is applied to construct the global knowledge based on the prototypes and filtrate noisy local knowledge by measuring the discrepancy of the representations in two feature spaces. Experiments with Faster-RCNN and RetinaNet on PASCAL and COCO datasets show that our method achieves the best performance for distilling object detectors with various backbones, which even surpasses the performance of the teacher model. We also show that the existing methods can be easily combined with global knowledge and obtain further improvement. © 2022, CC BY-NC-ND.

关键词： Distillation

Iteratively Refine the Segmentation of Head and Neck Tumor in FDG-PET and CT images 1

学校读者我要写书评

暂无评论

1st 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with 23rd International Conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Chen, Huai Chen, Haibin Wang, Lisheng Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Perception Vision Medical Technology Guangzhou China

ISBN: (数字)9783030671945

ISBN: (纸本)9783030671938

The automatic segmentation of head and neck (H&N) tumor from FDG-PET and CT images is urgently needed for radiomics. In this paper, we propose a framework to segment H&N tumor automatically by fusing information of PET and CT. In this framework, multiple 3D-Unets are trained one-by-one. The predictions and features of upstream models will be captured as additional information for the next one to further refine the segmentation. Experiments show that iterative refinements can improve the performance. We evaluated our framework on the dataset of HECKTOR2020 (The challenge of head and neck tumor segmentation) and won the 5th place with average DSC, precision and recall of 0.7241, 0.8479, 0.6701 respectively. © 2021, Springer Nature Switzerland AG.

关键词： Computerized tomography

Semantic Transformation-Based Data Augmentation for Few-Shot Learning

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Pan, Mei-Hong Xin, Hong-Yi Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiaotong University Shanghai200240 China

Few-shot learning (FSL) as a data-scarce method, aims to recognize instances of unseen classes solely based on very few examples. However, the model can easily become overfitted due to the biased distribution formed with extremely limited training data. This paper presents a task specific data augmentation approach by transferring samples from base dataset to the novel tasks in an encoder-decoder paradigm, which guarantees generating semantically meaningful features. Specifically, the feature transfer process is carried out in semantic space. We further impose a compactness constraint to the generated features with the prototypes working as the reference points, which ensures the generated features distribute around the class centers. Moreover, we incorporate the cluster centers of the query set with the prototypes of the support set to reduce the bias of the class centers. With the supervision of the compactness loss, the model is encouraged to generate discriminative features with high inter-class dispersion and intra-class compactness. Extensive experiments show that our method outperforms the state-of-the-arts on four benchmarks, namely MiniimageNet, TieredimageNet, CUB and CIFAR-FS. © 2023, The Authors. All rights reserved.

关键词： Motion planning

Measurement of Imaging Indexes of Distal Radius based on deep Learning Key Point Detection 23

学校读者我要写书评

暂无评论

Measurement of Imaging Indexes of Distal Radius based on dee...

9th International Conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Jiachao, Niu Xin, Zhang Xufeng, Ling Yang, Jie Yin, Yong Shanghai Normal University Tianhua College AI School No. 1661 North Sheng Xin Road China Department of Orthopedics Jiading District Central Hospital Affiliated Shanghai University of Medicine &Health Sciences No.1 Chengbei Road China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University 800 Dongchuan Road 200240 China

ISBN: (纸本)9781450399029

The measurement of imaging indexes of distal radius is the basic work of diagnosis and recovery evaluation. Due to the problems of large morphological differences and insufficient clarity in radial images, the accurate measurement of indexes relies heavily on the clinical experience of doctors, with defects such as low accuracy and poor consistency. In order to solve the above problems, this paper proposes an intelligent technology to achieve accurate measurement of imaging indicators of the distal radius. Firstly, 12 key points needed for distal imaging indexes were determined: radius left vertex, radius right vertex, radius left neck point, radius right neck point, radius left lower point, radius right lower point, ulna left vertex, ulna right vertex, ulna left neck point, right neck point, ulna left lower point, ulna right lower point. Then, the deep convolutional neural network was used to estimate the geometric transformation features and regress the thermal map of the feature points, so as to achieve accurate positioning of the 12 feature points. Finally, each imaging index was calculated accurately by the preset computational method. The experimental results show that the proposed measurement method has advantages of high accuracy, good consistency and fast speed. The test accuracy is higher than the manual measurement results of inexperienced doctors, and the network model has good robustness and generalization ability. This method has practical value and can be applied in the field of medical imaging to help doctors improve the efficiency of diagnosis. © 2023 Copyright held by the owner/author(s).

