2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)最新文献_第5页

Learning Photometric Stereo via Manifold-based Mapping 通过基于流形的映射学习光度立体

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301860

Yakun Ju, Muwei Jian, Junyu Dong, K. Lam

{"title":"Learning Photometric Stereo via Manifold-based Mapping","authors":"Yakun Ju, Muwei Jian, Junyu Dong, K. Lam","doi":"10.1109/VCIP49819.2020.9301860","DOIUrl":"https://doi.org/10.1109/VCIP49819.2020.9301860","url":null,"abstract":"Three-dimensional reconstruction technologies are fundamental problems in computer vision. Photometric stereo recovers the surface normals of a 3D object from varying shading cues, prevailing in its capability for generating fine surface normal. In recent years, deep learning-based photometric stereo methods are capable of improving the surface-normal estimation under general non-Lambertian surfaces, due to its powerful fitting ability on the non-Lambertian surface. These state-of-the-art methods however usually regress the surface normal directly from the high-dimensional features, without exploring the embedded structural information. This results in the underutilization of the information available in the features. Therefore, in this paper, we propose an efficient manifold-based framework for learning-based photometric stereo, which can better map combined high-dimensional feature spaces to low-dimensional manifolds. Extensive experiments show that our method, learning with the low-dimensional manifolds, achieves more accurate surface-normal estimation, outperforming other state-of-the-art methods on the challenging DiLiGenT benchmark dataset.","PeriodicalId":431880,"journal":{"name":"2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124545585","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: 8

Power/QoS-Adaptive HEVC FME Hardware using Machine Learning-Based Approximation Control 基于机器学习的近似控制的功率/ qos自适应HEVC FME硬件

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301797

Wagner Penny, D. Palomino, M. Porto, B. Zatt

引用次数: 0

Sensitivity-Aware Bit Allocation for Intermediate Deep Feature Compression 基于灵敏度感知的中深度特征压缩位分配

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301807

Yuzhang Hu, Sifeng Xia, Wenhan Yang, Jiaying Liu

{"title":"Sensitivity-Aware Bit Allocation for Intermediate Deep Feature Compression","authors":"Yuzhang Hu, Sifeng Xia, Wenhan Yang, Jiaying Liu","doi":"10.1109/VCIP49819.2020.9301807","DOIUrl":"https://doi.org/10.1109/VCIP49819.2020.9301807","url":null,"abstract":"In this paper, we focus on compressing and trans-mitting deep intermediate features to support the prosperous applications at the cloud side efficiently, and propose a sensitivity-aware bit allocation algorithm for the deep intermediate feature compression. Considering that different channels’ contributions to the final inference result of the deep learning model might differ a lot, we design a channel-wise bit allocation mechanism to maintain the accuracy while trying to reduce the bit-rate cost. The algorithm consists of two passes. In the first pass, only one channel is exposed to compression degradation while other channels are kept as the original ones in order to test this channel’s sensitivity to the compression degradation. This process will be repeated until all channels’ sensitivity is obtained. Then, in the second pass, bits allocated to each channel will be automatically decided according to the sensitivity obtained in the first pass to make sure that the channel with higher sensitivity can be allocated with more bits to maintain accuracy as much as possible. With the well-designed algorithm, our method surpasses state-of-the-art compression tools with on average 6.4% BD-rate saving.","PeriodicalId":431880,"journal":{"name":"2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128664469","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: 4

Network Update Compression for Federated Learning 联邦学习的网络更新压缩

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301815

B. Kathariya, Li Li, Zhu Li, Ling-yu Duan, Shan Liu

{"title":"Network Update Compression for Federated Learning","authors":"B. Kathariya, Li Li, Zhu Li, Ling-yu Duan, Shan Liu","doi":"10.1109/VCIP49819.2020.9301815","DOIUrl":"https://doi.org/10.1109/VCIP49819.2020.9301815","url":null,"abstract":"In federated learning setting, models are trained in a variety of edge-devices with locally generated data and each round only updates in the current model rather than the model itself are sent to the server where they are aggregated to compose an improved model. These edge devices, however, reside in highly uneven nature of network with higher latency and lower-throughput connections and are intermittently available for training. In addition, a network connection has an asymmetric nature of downlink and uplink. All these contribute to a major challenge while synchronizing these updates to the server.In this work, we proposed an efficient c oding s olution to significantly r educe u plink c ommunication c ost b y r educing the total number of parameters required for updates. This was achieved by applying Gaussian Mixture Model (GMM) to localize Karhunen–Loève Transform (KLT) on inter-model subspace and representing it with two low-rank matrices. Experiments on convolutional neural network (CNN) models showed the proposed model can significantly reduce the uplink communication cost in federated learning while preserving reasonable accuracy.","PeriodicalId":431880,"journal":{"name":"2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115530634","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

Adaptive Color Transform in VVC Standard VVC标准中的自适应颜色变换

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301798

Hong-Jheng Jhu, Xiaoyu Xiu, Yi-Wen Chen, Tsung-Chuan Ma, Xianglin Wang

引用次数: 0

No-Reference Stereoscopic Image Quality Assessment Based on Convolutional Neural Network with A Long-Term Feature Fusion 基于长期特征融合卷积神经网络的无参考立体图像质量评价

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301854

Sumei Li, Mingyi Wang

引用次数: 6

A Discrete Cosine Model of Light Field Sampling for Improving Rendering Quality of Views 一种用于提高视图渲染质量的光场采样离散余弦模型

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301838

Ying Wei, Changjian Zhu, You Yang, Yan Liu

引用次数: 1

A Marked Point Process Model For Visual Perceptual Groups Extraction 视觉感知群提取的标记点过程模型

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301776

A. Mbarki, M. Naouai

引用次数: 0

Two recent advances on normalization methods for deep neural network optimization 深度神经网络优化归一化方法的两个最新进展

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301751

Lei Zhang

引用次数: 0

Learning Matching Behavior Differences for Compressing Vehicle Re-identification Models 压缩车辆再识别模型的学习匹配行为差异

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301869

Yi Xie, Jianqing Zhu, Huanqiang Zeng, C. Cai, Lixin Zheng

{"title":"Learning Matching Behavior Differences for Compressing Vehicle Re-identification Models","authors":"Yi Xie, Jianqing Zhu, Huanqiang Zeng, C. Cai, Lixin Zheng","doi":"10.1109/VCIP49819.2020.9301869","DOIUrl":"https://doi.org/10.1109/VCIP49819.2020.9301869","url":null,"abstract":"Vehicle re-identification matching vehicles captured by different cameras has great potential in the field of public security. However, recent vehicle re-identification approaches exploit complex networks, causing large computations in their testing phases. In this paper, we propose a matching behavior difference learning (MBDL) method to compress vehicle re-identification models for saving testing computations. In order to represent the matching behavior evolution across two different layers of a deep network, a matching behavior difference (MBD) matrix is designed. Then, our MBDL method minimizes the L1 loss function among MBD matrixes from a small student network and a complex teacher network, ensuring the student network use less computations to simulate the teacher network’s matching behaviors. During the testing phase, only the small student network is utilized so that testing computations can be significantly reduced. Experiments on VeRi776 and VehicleID datasets show that MBDL outperforms many state-of-the-art approaches in terms of accuracy and testing time performance.","PeriodicalId":431880,"journal":{"name":"2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124279302","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