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共 2188 条记录，以下是551-560 订阅

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Multi-scale Residual Pyramid Attention Network for Monocular Depth Estimation 25

Multi-scale Residual Pyramid Attention Network for Monocular...

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25th International conference on pattern recognition (ICPR)

作者： Liu, Jing Zhang, Xiaona Li, Zhaoxin Mao, Tianlu Hebei Normal Univ Coll Comp & Cyber Secur Shijiazhuang Hebei Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Key Lab Mobile Comp & Pervas Device Beijing Peoples R China

ISBN: (纸本)9781728188089

Monocular depth estimation is a challenging problem in computer vision and is crucial for understanding 3D scene geometry. Recently, deep convolutional neural networks (DCNNs) based methods have improved the estimation accuracy significantly. However, existing methods fail to consider complex textures and geometries in scenes, thereby resulting in loss of local details, distorted object boundaries, and blurry reconstruction. In this paper, we proposed an end-to-end multi-scale residual pyramid attention network (MRPAN) to mitigate these problems. First, we propose a multi-scale attention context aggregation (MACA) module, which consists of spatial attention module (SAM) and global attention module (GAM). By considering the position and scale correlation of pixels from spatial and global perspectives, the proposed module can adaptively learn the similarity between pixels so as to obtain more global context information of the image and recover complex structures in the scene. then we proposed an improved residual refinement module (RRM) to further refine the scene structure, giving rise to deeper semantic information and retain more local details. Experimental results show that our method achieves more promising performance in object boundaries and local details compared with other state-of-the-art methods.

关键词： monocular depth estimation convolutional neural networks multi-scale attention module context

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Segmentation of thyroid Nodules Based on Hyperspectral Images

Segmentation of Thyroid Nodules Based on Hyperspectral Image...

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IEEE Joint International Information Technology and Artificial Intelligence conference (ITAIC)

作者： Junjie Wang Chenglong Tao Jian Du Bingliang Hu Zhoufeng Zhang Chinese Academy of Sciences Xi'an Institute of Optics and Precision Mechanics Xi'an China University of Chinese Academy of Sciences Beijing China Key Laboratory of Biomedical Spectroscopy of Xi'an Xi'an China

thyroid nodules are a common thyroid disease, and early detection and treatment have a significant impact on the prognosis of patients. Hyperspectral image technology has significant advantages in medical image analysis due to its high-resolution and multi band characteristics, especially in the recognition and classification of thyroid nodules, which has important application value. But currently, there is no very effective method for segmenting hyperspectral images of thyroid nodules. Deep learning algorithms have shown superior performance in recent years in computer vision and pattern recognition tasks, including medical image analysis. this paper proposes the use of VGG model for segmentation of hyperspectral images of thyroid nodule tissue. Hyperspectral images can obtain spatial and spectral information of organizations, and VGG models can segment images through deep networks. Using hyperspectral and VGG, we found that thyroid nodule tissue can be well distinguished at the pixel level.

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10th China Health Information Processing conference, CHIP 2024

10th China Health Information Processing Conference, CHIP 20...

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10th China Health Information Processing conference, CHIP 2024

ISBN: (纸本)9789819642977

the proceedings contain 32 papers. the special focus in this conference is on China Health Information Processing. the topics include: Predicting Patients’ Physician Selection Behavior Based on Multimodal Data: A Comparison of Feature Engineering and End-to-End Approaches;Automatic Pancreatitis CT Image Segmentation Model Based on UNet and SE Module;utilizing Retrieval-Augmented Generation for Open-Domain Question Answering in Healthcare;self-supervised Learning Driven Doctor Recommendation Model: Combining Communication Ability and Professional Competence;prompt-Aware Large Language Model for Sleep Stages Classification;a Method for the Classification Public Health Questions Based on Model Ensemble and Voting Mechanism;A 3D MRI Brain Image Segmentation and Reconstruction System Based on Augmented Reality Technology;Analysis of Hospitalization Data Before, During and After the COVID-19 Epidemic Using Short Time Series Clustering;look, Imitate and Refine: A Hierarchical Multimodel Retrieval Augmented vision-Language Model for Radiology Report Generation;CTGLM: A vision-Language Model for Automated chinese Chest CT Report Generation;pattern Matching of Positive and Negative DNA Sequences with General Gaps and One-off Constraints;survey of Suicidal Tendency recognition Based on Social Media;global Trends in the Application of Extended Reality Technology to Autism Research: Based on the Web of Bibliometric Analysis of Science (1999.1.1–2023.3.4);a Review of Machine Learning-Based Assessment of Depression.

