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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是81-90 订阅

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Zero-Shot Offline Handwritten Chinese Character recognition with Graph Embedding 26

Zero-Shot Offline Handwritten Chinese Character Recognition ...

引用

26th International conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhang, Xiong Weng, Zhenyu Zhang, Xizhe Hu, Jiahao Zhu, Yuesheng Peking University School of Electronic and Computer Engineering Shenzhen China

ISBN: (纸本)9798350331684

Handwritten Chinese character recognition (HCCR) is a challenging topic in the field of computer vision due to its numerous categories, complex structure, shape similarity, casual writing style, and lack of training data. Some studies have attempted to develop radicals-based approaches with zero-shot recognition capacity to alleviate the data dependency problem of deep learning. However, previous studies tend to treat Chinese characters as isolated individuals and focus solely on the structural characteristics, ignoring the correlation information among Chinese characters. In this paper, we construct a graph to represent the correlation information and propose a novelty zero-shot HCCR method based on graph embedding. In addition, we present a pre-training approach for the encoder based on contrastive learning. the experimental results show that our method outperforms the other radical-based zero-shot recognition methods and also achieves a competitive performance on traditional experiment setting. © 2023 ieee.

关键词： Embeddings

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Adaptive Tiny Object Detection for Improving Pest Detection 26

Adaptive Tiny Object Detection for Improving Pest Detection

引用

26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Huang, Renjie He, Yuting Xiao, Guoqiang Shi, Yangguang Zheng, Yongqiang Southwest Univ Citrus Res Inst Natl Engn Res Ctr Citrus Technol Sch Comp & Informat Sci Chongqing 400715 Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In agricultural pest management based on computer vision, numerous species of tiny pests need to be detected in images. However, such tiny detection objects are usually missed when adopting deep detection networks. To improve the detection of tiny pests, this paper presented an adaptive tiny object detection network based on the CenterNet framework. Firstly, a branch with a learnable gating function is integrated into the backbone, and supervised learning is performed on it so that tiny pests' high-resolution feature maps with category and location semantics are exploited, and the learned gating function adaptively controls the combination of such feature maps and the backbone. Moreover, we proposed a size-adaptive weighting method to improve the CenterNet's detection loss function. In training, a higher weight will be assigned to an instance if its size is smaller or its prediction center is farther from the ground truth. Extensive experiments on multiple datasets verify that our two contributions, i.e. the adaptive-gating branch, and the size-adaptive weighting method, are both help to enhance tiny pests' weak feature responses and their discriminations, and further improve the IoU accuracies in detection.

关键词： Training Heating systems computer vision Adaptive systems Supervised learning Semantics Object detection

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Edge Augmentation for Large-Scale Sketch recognition without Sketches 26

Edge Augmentation for Large-Scale Sketch Recognition without...

引用

26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Efthymiadis, Nikos Tolias, Giorgos Chum, Ondrej Czech Tech Univ VRG Fac Elect Engn Prague Czech Republic

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

this work addresses scaling up the sketch classification task into a large number of categories. Collecting sketches for training is a slow and tedious process that has so far precluded any attempts to large-scale sketch recognition. We overcome the lack of training sketch data by exploiting labeled collections of natural images that are easier to obtain. To bridge the domain gap we present a novel augmentation technique that is tailored to the task of learning sketch recognition from a training set of natural images. Randomization is introduced in the parameters of edge detection and edge selection. Natural images are translated to a pseudo-novel domain called "randomized Binary thin Edges" (rBTE), which is used as a training domain instead of natural images. the ability to scale up is demonstrated by training CNN-based sketch recognition of more than 2.5 times larger number of categories than used previously. For this purpose, a dataset of natural images from 874 categories is constructed by combining a number of popular computer vision datasets. the categories are selected to be suitable for sketch recognition. To estimate the performance, a subset of 393 categories with sketches is also collected.

关键词： Training Bridges computer vision Image edge detection pattern recognition Task analysis

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Intensity-Sensitive Similarity Indexes for Image Quality Assessment 26

Intensity-Sensitive Similarity Indexes for Image Quality Ass...

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Li, Xiaotong Armour, Wesley Univ Oxford Dept Engn Sci Oxford E Res Ctr Oxford England

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

the importance of Image quality assessment (IQA) is ever increasing due to the fast paced advances in imaging technology and computer vision. Among the numerous IQA methods, Structural SIMilarity (SSIM) index and its variants are better matched to the perceived quality of the human visual system. However, SSIM methods are insufficiently sensitive, when images contain low information, where the important information only occupies a low proportion of the image while most of the image is noise-like, which is common in scientific data. therefore, we propose two new IQA methods, InTensityWeighted SSIM index and Low-Information Similarity Index, for such low information images. In addition, auxiliary indexes are proposed to assist with the assessment. the application of these new IQA methods to natural images and field-specific images, such as radio astronomical images, medical images, and remote sensing images, are also demonstrated. the results show that our IQA methods perform better than state-of-the-art SSIM methods for differences in high-intensity parts of the input images and have similar performance to that of the original and gradient-based SSIM for differences in low-intensity parts. Different similarity indexes are suitable for different applications, which we demonstrate in our results.

