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检索条件"任意字段=Brazilian Symposium on Computer Graphics and Image Processing"

共 3048 条记录，以下是81-90 订阅

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Mastering Scene Understanding: Scene Graphs to the Rescue

Mastering Scene Understanding: Scene Graphs to the Rescue

引用

brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Carlos Caetano Leo Sampaio Ferraz Ribeiro Camila Laranjeira Gabriel Oliveira dos Santos Artur Barros Caio Petrucci Andreza Aparecida dos Santos João Macedo Gil Carvalho Fabricio Benevenuto Jefersson A. dos Santos Sandra Avila Instituto de Computação (IC) Universidade Estadual de Campinas (UNICAMP) Campinas Brazil Department of Computer Science (DCC) Universidade Federal de Minas Gerais (UFMG) Belo Horizonte Brazil School of Computer Science University of Sheffield Sheffield United Kingdom

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

The evolution of scene understanding in computer vision has seen remarkable advancements, driven significantly by the development and utilization of scene graphs due to their powerful structural and semantic representation. This structured approach allows for better contextual understanding, facilitating tasks such as image captioning, image generation, image retrieval, human-object interaction, and visual question answering. This tutorial paper aims to comprehensively investigate the current scene graph research by discussing their generation methods, applications, standard datasets, and future development insights.

关键词： Visualization computer vision image synthesis Semantics image retrieval Tutorials Question answering (information retrieval) Standards

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A New Grammar for Creating Convolutional Neural Networks Applied to Medical image Classification 34

A New Grammar for Creating Convolutional Neural Networks App...

引用

34th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： da Silva, Cleber A. C. F. Miranda, Pericles B. C. Cordeiro, Filipe R. Fed Rural Univ Pernambuco UFRPE Dept Stat & Informat Recife PE Brazil

ISBN: (纸本)9781665423540

In the last decade, the adoption of Deep Convolutional Neural Networks (CNNs) has been successfully applied to solve computer vision tasks, such as image classification in the medical field. However, the several architectures proposed in the literature are composed of an increasing number of parameters and complexity. Therefore, finding the optimal trade-off between accuracy and model complexity for a given data set is challenging. To help the search for these suitable configurations, this work proposes using a new Context-Free Grammar associated with a Multi-Objective Grammatical Evolution Algorithm that generates suitable CNNs for a given image classification problem. In this structure, the new grammar maps every possible search space for the creation of networks. Furthermore, the Multi-Objective Grammatical Evolution Algorithm used optimizes this search taking into account two objective functions: accuracy and F-1-score. Our proposal was used in three medical image datasets from MedMNIST: PathMNIST, OCTMNIST, and OrganMNIST Axial. The results showed that our method generated simpler networks with equal or superior performance from state-of-the-art (more complex) networks and others CNNs also generated by grammatical evolution process.

关键词： Grammatical Evolution Deep Neural Networks Multi-Objective Optimization

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Reducing the need for bounding box annotations in Object Detection using image Classification data 34

Reducing the need for bounding box annotations in Object Det...

引用

34th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Blanger, Leonardo Hirata, Nina S. T. Jiang, Xiaoyi Neuralmind AI Campinas SP Brazil Univ Sao Paulo Inst Math & Stat Sao Paulo Brazil Univ Munster Fac Math & Comp Sci Munster Germany

ISBN: (纸本)9781665423540

We address the problem of training Object Detection models using significantly less bounding box annotated images. For that, we take advantage of cheaper and more abundant image classification data. Our proposal consists in automatically generating artificial detection samples, with no need of expensive detection level supervision, using images with classification labels only. We also detail a pretraining initialization strategy for detection architectures using these artificially synthesized samples, before finetuning on real detection data, and experimentally show how this consistently leads to more data efficient models. With the proposed approach, we were able to effectively use only classification data to improve results on the harder and more supervision hungry object detection problem. We achieve results equivalent to those of the full data scenario using only a small fraction of the original detection data for Face, Bird, and Car detection.

关键词： Training graphics Annotations Object detection Birds Data models Proposals

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Analyzing the Effects of Dimensionality Reduction for Unsupervised Domain Adaptation 34

Analyzing the Effects of Dimensionality Reduction for Unsupe...

