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VPPT: Visual Pre-Trained Prompt Tuning Framework for Few-Shot image Classification

VPPT: Visual Pre-Trained Prompt Tuning Framework for Few-Sho...

引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zhao Song Ke Yang Naiyang Guan Junjie Zhu Peng Qiao Qingyong Hu Defense Innovation Institute Academy of Military Sciences Beijing China National University of Defense Technology Changsha China

Large-scale pre-trained transformers have recently achieved remarkable success in several computer vision tasks. However, it remains highly challenging to fully fine-tune models for downstream tasks, due to the expensive computational and storage cost. Recently, Parameter-Efficient Tuning (PETuning) techniques, e.g., Visual Prompt Tuning (VPT), have significantly reduced the computation cost by inserting lightweight prompt modules including prompt tokens or adapter layers, into the pre-trained models and tuning these prompt modules with a small number of trainable parameters, while keeping the transformer backbone freeze. Although encouraging results were achieved, existing PETuning methods cannot perform well under the few-shot learning settings (i.e., extremely limited training data, with only 1 or 2 shots per class), due to the scarce supervision signal. To this end, we first empirically identify the poor performance is mainly due to the inappropriate way of initializing prompt modules, which has also been verified in the pre-trained language models. Next, we propose a Visual Pre-trained Prompt Tuning framework (VPPT), which pre-trains the prompt modules first and then leverages the pre-trained modules along with the pre-trained transformer backbone to perform prompt tuning on downstream tasks. Extensive experiments show that our VPPT framework achieves 16.08% average accuracy absolute improvement under 1 shot setting on five fine-grained visual classification datasets, compared with the previous PETuning techniques, e.g., VPT, in few-shot image classification.

关键词： Visualization Adaptation models Costs Computational modeling Training data Transformers Task analysis

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DEM Generation From Mono-Multispectral images via Uncalibrated Epipolar Rectification and 3D Registration

DEM Generation From Mono-Multispectral Images via Uncalibrat...

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Recent Advances in Systems Science and Engineering (RASSE), ieee international conference on

作者： Ashutosh Gupta Hemant K. Lalwani Subhajit Paul Debajyoti Dhar Signal and Image Processing Area Space Applications Centre (SAC) Indian Space Research Organisation (ISRO) Ahmedabad India

Generation of Digital Elevation Models (DEM) typically requires fine image matching between set of images acquired with a wide-baseline and appropriate B/H ratio. Wide baseline stereo imaging is an expensive process since it requires two or more acquisitions at the cost of nominal mono-imaging operations. Due to the wide-baseline, angular separation in views is required, which results in increased occlusion problems leading to holes in the generated DEM. Multiview images taken with wide baselines also have lower overlap area compared to images taken from narrow-baseline systems. Moreover, while making orthorectified maps, large tilts need to be corrected using processing techniques to obtain a top-down view. To alleviate these issues, we propose a novel DEM generation pipeline that utilizes the narrow-baseline information between bands of a single multispectral image. Our method can generate sub-pixel accurate disparity maps while tackling spectral information change. Final DEM is generated from the disparity by using reference Ground Control Points (GCPs) and an iterative control point registration procedure. We demonstrate the effectiveness of our DEM generation pipeline on Resourcesat-2 LISS- IV multispectral image with a BIH ratio as low as 0.018. Our results show that sub pixel disparity of the order of 1/50th of a pixel can be computed. A visual and quantitative comparison of the generated DEM with the wide-baseline DEM from Cartosat-l shows that the elevation information is estimated with good overall agreement even for a hilly region. Our technique makes it possible to extract elevation information from mono-multispectral images with unusually low B/H ratios, potentially enabling the generation of DEMs from multispectral images that would not normall be considered viable for this purpose.

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Additive Angular Margin Loss for Federated Learning in image Classification 39

Additive Angular Margin Loss for Federated Learning in Image...

引用

39th international conference on Information Networking, ICOIN 2025

作者： Tran, Phuong-Nam Dang, Duc Ngoc Minh Huh, Eui-Nam Hong, Choong Seon Kyung Hee University Dept. of Artificial Intelligence Yongin 446-701 South Korea FPT University AiTA Lab Dept. of Computing Fundamental Ho Chi Minh City Viet Nam Kyung Hee University Dept. of Computer Science and Engineering Yongin 446-701 South Korea

