2021 IEEE International Conference on Image Processing (ICIP)最新文献_第9页

GI-AEE: GAN Inversion Based Attentive Expression Embedding Network For Facial Expression Editing 基于GAN反演的面部表情关注嵌入网络

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506434

Yun Zhang, R. Liu, Yifan Pan, Dehao Wu, Yuesheng Zhu, Zhiqiang Bai

{"title":"GI-AEE: GAN Inversion Based Attentive Expression Embedding Network For Facial Expression Editing","authors":"Yun Zhang, R. Liu, Yifan Pan, Dehao Wu, Yuesheng Zhu, Zhiqiang Bai","doi":"10.1109/ICIP42928.2021.9506434","DOIUrl":"https://doi.org/10.1109/ICIP42928.2021.9506434","url":null,"abstract":"Facial expression editing aims to modify facial expression by specific conditions. Existing methods adopt an encoder-decoder architecture under the guidance of expression condition to process the desired expression. However, these methods always tend to produce artifacts and blurs in expression-intensive regions due to simultaneously modifying images in expression changed regions and ensuring the consistency of other attributes with the source image. To address these issues, we propose a GAN inversion based Attentive Expression Embedding Network (GI-AEE) for facial expression editing, which decouples this task utilizing GAN inversion to alleviate the strong effect of the source image on the target image and produces high-quality expression editing results. Furthermore, different from existing methods that directly embed the expression condition into the network, we propose an Attentive Expression Embedding module to embed corresponding expression vectors into different facial regions, producing more plausible results. Qualitative and quantitative experiments demonstrate our method outperforms the state-of-the-art expression editing methods.","PeriodicalId":314429,"journal":{"name":"2021 IEEE International Conference on Image Processing (ICIP)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132184178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Choose Settings Carefully: Comparing Action Unit Detection At Different Settings Using A Large-Scale Dataset 仔细选择设置:使用大规模数据集比较不同设置下的动作单元检测

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506757

M. Bishay, Ahmed Ghoneim, M. Ashraf, Mohammad Mavadati

引用次数: 2

An Adversarial Collaborative-Learning Approach for Corneal Scar Segmentation with Ocular Anterior Segment Photography 基于眼前段摄影的对抗性合作学习方法在角膜疤痕分割中的应用

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506621

Ke Wang, Guangyu Wang, Kang Zhang, Ting Chen

{"title":"An Adversarial Collaborative-Learning Approach for Corneal Scar Segmentation with Ocular Anterior Segment Photography","authors":"Ke Wang, Guangyu Wang, Kang Zhang, Ting Chen","doi":"10.1109/ICIP42928.2021.9506621","DOIUrl":"https://doi.org/10.1109/ICIP42928.2021.9506621","url":null,"abstract":"Corneal scarring is a common eye disease that leads to reduced vision. An accurate diagnosis and segmentation of corneal scar is a critical in ensuring proper treatment. Deep neural networks have made great progress in medical image segmentation, but the training requires large amount of annotated data. Pixel-level corneal scar can only be annotated by experienced ophthalmologists, but eye structure annotation can be done easily by people with minimal medical knowledge. In this paper, we propose Dual-Eye-GAN Net (DEGNet), an end-to-end adversarial collaborative-learning corneal scar segmentation model. DEG-Net can improve segmentation quality with additional data that only has eye structure annotation. We collect the first corneal scar segmentation dataset in the form of anterior ocular photography. Experimental results demonstrate superiority to both supervised and semi-supervised approaches. This is the first empirical study on corneal scar segmentation with anterior ocular photography. The code and dataset can be found in https://github.com/kaisadadi/Dual-GAN-Net.","PeriodicalId":314429,"journal":{"name":"2021 IEEE International Conference on Image Processing (ICIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130978803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Two-Phase Multimodal Image Fusion Using Convolutional Neural Networks 基于卷积神经网络的两相多模态图像融合

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506703

Kushal Kusram, S. Transue, Min-Hyung Choi

引用次数: 0

From Gradient Leakage To Adversarial Attacks In Federated Learning 从梯度泄漏到联邦学习中的对抗性攻击

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506589

Jia Qi Lim, Chee Seng Chan

引用次数: 9

Mind The Structure: Adopting Structural Information For Deep Neural Network Compression 关注结构:采用结构信息进行深度神经网络压缩

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506102

Homayun Afrabandpey, Anton Muravevy, H. R. Tavakoli, Honglei Zhang, Francesco Cricri, M. Gabbouj, Emre B. Aksu