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34th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Lopes Junior, Renato Sergio Schwartz, William Robson Fed Univ Minas Gerais UFMG Dept Comp Sci Smart Sense Lab Belo Horizonte MG Brazil

ISBN: (纸本)9781665423540

Deep neural networks are extensively used for solving a variety of computer vision problems. However, in order for these networks to obtain good results, a large amount of data is necessary for training. In image classification, this training data consists of images and labels that indicate the class portrayed by each image. Obtaining this large labeled dataset is very time and resource consuming. Therefore, domain adaptation methods allow different, but semantic-related, datasets that are already labeled to be used during training, thus eliminating the labeling cost. In this work, the effects of embedding dimensionality reduction in a state-of-the-art domain adaptation method are analyzed. Furthermore, we experiment with a different approach that use the available data from all domains to compute the confidence of pseudo-labeled samples. We show through experiments in commonly used datasets that, in fact, the proposed modifications led to better results in the target domain in some scenarios.

关键词： Training Dimensionality reduction graphics Deep learning computer vision Costs Training data

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Representation Learning for image Retrieval through 3D CNN and Manifold Ranking 34

Representation Learning for Image Retrieval through 3D CNN a...

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34th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： de Almeida, Lucas Barbosa Pereira-Ferrero, Vanessa Helena Valem, Lucas Pascotti Almeida, Jurandy Guimaraes Pedronette, Daniel Carlos Sao Paulo State Univ UNESP Dept Stat Appl Math & Comp DEMAC Rio Claro Brazil Fed Univ Sao Paulo UNIFESP Inst Sci & Technol Sao Jose Dos Campos SP Brazil

ISBN: (纸本)9781665423540

Despite of the substantial success of Convolutional Neural Networks (CNNs) on many recognition and representation tasks, such models are very reliant on huge amount of data to allow effective training. In order to improve the generalization ability of CNNs, several approaches have been proposed, including variations of data augmentation strategies. With the goal of achieving more effective retrieval results on unsupervised learning scenarios, we propose a representation learning approach which exploits a rank-based formulation to build a more comprehensive data representation. The proposed model uses 2D and 3D CNNs trained by transfer learning and fuse both representations through a rank-based formulation based on manifold learning algorithms. Our approach was evaluated on an unsupervised image retrieval scenario applied to action recognition datasets. The experimental results indicated that significant effectiveness gains can be obtained on various datasets, reaching +56.93% of relative gains on MAP scores.

关键词： Representation learning Training Manifolds Solid modeling Three-dimensional displays image retrieval Transfer learning

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An Investigation of 2D Keypoints Detection on Challenging Scenarios Using Depthwise Separable Convolutions: A Hand Pose Estimation Case Study 34

An Investigation of 2D Keypoints Detection on Challenging Sc...

引用

34th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Costa, Willams Figueiredo, Lucas Teixeira, Joao Marcelo Lima, Joao Paulo Teichrieb, Veronica Univ Fed Pernambuco Ctr Informat Voxar Labs Recife PE Brazil Univ Fed Rural Pernambuco Dept Comp Recife PE Brazil

ISBN: (纸本)9781665423540

2D keypoints detection is a computer vision task applicable to several fields such as hand, face, and body tracking, which provides useful information for spatial analytics, gestural interactions, and augmented reality applications. This work investigates the usage of depthwise separable convolutions (an optimized convolution operation) to speed up the inference time on a largely used architecture for 2D keypoints estimation. We evaluate the impacts on the precision and performance of such optimization on a hand pose estimation task. We also extend the evaluation towards simulated challenging scenarios of defocused lens, motion blur, occlusions, and noisy images to understand how these stress situations affect both the original and the optimized architectures. We show that the execution time can be improved on average by 12.8% with an accuracy compromise of less than 1 pixel (mean EPE). The experiments on challenging scenarios revealed that the model powered by depthwise separable convolutions is most fit for the occlusion cases and noisy environments while suffering more on the motion blur simulated scenarios.

关键词： graphics computer vision Pose estimation computer architecture Noise measurement Task analysis Stress

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Learning to Segment Medical images from Few-Shot Sparse Labels 34

Learning to Segment Medical Images from Few-Shot Sparse Labe...

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34th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Gama, Pedro H. T. Oliveira, Hugo dos Santos, Jefersson A. Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil Univ Sao Paulo Inst Math & Stat Sao Paulo Brazil

ISBN: (纸本)9781665423540

In this paper, we propose a novel approach for few-shot semantic segmentation with sparse labeled images. We investigate the effectiveness of our method, which is based on the Model-Agnostic Meta-Learning (MAML) algorithm, in the medical scenario, where the use of sparse labeling and few-shot can alleviate the cost of producing new annotated datasets. Our method uses sparse labels in the meta-training and dense labels in the meta-test, thus making the model learn to predict dense labels from sparse ones. We conducted experiments with four Chest X-Ray datasets to evaluate two types of annotations (grid and points). The results show that our method is the most suitable when the target domain highly differs from source domains, achieving Jaccard scores comparable to dense labels, using less than 2% of the pixels of an image with labels in few-shot scenarios.