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10th China Health Information Processing conference, CHIP 2024

10th China Health Information Processing Conference, CHIP 20...

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10th China Health Information Processing conference, CHIP 2024

ISBN: (纸本)9789819637515

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10th China Health Information Processing conference, CHIP 2024

10th China Health Information Processing Conference, CHIP 20...

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10th China Health Information Processing conference, CHIP 2024

ISBN: (纸本)9789819637546

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DRSGN: Dual Revised Semantic Graph Structured Network for Image-Text Matching 7

DRSGN: Dual Revised Semantic Graph Structured Network for Im...

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7th IEEE International conference on Cloud Computing and Intelligence Systems, CCIS 2021

作者： Yang, Xiao Wu, Xiaojun Xu, Tianyang Jiangnan University School of Artificial Intelligence and Computer Science Wuxi214122 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

ISBN: (纸本)9781665441490

Image-Text matching has made significant contributions to bridge the gap in multi-modal retrieval for advanced pattern recognition systems. the key point of this task is mapping images and text semantics into a common space, where the correlations of similar pairs and dissimilar ones are distinguishable. To concentrate on obtaining semantics, most of existing methods utilize an external object detector to extract compatible region-level visual representations to match the word-level textual clues in captions. In addition to perform the local region matching, several recent studies design a classification task using global information in the final matching layer, but such global supervision signal is difficult to transmitted to the shallow feature descriptor learning layer. To address this issue, in this paper, we propose a novel Dual Revised Semantic Graph Structured Network (DRSGN) to supplement global supervision to the regional semantics adaptively in the shallow layers. In principle, DRSGN integrates regional and global descriptors to formulate an attention supervising mechanism, aiming to simultaneously highlight the regional instances and global scene to obtain complementary visual clues. A dual semantic supervising module is then established to interact between two modalities to extract the genuine matching pairs. Finally, a semantic graph consisting of the obtained multi-modal clues is designed to perform similarity reasoning between the positional relation embedded textual nodes and semantic related visual nodes. the dedicated global signals provide complementary supervision against local regions to support improved matching capacity. the experimental results on Flikr30K and MSCOCO demonstrate the effectiveness of the proposed DRSGN, with improved matching performance against the local region-based approaches. © 2021 IEEE.

关键词： Semantics

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A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction 4th

A Point Cloud Generative Model via Tree-Structured Graph Con...

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4th chinese conference on pattern recognition and computer vision (PRCV)

作者： Hu, Bowen Lei, Baiying Shen, Yanyan Liu, Yong Wang, Shuqiang Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Guangdong Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shenzhen Univ Shenzhen Guangdong Peoples R China Renmin Univ China Beijing Peoples R China

ISBN: (纸本)9783030880071;9783030880064

Fusing medical images and the corresponding 3D shape representation can provide complementary information and microstructure details to improve the operational performance and accuracy in brain surgery. However, compared to the substantial image data, it is almost impossible to obtain the intraoperative 3D shape information by using physical methods such as sensor scanning, especially in minimally invasive surgery and robot-guided surgery. In this paper, a general generative adversarial network (GAN) architecture based on graph convolutional networks is proposed to reconstruct the 3D point clouds (PCs) of brains by using one single 2D image, thus relieving the limitation of acquiring 3D shape data during surgery. Specifically, a tree-structured generative mechanism is constructed to use the latent vector effectively and transfer features between hidden layers accurately. With the proposed generative model, a spontaneous image-to-PC conversion is finished in real-time. Competitive qualitative and quantitative experimental results have been achieved on our model. In multiple evaluation methods, the proposed model outperforms another common point cloud generative model PointOutNet.

关键词： 3D reconstruction Generative adversarial network Graph convolutional network Point cloud

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Multi-view 3D Human Physique Dataset Construction For Robust Digital Human Modeling of Natural Scenes 8

Multi-view 3D Human Physique Dataset Construction For Robust...

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8th International conference on Communication and Information Processing, ICCIP 2022

作者： Lin, Weitao Zhang, Jiguang Zhang, Zhaohui Xu, Shibiao Xu, Hao Zhang, Xiaopeng School of Artificial Intelligence University of Chinese Academy of Sciences China China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China Co. Ltd China

ISBN: (纸本)9781450397100

A large number of diverse data sets are necessary for networks to predict human body parameters and reconstruct 3D body models from images. Due to the high cost of motion capture and body scanning, high precision pose and body shape parameters are difficult to obtain. Meanwhile, existing datasets cannot meet the requirements in terms of diversity, size, and data accuracy for practical applications. Inspired by the construction schemes of various datasets, we design and construct a large multi-view 3D human body reconstruction dataset (3DMVHumanBP) with more types of supervised data. By recording the different poses of 25 women and 25 men in a green screen laboratory from six perspectives, we constructed a complete large multi-view 3D body posture dataset containing 340, 000 images. It is worth noting that, we innovatively propose a body dimension prior to the constrained human parametric model construction strategy to provide high-precision ground truth parameters of the human body SMPL models. In addition, we also designed a dense UV data generation method based on human body boundary and mask mapping to provide high-quality dense UV data, which more closely fits the features of the human images. It makes up for the defect that few existing data sets can only provide sparse UV data. In the experiment, the effectiveness and advantages of the data set constructed by us in network training are verified. Compared with the training of existing datasets, the mainstream network models trained on our datasets can significantly improve their prediction accuracy and robustness, thanks to the monitoring data of multiple kinds of high-precision human model parameters provided by 3DMVHumanBP. We hope that the human body dataset construction scheme we designed can provide ideas for building large-scale high precision human body datasets in the future. © 2022 Owner/Author.

关键词： computer vision

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Emotion recognition using multimodal matchmap fusion and multi-task learning 11

Emotion recognition using multimodal matchmap fusion and mul...

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11th International conference of pattern recognition Systems, ICPRS 2021

作者： Pizarro, Ricardo Bekios-Calfa, Juan Universidad Católica del Norte Chile Antofagasta Chile

ISBN: (纸本)9781839534300

Emotion recognition is a complex task due to the great intra-class and inter-class variability that exists implicitly in the problem. From the point of view of the intra-class, an emotion can be expressed by different people, which generates different representations of it. For the inter-class case, there are some kinds of emotions that are alike. Traditionally, the problem has been approached in different ways, highlighting the analysis of images to determine the facial expression of a person to extrapolate it to a type of emotion, also, the use of audio sequences to estimate the emotion of the speaker. the present work seeks to solve this problem using multimodal techniques, multitask and Deep Learning. To help with these problems, the use of a fusion method based on the similarity between audio and video modalities will be investigated and applied to the emotion classification problem. the use of this method allows the use of auxiliary tasks that enhance the learned relationships between the emotions shown in video frames and audio frames belonging to the same emotion label and punish those that are different. the results show that when using the fusion method based on the similarity of modalities together with the use of multiple tasks, the classification is improved by 7% with respect to the classification obtained in the baseline model that uses concatenation of the characteristics of each modality, the experiments are performed on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) database. © 2021 Institution of Engineering and Technology. All rights reserved.

关键词： computer vision

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chinese Text Classification Using BERT and Flat-Lattice Transformer 11th

Chinese Text Classification Using BERT and Flat-Lattice Tran...

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11th International conference on Artificial Intelligence and Mobile Services, AIMS 2022 held as Part of the Services conference Federation, SCF 2022

作者： Lv, Haifeng Ning, Yishuang Ning, Ke Ji, Xiaoyu He, Sheng Guangxi Key Laboratory of Machine Vision and Intelligent Control WuZhou University Wuzhou China Kingdee Research Kingdee International Software Group Company Limited Shenzhen China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou543002 China

ISBN: (纸本)9783031235030

Recently, large scale pre-trained language models such as BERT and models with lattice structure that consisting of character-level and word-level information have achieved state-of-the-art performance in most downstream natural language processing (NLP) tasks, including named entity recognition (NER), English text classification and sentiment analysis. For chinese text classification, the existing methods have also tried such kinds of models. However, they cannot obtain the desired results since these pre-trained models are based on characters, which cannot be applied for chinese language that is based on words. To address this problem, in this paper, we propose BFLAT which a simple but efficient model for chinese text classification. Specifically, BFLAT utilizes BERT and word2vec to learn character-level and word-level vector representations, and then adopts the flat-lattice transformer to integrate both of the two-level vector representations. Experimental results on two datasets demonstrate that our proposed method outperforms the baseline methods over 1.38–21.82% and 3.42–20.7% in terms of relative F1-measure on two chinese text classification benchmarks, respectively. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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