ISBN: (纸本)9798331506940

Federated learning (FL) is a method that leverages data from multiple sources to enhance deep learning models while safeguarding data privacy. This approach finds applications in diverse domains such as healthcare, entertainment, and user experience enhancement. Despite its effectiveness, FL's performance is still low compared to centralized training methods, necessitating further improvements. Particularly in critical areas such as medicine, where accurate model predictions are crucial, conventional FL algorithms fall short compared to centralized training. This study introduces a new training approach to further improve the vanilla FL algorithm. Our proposed method takes advantage of feature-based adjustments using cosine angles by incorporating an angular margin loss function alongside the cross-entropy loss. It notably enhances the accuracy of the aggregated models on datasets such as MNIST, CIFAR10, and CIFAR100 while maintaining FL's privacy-preserving attributes. Moreover, we conduct a comprehensive comparative analysis of the proposed method against existing FL algorithms to evaluate the impact of the angular margin loss function on the learning process. Our experimental results underscore the effectiveness of the proposed algorithm when compared with standard benchmarks, proving its potential to advance the field of FL. © 2025 ieee.

关键词： Angular margin loss Data heterogeneity Decentralized training Federated learning (FL) Personalized Federated Learning (PFL) Representation learning

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Application of image Recognition Algorithms in the Detection of Philippine Lime Diseases

Application of Image Recognition Algorithms in the Detection...

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ieee international conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Mikho J Pelingon Valenzuela Franco Carlos Maria Leonora C Guico Jan Kevin A Galicia Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon City Philippines

Calamansi has been declared as one of the most important fruit growing crops in the Philippines. However, due to certain bacteria, it is susceptible to certain diseases affecting its harvest rate. This paper aims to effectively monitor the state of the calamansi at its healthy state and at its diseased state. Specifically, it classifies diseases such as Citrus Canker, Citrus Scab, and Citrus Browning by utilizing existing image processing techniques for disease detection of different fruits and determining which algorithm is most apt for this application in terms of precision, accuracy and recall. Techniques such as K-Means Clustering, utilization of an Artificial Neural Network (ANN), feature extraction through GLCM along with the usage of a minimum distance classifier, a Support Vector Machine (SVM) classifier and other techniques and/or their combinations were explored and measured. The researchers performed two kinds of tests: 1×1 comparison and merged comparison. For the 1×1 comparison, making use of GrabCut, color feature extraction, and SVM produced the best overall results, with an overall average of 98% for precision, 95% for accuracy, 91% for recall, and 94% for F-score. Adaptive Gaussian Filtering along with texture feature extraction and SVM was the most accurate for detecting calamansi fruits with citrus canker and citrus scab. Overall, the two methods acquired the same average accuracy of 61%

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StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

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conference on computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_image$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-Databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

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Artificial Neural Network using image processing for Digital Forensics Crime Scene Object Detection

Artificial Neural Network using Image Processing for Digital...

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international conference on Edge Computing and applications (ICECAA)

作者： Deepa Devasenapathy M Raja Rajat Kumar Dwibedi N Vinoth T. Jayasudha V Dilli Ganesh Instructor-II/ Computing & Software Engineering U.A. Whitaker College of Engineering Florida Gulf Coast University Department of Computer Science and Engineeting CMR Institute of Technology Bengaluru India Aarupadai Veedu Institute of Technology VMRF Chennai India Department of Mechatronics Engineering Chennai Institute of Technology Chennai India Department of English KPR Institute of Engineering and Technology Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Science-(SIMATS) Chennai India

Digital forensics science places a significant emphasis on the detection of objects as one of the most vital areas of study. Several industries and institutions may benefit from the object detection method, including those concerned with medical diagnostic scanning, traffic monitoring, airport security, law enforcement, and data rescue on a local and global scale. This study aims to detect weapons in video surveillance images by using various enhancement, segmentation, feature extraction, and classification methods by Artificial Neural Network to improve the detection accuracy. Yet, several mathematical and algorithmic models are computed to provide the appropriate approaches.

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Multi-scale Heterogeneous Graph Contrastive Learning*

Multi-scale Heterogeneous Graph Contrastive Learning*

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ieee international conference on Big Data

作者： Mingyuan Li Lei Meng Zhonglin Ye Haixing Zhao Yuzhi Xiao Shujuan Cao College of Computer Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

In recent years, heterogeneous graph neural networks have become the mainstream approach for handling heterogeneous graph data. However, due to the sparsity of labels, most existing methods for heterogeneous graph neural networks typically employ a semi-supervised learning approach, which has certain limitations in practical applications. To address this issue, we propose a self-supervised heterogeneous graph representation learning method, namely Multi-scale Heterogeneous Graph Contrastive Learning (MHGCL). This approach decodes encoded information from two perspectives: meta-paths and network patterns, in a multi-scale fashion. It uses a loss function that maximizes the similarity between positive pairs at different scales and minimizes the similarity between negative pairs. This encourages related nodes and edges to be close to each other in the embedding space, while unrelated nodes and edges are pushed farther apart. Experimental results demonstrate that MHGCL comprehensively captures semantic information at different scales between nodes. It exhibits excellent performance in node classification tasks, validating its effectiveness in heterogeneous graph node embedding learning.

