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AMC-OA: Adaptive Multi-Scale Convolutional Networks with Optimized Attention for Temporal Action Localization 27th

AMC-OA: Adaptive Multi-Scale Convolutional Networks with Op...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Yuan, Rui Yuan, Chun Tsinghua Shenzhen International Graduate School Nanshan District Xili University Town Tsinghua Campus Guangdong Shenzhen518055 China

ISBN: (纸本)9783031781063

Temporal Action Localization (TAL) is crucial in video understanding, focusing on identifying and timestamping actions within raw video footage. A critical challenge in TAL is processing the rich spatiotemporal details inherent in videos, traditionally addressed through methods adapted from image processing. the vision Transformer (VIT) model marked a significant evolution, using a self-attention mechanism for enhanced temporal information blending. Despite these advancements, two key issues remain: insufficient extraction of spatial semantic information at lower levels of feature pyramids and inadequate capture of temporal semantic information at higher levels. To address these challenges, we introduce Adaptive Multi-Scale Convolutional Networks with Optimized Attention (AMC-OA). AMC-OA enhances lower-level features within the pyramid using multi-scale convolutional kernels, enriching spatial contextual semantics. Simultaneously, upper-level features are refined with a temporally-focused contextual enhancement network utilizing residual structures for better temporal understanding. To further improve the model’s capability in handling extensive temporal spans, we integrate an advanced multi-head attention mechanism. Empirical results on benchmarks like thUMOS14 and ActivityNet1.3 demonstrate AMC-OA’s superiority in TAL tasks, significantly improving both spatial and temporal information extraction compared to state-of-the-art models. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Spatio-temporal data

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Capturing Prior Knowledge in Soft Labels for Classification with Limited or Imbalanced Data 5th

Capturing Prior Knowledge in Soft Labels for Classification ...

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5th Chinese conference on pattern recognition and computer vision (PRCV)

作者： Zhong, Zhehao Zhao, Shen Wang, Ruixuan Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Peoples R China Key Lab Machine Intelligence & Adv Comp MOE Guangzhou Peoples R China

ISBN: (纸本)9783031189098;9783031189104

Successful applications of deep learning often depend on large amount of training data. However, in practical image recognition tasks, available training data are often limited or imbalanced across classes, causing the over-fitting issue or the prediction bias issue during model training. In this paper, based on word embedding models from studies in natural language processing, the prior knowledge about the relationships between image classes is utilized to help train more generalizable classifiers under the condition of limited or class-imbalanced training data. Such inter-class relational knowledge is captured in the word embedding vectors for the textual names of image classes. Using these word embedding vectors as soft labels for corresponding image classes, the feature extractor part of a deep learning model can be guided to learn to extract visual features which contain both class-specific and class-shared information. Experiments on multiple image classification datasets confirm that the proposed learning framework helps improve model performance when training data is limited or class-imbalanced.

关键词： Prototype learning image classification Limited data Imbalance data Soft label

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Deep Learning for Fine-Grained image recognition: A Comprehensive Study 2022

Deep Learning for Fine-Grained Image Recognition: A Comprehe...

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4th Asia Pacific Information Technology conference, APIT 2022

作者： Kang, Yuhua Chao, Guoqing Hu, Xin Tu, Zhiying Chu, Dianhui Department of Computer Science Harbin Institute of Technology Weihai China

ISBN: (纸本)9781450395571

In computer vision, image recognition is a noteworthy and hot research area which develops rapidly. the principal task of this technique is to automatically predict which pre-defined categories an image might belong to. Traditional image recognition targets to classify images into diversified highly distinguished categories. However, Fine-Grained image recognition (FGIR) aims to recognize the variances among images categorized in subordinate classes, e.g., species of birds, types of cars or species of flowers, which are equivalent to "species"in Taxonomy in certain aspects. As a result, models of FGIR are required to pick out features from finer granularity. Conventional methods apply special feature encoding to explore discernible attributes, while recent methods of FGIR makes great advancement with assistance of deep learning which has obtained the remarkable development nowadays. In this paper, we provide a new integration of the current leading FGIR models according to how they improve the development of FGIR. We classified them into five main categories and then compared their performance on three popular datasets and analyzed the results. To advance the further development of this topic, we point out some open problems worth further exploring. © 2022 ACM.

关键词： computer vision

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Weighted Features Based Classification of Polarimetric SAR images

Weighted Features Based Classification of Polarimetric SAR I...

