2017 IEEE International Conference on Computer Vision Workshops (ICCVW)最新文献_第10页

Know You at One Glance: A Compact Vector Representation for Low-Shot Learning 一眼就知道你:用于低镜头学习的紧凑向量表示

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-01 DOI: 10.1109/ICCVW.2017.227

Yu Cheng, Jian Zhao, Zhecan Wang, Yan Xu, J. Karlekar, Shengmei Shen, Jiashi Feng

{"title":"Know You at One Glance: A Compact Vector Representation for Low-Shot Learning","authors":"Yu Cheng, Jian Zhao, Zhecan Wang, Yan Xu, J. Karlekar, Shengmei Shen, Jiashi Feng","doi":"10.1109/ICCVW.2017.227","DOIUrl":"https://doi.org/10.1109/ICCVW.2017.227","url":null,"abstract":"Low-shot face recognition is a very challenging yet important problem in computer vision. The feature representation of the gallery face sample is one key component in this problem. To this end, we propose an Enforced Softmax optimization approach built upon Convolutional Neural Networks (CNNs) to produce an effective and compact vector representation. The learned feature representation is very helpful to overcome the underlying multi-modality variations and remain the primary key features as close to the mean face of the identity as possible in the high-dimensional feature space, thus making the gallery basis more robust under various conditions, and improving the overall performance for low-shot learning. In particular, we sequentially leverage optimal dropout, selective attenuation, ℓ2 normalization, and model-level optimization to enhance the standard Softmax objective function for to produce a more compact vectorized representation for low-shot learning. Comprehensive evaluations on the MNIST, Labeled Faces in the Wild (LFW), and the challenging MS-Celeb-1M Low-Shot Learning Face Recognition benchmark datasets clearly demonstrate the superiority of our proposed method over state-of-the-arts. By further introducing a heuristic voting strategy for robust multi-view combination, and our proposed method has won the Top-1 place in the MS-Celeb-1M Low-Shot Learning Challenge.","PeriodicalId":149766,"journal":{"name":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","volume":"12 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123795558","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: 42

Deep Learning Anthropomorphic 3D Point Clouds from a Single Depth Map Camera Viewpoint 深度学习拟人化3D点云从单一深度地图相机视点

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-01 DOI: 10.1109/ICCVW.2017.87

Nolan Lunscher, J. Zelek

引用次数: 5

SymmSLIC: Symmetry Aware Superpixel Segmentation SymmSLIC:对称感知超像素分割

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-01 DOI: 10.1109/ICCVW.2017.208

R. Nagar, S. Raman

引用次数: 6

Multi-view 6D Object Pose Estimation and Camera Motion Planning Using RGBD Images 基于RGBD图像的多视图6D目标姿态估计和相机运动规划

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-01 DOI: 10.1109/ICCVW.2017.260

Juil Sock, S. Kasaei, L. Lopes, Tae-Kyun Kim

引用次数: 49

Doppelganger Mining for Face Representation Learning 人脸表征学习的二重身挖掘

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-01 DOI: 10.1109/ICCVW.2017.226

Evgeny Smirnov, A. Melnikov, Sergey Novoselov, Eugene Luckyanets, G. Lavrentyeva

引用次数: 42

Class-Specific Reconstruction Transfer Learning via Sparse Low-Rank Constraint 基于稀疏低秩约束的类特定重构迁移学习

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-01 DOI: 10.1109/ICCVW.2017.116

Shanshan Wang, Lei Zhang, W. Zuo

{"title":"Class-Specific Reconstruction Transfer Learning via Sparse Low-Rank Constraint","authors":"Shanshan Wang, Lei Zhang, W. Zuo","doi":"10.1109/ICCVW.2017.116","DOIUrl":"https://doi.org/10.1109/ICCVW.2017.116","url":null,"abstract":"Subspace learning and reconstruction have been widely explored in recent transfer learning work and generally a specially designed projection and reconstruction transfer matrix are wanted. However, existing subspace reconstruction based algorithms neglect the class prior such that the learned transfer function is biased, especially when data scarcity of some class is encountered. Different from those previous methods, in this paper, we propose a novel reconstruction-based transfer learning method called Class-specific Reconstruction Transfer Learning (CRTL), which optimizes a well-designed transfer loss function without class bias. Using a class-specific reconstruction matrix to align the source domain with the target domain which provides help for classification with class prior modeling. Furthermore, to keep the intrinsic relationship between data and labels after feature augmentation, a projected Hilbert-Schmidt Independence Criterion (pHSIC), that measures the dependency between two sets, is first proposed by mapping the data from original space to RKHS in transfer learning. In addition, combining low-rank and sparse constraints on the class-specific reconstruction coefficient matrix, the global and local data structures can be effectively preserved. Extensive experiments demonstrate that the proposed method outperforms conventional representation-based domain adaptation methods.","PeriodicalId":149766,"journal":{"name":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130702268","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: 7