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GC-SALM: Multi-Task Runoff Prediction Using Spatial-Temporal Attention Graph Convolution Networks

GC-SALM: Multi-Task Runoff Prediction Using Spatial-Temporal...

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ieee international conference on Systems, Man and Cybernetics

作者： Jin Lu Zaipeng Xie Jiayu Chen Maohua Li Chenghong Xu Hongli Cao Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer and Information Hohai University Nanjing China Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Southeast University Nanjing China

Runoff prediction is essential for flood forecasting, irrigation planning, and sustainable water resource management. However, accurate predictions can be challenging due to the involvement of multiple variables. This paper presents a novel Graph Convolution-based Spatial-temporal Attention LSTM Multi-Task learning (GC-SALM) model for accurate runoff predictions. Our approach combines a multilayer neural network and an attention mechanism for enhanced generalization performance. The GC-SALM model employs spatial attention and graph convolutional networks to discern local and global spatial patterns, while temporal attention and LSTM are utilized to capture temporal characteristics within extended sequences. Experimental results reveal that the proposed model outperforms six state-of-the-art methods in runoff prediction and flow calibration, emphasizing its potential for real-world hydrological applications.

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SpliceCannon: A novel framework for the prediction of canonical and non-canonical splice sites based on deep learning

SpliceCannon: A novel framework for the prediction of canoni...

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ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Yanju Zhang Wangjing Qi Ruohan Lin Jinli Fan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China College of Computer Science and Technology Huaqiao University Xiamen China

Accurately predicting splice sites in DNA sequences is not only of utmost importance in the study of gene expression and protein synthesis, but also crucial to precisely understanding the pathogenesis of various diseases related to splicing. In the past few decades, several tools for identifying splice sites have been developed, however, many of them ignore the contextual influence in DNA sequences in the process of encoding, consequently leading to considerable false positives in prediction. Additionally, most methods solely investigate canonical splicing whereas pay little attention to non-canonical ones. To address the issues mentioned above, we propose a comprehensive method, SpliceCannon, for accurately predicting both canonical and non-canonical splice sites based on neural networks. This method uses the combination of k-nucleotide frequencies and Doc2vec to encode the sequence, then employs a series of comprehensive neural networks such as bidirectional long short-term memory networks and residual networks to effectively extract features from the original sequence while avoiding the problems such as gradient vanishing. The results demonstrate that our method consistently achieves high accuracy when tested on multiple species. Compared to other recent methods, our model exhibits superior performance, stronger generalization ability, and wider applicability. In particular, The recognition accuracy is also remarkably high on the non-canonical dataset. We envision that this proposed method will serve as a promising tool for splice sites prediction to facilitate genomic research.

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Multi-scale convolutional neural networks for brain disease diagnosis

Multi-scale convolutional neural networks for brain disease ...

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ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhongyi Hu Libin Gao Yinsheng Tong Xingjin Lu Rui Li Lei Xiao Zuoyong Li College of Computer Science and Aritificial Intelligence Wenzhou University Wenzhou China Key Laboratory of Intelligent Image Processing and Analysis Wenzhou China College of Computer and Control Engineering Minjiang University Fuzhou China

The brain is the most sophisticated and complex organ in the human body. Nowadays, diagnosing complex and diverse brain diseases is a hot topic. Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), and others are common brain diseases. With the increased usage of Artificial Intelligence (AI) in medical image analysis, the endeavor to make AI comprehend brain images for assisting doctors in making objective diagnoses of brain diseases has gained considerable attention. Resting-state functional magnetic resonance imaging (rs-fMRI) is a widely used tool for diagnosing and analyzing brain ***, the Pearson correlation coefficient (PCC) method constructs a dynamic functional connectivity (dFC) network using a fixed window size. However, this method has limitations as it is challenging to extract potential high-level features from the dFC and determine local feature relevance. This paper presents a method for constructing dFC based on multi-scale sliding windows and proposes Multi-scale Convolutional Neural Networks (MsCNN) for learning and analyzing dFC at various scales. Finally, a deep fusion of features learned at different scales is employed for the diagnostic classification of brain *** to the rs-fMRI dataset from ADNI, the classification accuracy was 84.0% for eMCI/lMCI, 84.4% for lMCI/AD, and 58.4% for NC/eMCI/lMCI/AD. Applied to rs-fMRI data from ABIDE, the accuracy was 74.7% for ASD/NC. The proposed method exhibits robust classification performance and introduces a new approach to diagnosing other brain diseases.

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