{"title":"Mind The Structure: Adopting Structural Information For Deep Neural Network Compression","authors":"Homayun Afrabandpey, Anton Muravevy, H. R. Tavakoli, Honglei Zhang, Francesco Cricri, M. Gabbouj, Emre B. Aksu","doi":"10.1109/ICIP42928.2021.9506102","DOIUrl":"https://doi.org/10.1109/ICIP42928.2021.9506102","url":null,"abstract":"Deep neural networks have huge number of parameters and require large number of bits for representation. This hinders their adoption in decentralized environments where model transfer among different parties is a characteristic of the environment while the communication bandwidth is limited. Parameter quantization is a compression approach to address this challenge by reducing the number of bits required to represent a model, e.g. a neural network. However, majority of existing neural network quantization methods do not exploit structural information of layers and parameters during quantization. In this paper, focusing on Convolutional Neural Networks (CNNs), we present a novel quantization approach by employing the structural information of neural network layers and their corresponding parameters. Starting from a pre-trained CNN, we categorize network parameters into different groups based on the similarity of their layers and their spatial structure. Parameters of each group are independently clustered and the centroid of each cluster is used as representative for all parameters in the cluster. Finally, the centroids and the cluster indexes of the parameters are used as a compact representation of the parameters. Experiments with two different tasks, i.e., acoustic scene classification and image compression, demonstrate the effectiveness of the proposed approach.","PeriodicalId":314429,"journal":{"name":"2021 IEEE International Conference on Image Processing (ICIP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125394251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Augmenting 3D Ultrasound Strain Elastography by combining Bayesian inference with local Polynomial fitting in Region-growing-based Motion Tracking 基于区域生长运动跟踪的贝叶斯推理与局部多项式拟合增强三维超声应变弹性图

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506520

Shuojie Wen, Bo Peng, Hao Jiang, Junkai Cao, Jingfeng Jiang

{"title":"Augmenting 3D Ultrasound Strain Elastography by combining Bayesian inference with local Polynomial fitting in Region-growing-based Motion Tracking","authors":"Shuojie Wen, Bo Peng, Hao Jiang, Junkai Cao, Jingfeng Jiang","doi":"10.1109/ICIP42928.2021.9506520","DOIUrl":"https://doi.org/10.1109/ICIP42928.2021.9506520","url":null,"abstract":"Accurately tracking large tissue motion over a sequence of ultrasound images is critically important to several clinical applications including, but not limited to, elastography, flow imaging, and ultrasound-guided motion compensation. However, tracking in vivo large tissue deformation in 3D is a challenging problem and requires further developments. In this study, we explore a novel tracking strategy that combines Bayesian inference with local polynomial fitting. Since this strategy is incorporated into a region-growing block-matching motion tracking framework we call this strategy a Bayesian region-growing motion tracking with local polynomial fitting (BRGMTLPF) algorithm. More specifically, unlike a conventional block-matching algorithm, we use a maximum posterior probability density function to determine the “correct” three-dimensional displacement vector.The proposed BRGMT-LPF algorithm was evaluated using a tissue-mimicking phantom and ultrasound data acquired from a pathologically-confirmed human breast tumor. The in vivo ultrasound data was acquired using a 3D whole breast ultrasound scanner, while the tissue-mimicking phantom was acquired using an experimental CMUT ultrasound transducer. To demonstrate the effectiveness of combining Bayesian inference with local Polynomial fitting, the proposed method was compared to the original region-growing motion tracking algorithm (RGMT), region-growing with Bayesian interference only (BRGMT), and region-growing with local polynomial fitting (RGMT-LPF). Our preliminary data demonstrate that the proposed BRGMT-LPF algorithm can improve the accuracy of motion tracking.","PeriodicalId":314429,"journal":{"name":"2021 IEEE International Conference on Image Processing (ICIP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125449293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

CMID: A New Dataset for Copy-Move Forgeries on ID Documents CMID:一个新的ID文件复制-移动伪造数据集

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506723

Gaël Mahfoudi, F. Morain-Nicolier, F. Retraint, M. Pic

引用次数: 1

GSLD: A Global Scanner with Local Discriminator Network for Fast Detection of Sparse Plasma Cell in Immunohistochemistry GSLD:基于局部鉴别网络的免疫组织化学稀疏浆细胞快速检测的全局扫描仪

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506782

Qi Zhang, Zhu Meng, Zhicheng Zhao, Fei Su

引用次数: 1

Knowledge Transferred Fine-Tuning for Anti-Aliased Convolutional Neural Network in Data-Limited Situation 数据有限情况下抗锯齿卷积神经网络的知识转移微调

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506696

Satoshi Suzuki, Shoichiro Takeda, Ryuichi Tanida, H. Kimata, Hayaru Shouno

{"title":"Knowledge Transferred Fine-Tuning for Anti-Aliased Convolutional Neural Network in Data-Limited Situation","authors":"Satoshi Suzuki, Shoichiro Takeda, Ryuichi Tanida, H. Kimata, Hayaru Shouno","doi":"10.1109/ICIP42928.2021.9506696","DOIUrl":"https://doi.org/10.1109/ICIP42928.2021.9506696","url":null,"abstract":"Anti-aliased convolutional neural networks (CNNs) introduce blur filters to intermediate representations in CNNs to achieve high accuracy. A promising way to build a new antialiased CNN is to fine-tune a pre-trained CNN, which can easily be found online, with blur filters. However, blur filters drastically degrade the pre-trained representation, so the fine-tuning needs to rebuild the representation by using massive training data. Therefore, if the training data is limited, the fine-tuning cannot work well because it induces overfitting to the limited training data. To tackle this problem, this paper proposes “knowledge transferred fine-tuning”. On the basis of the idea of knowledge transfer, our method transfers the knowledge from intermediate representations in the pre-trained CNN to the anti-aliased CNN while fine-tuning. We transfer only essential knowledge using a pixel-level loss that transfers detailed knowledge and a global-level loss that transfers coarse knowledge. Experimental results demonstrate that our method significantly outperforms the simple fine-tuning method.","PeriodicalId":314429,"journal":{"name":"2021 IEEE International Conference on Image Processing (ICIP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126266837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1