关键词： Image quality computer vision Sensitivity Visual systems pattern recognition Indexes Remote sensing

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Unsupervised Domain Adaptation for Video Transformers in Action recognition 26

Unsupervised Domain Adaptation for Video Transformers in Act...

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： da Costa, Victor G. Turrisi Zara, Giacomo Rota, Paolo Oliveira-Santos, thiago Sebe, Nicu Murino, Vittorio Ricci, Elisa Univ Trento Trento Italy Univ Fed Espirito Santo Vitoria ES Brazil Univ Verona Verona Italy Ist Italiano Tecnol Pattern Anal & Comp Vis PAVIS Genoa Italy Fdn Bruno Kessler Povo Italy

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Over the last few years, Unsupervised Domain Adaptation (UDA) techniques have acquired remarkable importance and popularity in computer vision. However, when compared to the extensive literature available for images, the field of videos is still relatively unexplored. On the other hand, the performance of a model in action recognition is heavily affected by domain shift. In this paper, we propose a simple and novel UDA approach for video action recognition. Our approach leverages recent advances on spatio-temporal transformers to build a robust source model that better generalises to the target domain. Furthermore, our architecture learns domain invariant features thanks to the introduction of a novel alignment loss term derived from the Information Bottleneck principle. We report results on two video action recognition benchmarks for UDA, showing state-of-the-art performance on HMDB <-> UCF, as well as on Kinetics -> NEC-Drone, which is more challenging. this demonstrates the effectiveness of our method in handling different levels of domain shift. the source code is available at https://***/vturrisi/UDAVT.

关键词： Adaptation models Visualization computer vision Source coding computer architecture Benchmark testing Transformers

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Pedestrian Identification and Tracking within Adaptive Collaboration Edge Computing* 26

Pedestrian Identification and Tracking within Adaptive Colla...

引用

26th International conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Tao, Ming Li, Xueqiang Xie, Renping Ding, Kai Dongguan University of Technology School of Computer Science and Technology Dongguan China

ISBN: (纸本)9798350331684

Nowadays, video surveillance is widespread used to achieve security life in the construction of smart city. As a result, prevalence of video surveillance equipments and technologies enables pedestrian identification and tracking to be research hotspots in the field of computer vision, whose development and application are of great significance to the construction of a good social security environment. However, pedestrian identification and tracking in monitoring scenarios still have problems of low recognition accuracy and high model complexity, and computer vision based adaptive recognition methods still have a large room for improvement and development. To address this issue, real-time pedestrian identification and tracking within adaptive collaboration edge computing environment is investigated in this paper. Within the paradigm of edge computing, the Raspberry Pi 3B+ acting as the edge computing node is adopted to handle the related issues of pedestrian identification and tracking. the combination of HOG (Histogram of Oriented Gradient) and SVM (Support Vector Machine) is investigated to achieve pedestrian identification, where, HOG is employed as the feature descriptor and SVM is employed as the classification algorithm. Furthermore, the pixel-based visual tracking algorithm is investigated to achieve effective and uninterrupted pedestrian tracking. By implementing a prototype on Raspberry Pi 3B+ using OpenCV libraries, the experimental results finally have been shown to demonstrate the efficiency of the investigations. © 2023 ieee.

关键词： Edge computing

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vision Transformers Based Classification for Glaucomatous Eye Condition 26

Vision Transformers Based Classification for Glaucomatous Ey...

引用

26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Wassel, Moustafa Hamdi, Ahmed M. Adly, Noha Torki, Marwan Minist Commun & Informat Technol Appl Innovat Ctr Alexandria Egypt Alexandria Univ Comp & Syst Engn Dept Alexandria Egypt

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Glaucoma is an eye condition marked by apoptotic ganglion cell death. this condition is primarily caused by increased intraocular pressure (IOP). the clinical examination of the ganglion cell layer is difficult. However, its demise results in distinctive optic nerve alterations. Several clinical examinations are present to diagnose the glaucoma condition. the fundus image captures the optic nerve. Hence, we can apply deep learning models to automatically diagnose the Glaucoma condition in a fundus image. In this paper, we study the classification of the fundus image using a vision transformer-based ensemble. vision transformers employ self-attention to capture global characteristics of the fundus image. this makes vision transformers a perfect choice for classification problems. We formed one large merged dataset of six publicly available full fundus images for Glaucoma detection. We provide a comprehensive evaluation for more than seven different vision transformer baseline models. Also, we propose an ensemble of the best vision transformer models. We report areas under the curve, sensitivity, and specificity measures due to the class imbalance in the available data. We report the best standalone model achieves 92.57 in sensitivity, 96.94 in specificity, and 97.9 in AUC.