关键词： Diagnosis

3D Vessel Segmentation with Limited Guidance of 2D Structure-agnostic Vessel Annotations

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Chen, Huai Wang, Xiuying Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science The University of Sydney SydneyNSW2006 Australia

Delineating 3D blood vessels is essential for clinical diagnosis and treatment, however, is challenging due to complex structure variations and varied imaging conditions. Supervised deep learning has demonstrated its superior capacity in automatic 3D vessel segmentation. However, the reliance on expensive 3D manual annotations and limited capacity for annotation reuse hinder the clinical applications of supervised models. To avoid the repetitive and laborious annotating process and make full use of existing vascular annotations, this paper proposes a novel 3D shape-guided local discrimination (3D-SLD) model for 3D vascular segmentation under limited guidance from public 2D vessel annotations. The primary hypothesis is that 3D vessels are composed of semantically similar voxels and often exhibit tree-shaped morphology. Accordingly, the 3D region discrimination loss is firstly proposed to learn the discriminative representation measuring voxel-wise similarities and cluster semantically consistent voxels to form the candidate 3D vascular segmentation in unlabeled images;secondly, based on the similarity of the tree-shaped morphology between 2D and 3D vessels, the Crop- and-Overlap strategy is presented to generate reference masks from the existing 2D structure-agnostic vessel annotations, which are fit for varied vascular structures, and the adversarial loss is introduced to guide the 3D vessels with the tree-shaped morphology;and thirdly, the temporal consistency loss is proposed to foster the training stability and keep the model updated smoothly. To further enhance the model’s robustness and reliability, the orientation-invariant CNN module and Reliability-Refinement algorithm are presented. Experimental results from the public 3D cerebrovascular and 3D arterial tree datasets demonstrate that our model achieves comparable effectiveness against nine deep learning supervised models. Copyright © 2023, The Authors. All rights reserved.

关键词： Morphology

Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical image Segmentation

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Yang, Yuncheng Wei, Meng He, Junjun Yang, Jie Ye, Jin Gu, Yun Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Shanghai AI Lab Shanghai China

Transfer learning is a critical technique in training deep neural networks for the challenging medical image segmentation task that requires enormous resources. With the abundance of medical image data, many research institutions release models trained on various datasets that can form a huge pool of candidate source models to choose from. Hence, it’s vital to estimate the source models’ transferability (i.e., the ability to generalize across different downstream tasks) for proper and efficient model reuse. To make up for its deficiency when applying transfer learning to medical image segmentation, in this paper, we therefore propose a new Transferability Estimation (TE) method. We first analyze the drawbacks of using the existing TE algorithms for medical image segmentation and then design a source-free TE framework that considers both class consistency and feature variety for better estimation. Extensive experiments show that our method surpasses all current algorithms for transferability estimation in medical image segmentation. Code is available at here. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

Data Imputation by Pursuing Better Classification: A Supervised Kernel-Based Method

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Yang, Ruikai He, Fan He, Mingzhen Wang, Kaijie Huang, Xiaolin Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University 800 Dongchuan RD Shanghai200240 China STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Oude Markt 13 Leuven3000 Belgium

Data imputation, the process of filling in missing feature elements for incomplete data sets, plays a crucial role in data-driven learning. A fundamental belief is that data imputation is helpful for learning performance, and it follows that the pursuit of better classification can guide the data imputation process. While some works consider using label information to assist in this task, their simplistic utilization of labels lacks flexibility and may rely on strict assumptions. In this paper, we propose a new framework that effectively leverages supervision information to complete missing data in a manner conducive to classification. Specifically, this framework operates in two stages. Firstly, it leverages labels to supervise the optimization of similarity relationships among data, represented by the kernel matrix, with the goal of enhancing classification accuracy. To mitigate overfitting that may occur during this process, a perturbation variable is introduced to improve the robustness of the framework. Secondly, the learned kernel matrix serves as additional supervision information to guide data imputation through regression, utilizing the block coordinate descent method. The superiority of the proposed method is evaluated on four real-world data sets by comparing it with state-of-the-art imputation methods. Remarkably, our algorithm significantly outperforms other methods when the data is missing more than 60% of the features. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning