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arXiv 2024年

作者： Tang, Song Su, Wenxin Ye, Mao Zhang, Jianwei Zhu, Xiatian The Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China The TAMS Group Department of Informatics Universität Hamburg Hamburg Germany The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China The Surrey Institute for People-Centred Artificial Intelligence Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

In the pursuit of transferring a source model to a target domain without access to the source training data, Source-Free Domain Adaptation (SFDA) has been extensively explored across various scenarios, including closed-set, open-set, partial-set, and generalized settings. Existing methods, focusing on specific scenarios, not only address only a subset of challenges but also necessitate prior knowledge of the target domain, significantly limiting their practical utility and deployability. In light of these considerations, we introduce a more practical yet challenging problem, termed unified SFDA, which comprehensively incorporates all specific scenarios in a unified manner. To tackle this unified SFDA problem, we propose a novel approach called Latent Causal Factors Discovery (LCFD). In contrast to previous alternatives that emphasize learning the statistical description of reality, we formulate LCFD from a causality perspective. The objective is to uncover the causal relationships between latent variables and model decisions, enhancing the reliability and robustness of the learned model against domain shifts. To integrate extensive world knowledge, we leverage a pre-trained vision-language model such as CLIP. This aids in the formation and discovery of latent causal factors in the absence of supervision in the variation of distribution and semantics, coupled with a newly designed information bottleneck with theoretical guarantees. Extensive experiments demonstrate that LCFD can achieve new state-of-the-art results in distinct SFDA settings, as well as source-free out-of-distribution generalization. Our code and data are available at https://***/tntek/source-free-domain-adaptation. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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HPPS: A Hierarchical Progressive Perception System for Luggage Trolley Detection and Localization at Airports

arXiv

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arXiv 2024年

作者： Sun, Zhirui Zhang, Zhe Zhao, Jieting Ye, Hanjing Wang, Jiankun Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Shenzhen Key Laboratory of Robotics and Computer Vision Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

The robotic autonomous luggage trolley collection system employs robots to gather and transport scattered luggage trolleys at airports. However, existing methods for detecting and locating these luggage trolleys often fail when they are not fully visible. To address this, we introduce the Hierarchical Progressive Perception System (HPPS), which enhances the detection and localization of luggage trolleys under partial occlusion. The HPPS processes the luggage trolley’s position (x, y) and orientation (θ) separately, which requires only RGB images for labeling and training, eliminating the need for 3D coordinates and alignment. The HPPS can accurately determine the position of the luggage trolley with just one well-detected keypoint and estimate the luggage trolley’s orientation when it is partially occluded. Once the luggage trolley’s initial pose is detected, HPPS updates this information continuously to refine its accuracy until the robot begins grasping. The experiments on detection and localization demonstrate that HPPS is more reliable under partial occlusion compared to existing methods. Its effectiveness and robustness have also been confirmed through practical tests in actual luggage trolley collection tasks. A website about this work is available at HPPS. Copyright © 2024, The Authors. All rights reserved.

关键词： Robotics

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A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems

A Novel Duo-Stage driven Deep Neural Network Approach for Mi...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Kulwa, Frank Samuel, Oluwarotimi Williams Asogbon, Mojisola Grace Oyemakinde, Tolulope Tofunmi Obe, Olumide Olayinka Li, Guanglin Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems SIAT-CAS China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Guangdong Shenzhen518055 China University of Derby School of Computing and Engineering DerbyDE22 3AW United Kingdom Federal University of Technology Department of Computer Science Akure Nigeria

ISBN: (纸本)9781665493840

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario ( © 2023 IEEE.

关键词： Electrodes

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Fully automated grading of pituitary adenoma

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NeuroImage: Reports 2025年第1期5卷 100233-100233页

作者： Da Mutten, Raffaele Zanier, Olivier Bottini, Massimo Baumann, Yves Ciobanu-Caraus, Olga Regli, Luca Serra, Carlo Staartjes, Victor E. Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory Department of Neurosurgery Clinical Neuroscience Center University Hospital Zurich University of Zurich Zurich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland

Background: The Zurich Pituitary Score (ZPS) is an externally validated radiological grading scale to predict the likelihood of gross total resection (GTR) on coronal T1w magnetic resonance imaging of pituitary adenomas. The ZPS is based on the ratio of maximum tumor horizontal diameter and minimum intercarotid distance and on carotid artery encasement. While the interobserver agreement of the ZPS was relatively good, automated grading would be beneficial. Methods: A nnU-Net algorithm was trained to segment the manually labeled tumor tissue and the cavernous segment of the internal carotid artery. Subsequently, maximum horizontal tumor diameter and minimum intercarotid distance were extracted. Last, a seed-growing algorithm checked for encasement of the carotid to determine the ZPS. Results: 213 patients were included, of which 128 (60%) had non-functioning adenomas, 49 (23%) a growth-hormone secreting and 19 (9%) a prolactin producing tumor. Accordingly, ZPS gradings were I = 63 (30%), II = 94 (44%), III = 41 (19%) and IV = 15 (7%). Dice score (mean ± standard deviation) for the tumor, left carotid, and right carotid in training validation of 0.78 ± 0.24, 0.62 ± 0.31, 0.62 ± 0.30 and during holdout testing of 0.79 ± 0.24, 0.59 ± 0.32, 0.58 ± 0.33 was reached. After the exclusion of two cases with poor segmentation results, intraclass correlation coefficients [95% CI] for the intercarotid distance, maximum horizontal tumor diameter, and the ZPS ratio of the two measurements were 0.89 [0.80, 0.94], 0.91 [0.82, 0.96], 0.80 [0.66, 0.89] respectively. Cohen's weighted Kappa for the final ZPS grading was 0.79 [0.68, 0.90] and Spearman rank correlation was 0.83. Conclusions: We developed and internally validated a machine learning-based method for fully automated grading of the ZPS. Generally, robust segmentation performance was achieved. While ZPS grading generally worked well, human ratings remain superior in many situations. Especially for raters with low experience, o

关键词： Algorithms Artificial intelligence Deep learning machine learning Neurosurgery Pituitary

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View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network

View-decoupled Transformer for Person Re-identification unde...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Quan Zhang Lei Wang Vishal M. Patel Xiaohua Xie Jianhuang Lai School of Computer Science and Engineering Sun Yat-Sen University China Department of Electrical and Computer Engineering Johns Hopkins University USA Pazhou Lab (HuangPu) Guangdong China Guangdong Province Key Laboratory of Information Security Technology Guangdong China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing person re-identification methods have achieved remarkable advances in appearance-based identity association across homogeneous cameras, such as ground-ground matching. However, as a more practical scenario, aerial-ground person re-identification (AGPReID) among heterogeneous cameras has received minimal attention. To alleviate the disruption of discriminative identity representation by dramatic view discrepancy as the most significant challenge in AGPReID, the view-decoupled transformer (VDT) is proposed as a simple yet effective framework. Two major components are designed in VDT to decouple view-related and view-unrelated features, namely hierarchical subtractive separation and orthogonal loss, where the former separates these two features inside the VDT, and the latter constrains these two to be independent. In addition, we contribute a large-scale AGPReID dataset called CARGO, consisting of five/eight aerial/ground cameras, 5,000 identities, and 108,563 images. Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID, surpassing the previous method on mAP/Rank1 by up to 5.0%/2.7% on CARGO and 3.7%/5.2% on AG-ReID, keeping the same magnitude of computational complexity. Our project is available at https://***/LinlyAC/VDT-AGPReID.

关键词： computer vision Cameras Transformers Pattern recognition Computational complexity Identification of persons

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FAA-CLIP: Federated Adversarial Adaptation of CLIP

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IEEE Internet of Things Journal 2025年第12期12卷 21091-21102页

作者： Wu, Yihang Chaddad, Ahmad Desrosiers, Christian Daqqaq, Tareef Kateb, Reem Guilin University of Electronic Technology Artificial Intelligence for Personalised medicine School of Artificial Intelligence Guilin China Ecole de Technologie Superieure The Laboratory for Imagery Vision and Artificial Intelligence Montreal Canada Taibah University College of Medicine Al Madinah Saudi Arabia Ministry of National Guard health Affairs Prince Mohammed Bin Abdulaziz Hospital Al-Madinah Saudi Arabia Taibah University and College of Computer Science and Engineering Jeddah University College of Computer Science and Engineering Cyber Security Department Jeddah Saudi Arabia

Despite the remarkable performance of vision language models (VLMs) such as Contrastive Language Image Pre-training (CLIP), the large size of these models is a considerable obstacle to their use in federated learning (FL) systems where the parameters of local client models need to be transferred to a global server for aggregation. Another challenge in FL is the heterogeneity of data from different clients, which affects the generalization performance of the solution. In addition, natural pre-trained VLMs exhibit poor generalization ability in the medical datasets, suggests there exists a domain gap. To solve these issues, we introduce a novel method for the Federated Adversarial Adaptation (FAA) of CLIP. Our method, named FAA-CLIP, handles the large communication costs of CLIP using a light-weight feature adaptation module (FAM) for aggregation, effectively adapting this VLM to each client's data while greatly reducing the number of parameters to transfer. By keeping CLIP frozen and only updating the FAM parameters, our method is also computationally efficient. Unlike existing approaches, our FAA-CLIP method directly addresses the problem of domain shifts across clients via a domain adaptation (DA) module. This module employs a domain classifier to predict if a given sample is from the local client or the global server, allowing the model to learn domain-invariant representations. Extensive experiments on six different datasets containing both natural and medical images demonstrate that FAA-CLIP can generalize well on both natural and medical datasets compared to recent FL approaches. Our codes are available at https://***/AIPMLab/FAA-CLIP. © 2014 IEEE.

关键词： Federated learning

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Support-Query Prototype Fusion Network for Few-shot Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Wu, Xiaoxiao Gao, Zhenguo Chen, Xiaowei Wang, Yakai Qu, Shulei Li, Na Department of Computer Science and Technology Huaqiao University No.668 Jimei Avenue Fujian Xiamen361021 China Department of Mechanical Engineering and Automation Huaqiao University No.668 Jimei Avenue Fujian Xiamen361021 China Key Laboratory of Computer Vision and Machine Learning Fujian Provincial Universities Fujian Xiamen361021 China

In recent years, deep learning based on Convolutional Neural Networks (CNNs) has achieved remarkable success in many applications. However, their heavy reliance on extensive labeled data and limited generalization ability to unseen classes pose challenges to their suitability for medical image processing tasks. Few-shot learning, which utilizes a small amount of labeled data to generalize to unseen classes, has emerged as a critical research area, attracting substantial attention. Currently, most studies employ a prototype-based approach, in which prototypical networks are used to construct prototypes from the support set, guiding the processing of the query set to obtain the final results. While effective, this approach heavily relies on the support set while neglecting the query set, resulting in notable disparities within the model classes. To mitigate this drawback, we propose a novel Support-Query Prototype Fusion Network (SQPFNet). SQPFNet initially generates several support prototypes for the foreground areas of the support images, thus producing a coarse segmentation mask. Subsequently, a query prototype is constructed based on the coarse segmentation mask, additionally exploiting pattern information in the query set. Thus, SQPFNet constructs highquality support-query fused prototypes, upon which the query image is segmented to obtain the final refined query mask. Evaluation results on two public datasets, SABS and CMR, show that SQPFNet achieves state-of-the-art performance. © 2024, CC BY.

关键词： Image segmentation

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Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning

arXiv

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arXiv 2023年

作者： Liu, Yang Chen, Chen Wang, Can King, Xulin Liu, Mengyuan College of Computer Science Sichuan University China Center for Research in Computer Vision University of Central Florida United States Laboratory on Multimedia Information Processing The Department of Computer Science Kiel University Hangzhou Linxrobot Company China Hangzhou GOTHEN Technology Co. Ltd China Key Laboratory of Machine Perception Shenzhen Graduate School Peking University China

Masked Autoencoders (MAE) have demonstrated promising performance in self-supervised learning for both 2D and 3D computer vision. Nevertheless, existing MAE-based methods still have certain drawbacks. Firstly, the functional decoupling between the encoder and decoder is incomplete, which limits the encoder’s representation learning ability. Secondly, downstream tasks solely utilize the encoder, failing to fully leverage the knowledge acquired through the encoder-decoder architecture in the pre-text task. In this paper, we propose Point Regress AutoEncoder (Point-RAE), a new scheme for regressive autoencoders for point cloud self-supervised learning. The proposed method decouples functions between the decoder and the encoder by introducing a mask regressor, which predicts the masked patch representation from the visible patch representation encoded by the encoder and the decoder reconstructs the target from the predicted masked patch representation. By doing so, we minimize the impact of decoder updates on the representation space of the encoder. Moreover, we introduce an alignment constraint to ensure that the representations for masked patches, predicted from the encoded representations of visible patches, are aligned with the masked patch presentations computed from the encoder. To make full use of the knowledge learned in the pre-training stage, we design a new finetune mode for the proposed Point-RAE. Extensive experiments demonstrate that our approach is efficient during pre-training and generalizes well on various downstream tasks. Specifically, our pretrained models achieve a high accuracy of 90.28% on the ScanObjectNN hardest split and 94.1% accuracy on ModelNet40, surpassing all the other self-supervised learning methods. Our code and pretrained model are public available at: https://***/liuyyy111/ Point-RAE. Copyright © 2023, The Authors. All rights reserved.

关键词： Supervised learning

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Robust 3D Face Alignment with Multi-Path Neural Architecture Search

Robust 3D Face Alignment with Multi-Path Neural Architecture...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhichao Jiang Hongsong Wang Xi Teng Baopu Li Institute of Deep Learning (IDL) Baidu Beijing China Department of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China Computer Vision Technology Institution Baidu Beijing China Baidu Research Baidu Sunnyvale USA

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

3D face alignment is a very challenging and fundamental problem in computer vision. Existing deep learning-based methods manually design different networks to regress either parameters of a 3D face model or 3D positions of face vertices. However, designing such networks relies on expert knowledge, and these methods often struggle to produce consistent results across various face poses. To address this limitation, we employ Neural Architecture Search (NAS) to automatically discover the optimal architecture for 3D face alignment. We propose a novel Multi-path One-shot Neural Architecture Search (MONAS) framework that leverages multi-scale features and contextual information to enhance face alignment across various poses. The MONAS comprises two key algorithms: Multi-path Networks Unbiased Sampling Based Training and Simulated Annealing based Multi-path One-shot Search. Experimental results on three popular benchmarks demonstrate the superior performance of the MONAS for both sparse alignment and dense alignment.

关键词： Training Learning systems Solid modeling computer vision Three-dimensional displays Neural networks Simulated annealing Network architecture Search problems Faces

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Data Engineering and AI-Powered Skin Cancer Identification for Healthcare Applications

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Procedia computer science 2024年 246卷 179-188页

作者： Imran Ahmed Misbah Ahmad Abdellah Chehri Gwanggil Jeon School of Computing and Information Science Anglia Ruskin University Cambridge UK Animal and Agriculture Department Hartpury University Gloucester UK Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol UK Department of Mathematics and Computer Science at the Royal Military College of Canada (RMC) Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea

Skin cancer diagnosis, a critical task in the medical domain, can be revolutionized through the application of advanced deep-learning techniques. This work investigates the efficacy of Convolutional Neural Networks (CNNs) in the automated classification of skin cancer. The process begins with a comprehensive explanation of key CNN layers: Conv2D, MaxPool2D, Dropout, and Dense. The Conv2D layers employ learnable filters that transform localized image segments, while MaxPool2D contributes to downsampling, effectively reducing computational cost and overfitting risk. Integrating these layers enables the network to capture local and global characteristics, which is crucial for accurate classification. Adding Dropout layers enhances generalization and mitigates overfitting by introducing randomness during training. ReLU activation functions infuse non-linearity, and the Flatten layer facilitates the transition to fully connected layers. The proposed CNN architecture is meticulously designed considering filter counts, kernel sizes, and pooling dimensions. The trained model demonstrates promising performance by utilizing the HAM10000 dataset, encompassing diverse skin lesion images across seven classes. The CNN model’s parameters and architecture are systematically presented, offering insights into its design rationale. The model undergoes optimization with the Adam optimizer and annealing techniques to facilitate convergence. The model’s effectiveness is evaluated on validation and test datasets, demonstrating an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. Data augmentation strategies are introduced to enhance model generalization further. The results underscore CNN’s potential as a robust tool for automating skin cancer diagnosis, aligning with the broader trend of leveraging deep learning for medical image analysis

关键词： Image Processing Medical Imaging Deep learning Skin Cancer Data Analytics

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