关键词： graphics image segmentation Costs Annotations Semantics Predictive models Prediction algorithms

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Depth-of-Field Guided Rendering for Light Field Displays 22

Depth-of-Field Guided Rendering for Light Field Displays

引用

IS and T International symposium on Electronic Imaging 2024: 22nd image processing: Algorithms and Systems, IPAS 2024

作者： Akbar, Kamran Bregovic, Robert Tampere University Tampere Finland

Light field displays have a finite angular and spatial resolution, which limits the display's depth-of-field, that is, the depth range around the display screen in which the display can visualize a 3D scene with the maximum spatial resolution. This limitation causes aliasing artifacts in the parts of the scene that are outside of that range, resulting in a distorted appearance. The aliasing artifacts can be mitigated by properly blurring those parts, with blurring preferably done at the rendering stage. Though methods for rendering a single view with a correct depth of field exist, using those methods for rendering a large light field is computationally heavy. In this paper we propose a method for simultaneously rendering multiple adjacent views in a light field, with each of them having the required depth of field. By means of examples, we show that the proposed method can render a desired light field several times faster than methods for rendering a single view, without compromising on the overall rendered quality. © 2024, Society for Imaging Science and Technology.

关键词： Rendering (computer graphics)

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Few-shot Retinal Disease Classification on the brazilian Multilabel Ophtalmological Dataset

Few-shot Retinal Disease Classification on the Brazilian Mul...

引用

brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Gabriel J. Perin Nina S. T. Hirata Institute of Mathematics and Statistics University of São Paulo

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Motivated by the inherent data scarcity in the medical domain, this work studies few-shot retinal disease classification’ using the brazilian Multilabel Ophtalmological Dataset. We compare different network architectures and non-trivial data augmentations under the application of the Reptile Algorithm, conducting quantitative and qualitative analysis. Regarding the architectures, we observe that Swin outperforms ViT and ResNet. We also observe that clever data augmentations not only improve performance, but can also generate prediction confidence distributions that are more interpretable and trustworthy. Further-more, pre-training the models with domain-specific data leads to superior ability of the models to detect the relevant patterns in the images. Code is available at ***/gabjp/few-shot-BRSET.

关键词： Metalearning Heating systems Training Network architecture Data augmentation Retina Prediction algorithms Data models Classification algorithms Diseases

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Bias and Fairness in Face Detection 34

Bias and Fairness in Face Detection

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34th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Menezes, Hanna F. Ferreira, Arthur S. C. Pereira, Eanes T. Gomes, Herman M. Univ Fed Campina Grande Acad Unity Syst & Comp Campina Grande Paraiba Brazil

ISBN: (纸本)9781665423540

processing of face images is used in many areas, for example: commercial applications such as video-games;facial biometrics;facial expression recognition, etc. Face detection is a crucial step for any system that processes face images. Therefore, if there is bias or unfairness in this first step, all the processing steps that follow may be compromised. Errors in automatic face detection may be harmful to people as, for instance, in situations where a decision may limit or restrict their freedom to come and go. Therefore, it is crucial to investigate the existence of these errors caused due to bias or unfairness. In this paper, an analysis of five well-known top accuracy face detectors is performed to investigate the presence of bias and unfairness in their results. Some of the metrics used to identify the existence of bias and unfairness involved the verification of demographic parity, verification of existence of false positives and/or false negatives, rate of positive prediction, and verification of equalized odds. Data from about 365 different individuals were randomly selected from the Facebook Casual Conversations Dataset, resulting in approximately 5,500 videos, providing 550,000 frames used for face detection in the performed experiments. The obtained results show that all five face detectors presented a high risk of not detecting faces from the female gender and from people between 46 and 85 years old. Furthermore, the skin tone groups related with dark skin are the groups pointed out with highest risk of faces not being detected for four of the five evaluated face detectors. This paper points out the necessity of the research community to engage in breaking the perpetuation of injustice that may be present in datasets or machine learning models.

关键词： Measurement graphics image recognition Social networking (online) Face recognition Detectors Machine learning

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