关键词： Shape Microprocessors computer architecture Sensitivity and specificity Transformers Optical imaging Optical sensors

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MD-Net: Multi-Detector for Local Feature Extraction 26

MD-Net: Multi-Detector for Local Feature Extraction

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Santellani, Emanuele Sormann, Christian Rossi, Mattia Kuhn, Andreas Fraundorfer, Friedrich Graz Univ Technol Inst Comp Graph & Vision Graz Austria R&D Ctr Stuttgart Lab 1 Sony Europe B V Berlin Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Establishing a sparse set of keypoint correspondences between images is a fundamental task in many computer vision pipelines. Often, this translates into a computationally expensive nearest neighbor search, where every keypoint descriptor at one image must be compared with all the descriptors at the others. In order to lower the computational cost of the matching phase, we propose a deep feature extraction network capable of detecting a predefined number of complementary sets of keypoints at each image. Since only the descriptors within the same set need to be compared across the different images, the matching phase computational complexity decreases with the number of sets. We train our network to predict the keypoints and compute the corresponding descriptors jointly. In particular, in order to learn complementary sets of keypoints, we introduce a novel unsupervised loss which penalizes intersections among the different sets. Additionally, we propose a novel descriptor-based weighting scheme meant to penalize the detection of keypoints with non-discriminative descriptors. With extensive experiments we show that our feature extraction network, trained only on synthetically warped images and in a fully unsupervised manner, achieves competitive results on 3D reconstruction and re-localization tasks at a reduced matching complexity.

关键词： computer vision three-dimensional displays Pipelines Nearest neighbor methods Feature extraction Computational efficiency Task analysis

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ICPR 2022: Challenge on Harvesting Raw Tables from Infographics (CHART-Infographics) 26

ICPR 2022: Challenge on Harvesting Raw Tables from Infograph...

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Davila, Kenny Xu, Fei Ahmed, Saleem Mendoza, David A. Setlur, Srirangaraj Govindaraju, Venu Univ Tecnol Centroamer Fac Ingn Tegucigalpa Honduras Univ Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

the outcomes of the third Challenge on HArvesting Raw Tables from Infographics (ICPR 2022 CHART-Infographics) are presented in this work. Recognizing charts is a difficult process which we divided into the following task: Chart Image Classification (Task 1), Text Detection and recognition (Task 2), Text Role Classification (Task 3), Axis Analysis (Task 4), Legend Analysis (Task 5), Plot Element Detection and Classification (Task 6.a), Data Extraction (Task 6.b), and End-to-End Data Extraction (Task 7). We have provided a novel dataset for training reusing all available data from previous challenges, and we also provide a brand new testing dataset for the evaluation of submissions. Both datasets were constructed by manually annotating charts extracted from the Open Access section of the PubMed Central. A total of 9 teams registered out of which 5 submitted results for different tasks of the challenge. Many submissions are based on state-of-the-art methods from computer vision, but the final scores imply that more work will be required to solve the chart recognition problem. the data, annotation tools, and evaluation scripts have been publicly released for academic use.

关键词： Training computer vision Image recognition Text recognition Open Access Transformers Data mining

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Towards Scalable and Efficient Client Selection for Federated Object Detection 26

Towards Scalable and Efficient Client Selection for Federate...

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Constantinou, George You, Suya Shahabi, Cyrus Univ Southern Calif Integrated Media Syst Ctr Los Angeles CA 90089 USA Army Res Lab Adelphi MD USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Various computer vision techniques based on deep neural networks have been proposed to detect objects accurately and fast. However, due to the privacy, security and communication bandwidth restrictions of diverse participating parties, it is sometimes prohibitive to train such models on a centralized machine. Federated Learning (FL) provides a promising solution to learn a model from decentralized data. Despite the advances in FL, the diversity of client regions in which they operate and the Non-IID nature of the crowdsourced datasets reduces the accuracy of object detection models significantly. In this paper, we introduce a novel FL object detection system to efficiently train models with heterogeneous client datasets. We propose lightweight client selection methods to learn object detection models faster. Our client selection methods based on the object data distribution at clients achieves up to 74% reduction in required federated rounds compared to conventional approaches. We further extend this method by leveraging the metadata of the training images (e.g., location, direction, depth), to select clients which maximize the coverage of diverse geographical regions. We report on extensive experiments with real datasets.

关键词： Training Deep learning Privacy computer vision Federated learning Neural networks Object detection

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