Variational Robust Subspace Clustering with Mean Update Algorithm 基于均值更新算法的变分鲁棒子空间聚类

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-01 DOI: 10.1109/ICCVW.2017.212

Sergej Dogadov, A. Masegosa, Shinichi Nakajima

引用次数: 0

Dual Structured Convolutional Neural Network with Feature Augmentation for Quantitative Characterization of Tissue Histology 基于特征增强的双结构卷积神经网络定量表征组织组织学

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-01 DOI: 10.1109/ICCVW.2017.10

Mira Valkonen, K. Kartasalo, Kaisa Liimatainen, M. Nykter, Leena Latonen, P. Ruusuvuori

{"title":"Dual Structured Convolutional Neural Network with Feature Augmentation for Quantitative Characterization of Tissue Histology","authors":"Mira Valkonen, K. Kartasalo, Kaisa Liimatainen, M. Nykter, Leena Latonen, P. Ruusuvuori","doi":"10.1109/ICCVW.2017.10","DOIUrl":"https://doi.org/10.1109/ICCVW.2017.10","url":null,"abstract":"We present a dual convolutional neural network (dCNN) architecture for extracting multi-scale features from histological tissue images for the purpose of automated characterization of tissue in digital pathology. The dual structure consists of two identical convolutional neural networks applied to input images with different scales, that are merged together and stacked with two fully connected layers. It has been acknowledged that deep networks can be used to extract higher-order features, and therefore, the network output at final fully connected layer was used as a deep dCNN feature vector. Further, engineered features, shown in previous studies to capture important characteristics of tissue structure and morphology, were integrated to the feature extractor module. The acquired quantitative feature representation can be further utilized to train a discriminative model for classifying tissue types. Machine learning based methods for detection of regions of interest, or tissue type classification will advance the transition to decision support systems and computer aided diagnosis in digital pathology. Here we apply the proposed feature-augmented dCNN method with supervised learning in detecting cancerous tissue from whole slide images. The extracted quantitative representation of tissue histology was used to train a logistic regression model with elastic net regularization. The model was able to accurately discriminate cancerous tissue from normal tissue, resulting in blockwise AUC=0.97, where the total number of analyzed tissue blocks was approximately 8.3 million that constitute the test set of 75 whole slide images.","PeriodicalId":149766,"journal":{"name":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134264661","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: 14

Automated Stem Angle Determination for Temporal Plant Phenotyping Analysis 用于植物表型分析的茎角自动测定

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-01 DOI: 10.1109/ICCVW.2017.237

S. D. Choudhury, Saptarsi Goswami, Srinidhi Bashyam, T. Awada, A. Samal

{"title":"Automated Stem Angle Determination for Temporal Plant Phenotyping Analysis","authors":"S. D. Choudhury, Saptarsi Goswami, Srinidhi Bashyam, T. Awada, A. Samal","doi":"10.1109/ICCVW.2017.237","DOIUrl":"https://doi.org/10.1109/ICCVW.2017.237","url":null,"abstract":"Image-based plant phenotyping analysis refers to the monitoring and quantification of phenotyping traits by analyzing images of the plants captured by different types of cameras at regular intervals in a controlled environment. Extracting meaningful phenotypes for temporal phenotyping analysis by considering individual parts of a plant, e.g., leaves and stem, using computer-vision based techniques remains a critical bottleneck due to constantly increasing complexity in plant architecture with variations in self-occlusions and phyllotaxy. The paper introduces an algorithm to compute the stem angle, a potential measure for plants' susceptibility to lodging, i.e., the bending of stem of the plant. Annual yield losses due to stem lodging in the U.S. range between 5 and 25%. In addition to outright yield losses, grain quality may also decline as a result of stem lodging. The algorithm to compute stem angle involves the identification of leaf-tips and leaf-junctions based on a graph theoretic approach. The efficacy of the proposed method is demonstrated based on experimental analysis on a publicly available dataset called Panicoid Phenomap-1. A time-series clustering analysis is also performed on the values of stem angles for a significant time interval during vegetative stage life cycle of the maize plants. This analysis effectively summarizes the temporal patterns of the stem angles into three main groups, which provides further insight into genotype specific behavior of the plants. A comparison of genotypic purity using time series analysis establishes that the temporal variation of the stem angles is likely to be regulated by genetic variation under similar environmental conditions.","PeriodicalId":149766,"journal":{"name":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133362332","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: 22

LPSNet: A Novel Log Path Signature Feature Based Hand Gesture Recognition Framework LPSNet:一种新的基于日志路径特征的手势识别框架

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-01 DOI: 10.1109/ICCVW.2017.80

Chenyang Li, Xin Zhang, Lianwen Jin

引用次数: 34