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Iranian Machine vision and image processing (MVIP)

作者： Fatemeh Saneipour Maryam Imani Hassan Ghassemian Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

Today, classification of polarimetric images is an important topic where various statistical pattern recognition methods have been used to achieve the high accurate classification maps. In this work, weighting the polarimetric features according to their statistical behavior (the mean vector and variance values as the first and second statistics) is suggested to improve the PolSAR image classification. A weighted feature matrix is composed and applied to the popular classifiers such as maximum likelihood, K-nearest neighbor and support vector machine. the weighted feature matrix can be also implemented on other arbitrary classifiers to improve their discrimination ability. the experiments on the L-band AIRSAR dataset show appropriate classification results.

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A Method of Cell Counting Based on computer vision 12

A Method of Cell Counting Based on Computer Vision

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12th international conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Ding, Shihong Zhao, Huanyu Han, Zhi Tang, Yandong Shenyang Ligong University CO Shenyang110158 China The First Affiliated Hospital of China Medical University CO Shenyang110002 China Shenyang Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Robotics CO Shenyang110016 China Institutcs for Robotics and Intelligent Manufacturing Chinese Academy of Sciences CO Shenyang110168 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

For a long time, making machines have the ability of pattern recognition is the direction of scientists' continuous research. the study of pattern recognition is the fundamental way to understand the essence of artificial intelligence. At present, computer vision has been used to identify and count cells. In view of the problems existing in the field of cell counting, such as difficulties in identifying cells, dense cells, viscosity, difficult cell counting, inaccurate judgment and so on, this paper realizes the identification and counting through the technology of computer vision, determines the types of cells, and explores the characteristics of dense and sticky cells, At the same time, based on the characteristics of dense and sticky cells, a color based threshold segmentation algorithm is introduced to optimize the performance of cell classification. At the same time, the recognition and classification of different regions of adhesive cells are carried out, and the adhesive cells in different regions are recognized and judged by the technology of computer vision. through gray-scale transformation, threshold segmentation, morphological processing and LOG algorithm filtering of medical cell image, the cell region is extracted and counted. the cell counting is realized by calculating the connected domain. the experimental results preliminarily verify the effectiveness of this method, but it still needs to be improved. © 2022 IEEE.

关键词： Cells

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Uav automatic spraying system based on image recognition of pests and diseases 4

Uav automatic spraying system based on image recognition of ...

引用

4th international conference on Big Data Economy and Information Management, BDEIM 2023

作者： Yi, Yongyi Lang, Chunling Xin, Chuanxin Lan, Haoxin Gao, Xiulong Lin, Guojian Lian, Zhaoran College of Mathematics and Computer Science Guangxi Science and Technology Normal University LaiBin546199 China College of Vocational and Technical Education Guangxi Science and Technology Normal University LaiBin546199 China

ISBN: (纸本)9798400716669

In recent years, with the development of UAV technology, more and more application scenarios have emerged. Among them, the application of drones to agricultural spraying is a field with great potential. this paper presents an automatic spraying system of UAV based on image recognition of pests and diseases. the system uses cameras to take photos and image processing, identify the type and severity of pests and diseases through the images, and automatically adjust the concentration and flight altitude according to the information obtained, and accurately spray pesticides. the experimental results show that the system can identify more than 10 kinds of common diseases and pests with an accuracy of more than 90%, and the spraying effect is significantly better than the traditional manual spraying method. the system has the advantages of simple operation, high efficiency and environmental protection, and is expected to be applied in large-scale agricultural production. © 2023 ACM.

关键词： Drones

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Application of image segmentation technology in TCM eye diagnosis 16

Application of image segmentation technology in TCM eye diag...

引用

16th international conference on Photonics and Imaging in Biology and Medicine, PIBM 2023

作者： Peng, Hong Niu, Ning Zhang, Yilin Wang, Guanjun Xue, Chenyang Huang, Mengxing Hainan University Haikou City China North University of China Taiyuan City China

ISBN: (纸本)9781510667341

image segmentation is a critical technology in many fields, such as image processing, pattern recognition, and artificial intelligence. It is also the first and critical step in computer vision technology. Tongue diagnosis combined with deep learning for segmentation and extracting pathological features is relatively mature, but deep learning combined with TCM visualization is sporadic. First, We used the U2Net network1 for segmentation extraction of the sclera in this study. Where the U2Net1 network1 (based on PyTorch) relies on the extensive use of data enhancements to use the available annotation samples more efficiently, and compared with the U-Net network, the U2Net network1 updates an RSU module, each RSU module is a small U-net network, merging multiple U-Net outputs to get the merged Mask target. Finally, we applied classical CNN networks to evaluate the segmentation effect, introducing different evaluation metrics such as Miou, Precision, and Recall. We used the publicly available dataset UBIVIS.V12 for our experiments, where our Miou was as high as 97.3%, and U2Net achieved better results among all the networks, which laid the foundation for our subsequent segmentation and extraction of blood filament features © 2023 SPIE.

关键词： image segmentation

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Lightweight Single image Super-Resolution Network Integrating CNN and Transformer 27th

Lightweight Single Image Super-Resolution Network Integratin...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Zhu, Kai Chen, Li School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031781247

Recently, remarkable progress has been achieved in single image super-resolution using methods based on CNN and Transformer architectures. However, existing approaches often construct a substantial number of network layers, leading to a significant increase in performance requirement and memory consumption, thereby limiting the practical deployment and usability of the models. To address this issue, we propose an Alternating CNN Transformer Block and an Integrative CNN Efficient Transformer for single image super-resolution. We enhance feature extraction efficiency by combining CNN within and between Transformer modules. In addition, we propose two novel structures: Multi-branch Gated CNN and Parallel Channel Attention, aiming to efficiently extract local spatial information and global channel information from images. Extensive experiments demonstrate that our model achieves high performance while maintaining low model complexity. the proposed model attains PSNR values of 32.32 and 30.78 for the Set5 and Manga109 benchmark datasets, respectively, at a scale factor of ×4. Compared to other lightweight super-resolution models, our proposed model outperforms them at lower computational costs. the source codes are available at https://***/kylechuuuuu/ICTSRN. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： HTTP

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Enhancing the Performance of Multi-Objective Regression for Pelvic Organ Prolapse Prediction via Data Augmentation 4

Enhancing the Performance of Multi-Objective Regression for ...

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4th international conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2023

作者： Mi, Yanlin Tabirca, Sabin O'Reilly, A. University College Cork School of Computer Science and Information Technology Cork Ireland University College Cork Sfi Centre for Research Training in Artificial Intelligence Cork Ireland Transylvania University of Brasov Faculty of Mathematics and Informatics Brasov Romania Cork University Maternity Hospital Department of Obstetrics and Gynecology Cork Ireland

ISBN: (纸本)9798350329490

In healthcare, machine learning has been increasingly applied to predictive models, but the efficacy of these models is often compromised due to limitations in data quality, diversity, and metrics. In other domains, such as image recognition and natural language processing, data augmentation techniques have been successfully applied to mitigate these challenges, but in healthcare such strategies have not been widely applied. therefore, our research actively explores how these data augmentation techniques can be applied to machine learning models for predicting the outcome of pelvic organ prolapse surgery. We first performed in-depth data preprocessing and then tried innovative data enhancement techniques such as noise injection and self-sampling. the results show that the application of data enhancement techniques significantly improves the performance of predictive models and effectively addresses data scarcity and quality issues, which opens up new possibilities for wider application of data enhancement techniques in the medical field in the future. © 2023 IEEE.

关键词： Surgery

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Research on Obstacle Avoidance and image Registration Technology Based on Binocular vision 4

Research on Obstacle Avoidance and Image Registration Techno...

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4th international conference on Advances in Electrical Engineering and computer Applications, AEECA 2023

作者： Chen, Zihong Zhang, Xing Anhui Sanlian University Hefei230601 China

ISBN: (纸本)9798350308082

To enable robots to navigate and avoid obstacles in uncertain environments, a 3D coordinate system was constructed based on visual information decision-making and camera calibration results. A region-based stereo matching algorithm was used to extract depth information from the binocular stereo images to improve the reliability of the disparity map and to enable the robot to achieve local obstacle avoidance. the algorithm works by first selecting a point that does not belong to any growing region, then using an energy function to determine its disparity and setting it as the root disparity, and calculating the error of adjacent points based on the root disparity to determine the region correlation. this process is repeated until all points in the image have been processed. Building a 3D coordinate system based on visual information decision-making and camera calibration results enables the robot to autonomously navigate and avoid obstacles. It allows the robot to accurately perceive its location and direction in uncertain environments, and adjust its trajectory in real-time based on environmental changes. Simulation analysis shows that using binocular recognition and Bug2 algorithm can improve the reliability of the disparity map and enable the robot to achieve local obstacle avoidance. © 2023 IEEE.

关键词： Decision making

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