2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)最新文献_第3页

DeshadowNet: A Multi-context Embedding Deep Network for Shadow Removal deshadownnet:一个用于阴影去除的多上下文嵌入深度网络

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.248

Liangqiong Qu, Jiandong Tian, Shengfeng He, Yandong Tang, Rynson W. H. Lau

{"title":"DeshadowNet: A Multi-context Embedding Deep Network for Shadow Removal","authors":"Liangqiong Qu, Jiandong Tian, Shengfeng He, Yandong Tang, Rynson W. H. Lau","doi":"10.1109/CVPR.2017.248","DOIUrl":"https://doi.org/10.1109/CVPR.2017.248","url":null,"abstract":"Shadow removal is a challenging task as it requires the detection/annotation of shadows as well as semantic understanding of the scene. In this paper, we propose an automatic and end-to-end deep neural network (DeshadowNet) to tackle these problems in a unified manner. DeshadowNet is designed with a multi-context architecture, where the output shadow matte is predicted by embedding information from three different perspectives. The first global network extracts shadow features from a global view. Two levels of features are derived from the global network and transferred to two parallel networks. While one extracts the appearance of the input image, the other one involves semantic understanding for final prediction. These two complementary networks generate multi-context features to obtain the shadow matte with fine local details. To evaluate the performance of the proposed method, we construct the first large scale benchmark with 3088 image pairs. Extensive experiments on two publicly available benchmarks and our large-scale benchmark show that the proposed method performs favorably against several state-of-the-art methods.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"60 1","pages":"2308-2316"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81049382","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: 195

Hyper-Laplacian Regularized Unidirectional Low-Rank Tensor Recovery for Multispectral Image Denoising 多光谱图像去噪的超拉普拉斯正则化单向低秩张量恢复

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.625

Yi Chang, Luxin Yan, Sheng Zhong

{"title":"Hyper-Laplacian Regularized Unidirectional Low-Rank Tensor Recovery for Multispectral Image Denoising","authors":"Yi Chang, Luxin Yan, Sheng Zhong","doi":"10.1109/CVPR.2017.625","DOIUrl":"https://doi.org/10.1109/CVPR.2017.625","url":null,"abstract":"Recent low-rank based matrix/tensor recovery methods have been widely explored in multispectral images (MSI) denoising. These methods, however, ignore the difference of the intrinsic structure correlation along spatial sparsity, spectral correlation and non-local self-similarity mode. In this paper, we go further by giving a detailed analysis about the rank properties both in matrix and tensor cases, and figure out the non-local self-similarity is the key ingredient, while the low-rank assumption of others may not hold. This motivates us to design a simple yet effective unidirectional low-rank tensor recovery model that is capable of truthfully capturing the intrinsic structure correlation with reduced computational burden. However, the low-rank models suffer from the ringing artifacts, due to the aggregation of overlapped patches/cubics. While previous methods resort to spatial information, we offer a new perspective by utilizing the exclusively spectral information in MSIs to address the issue. The analysis-based hyper-Laplacian prior is introduced to model the global spectral structures, so as to indirectly alleviate the ringing artifacts in spatial domain. The advantages of the proposed method over the existing ones are multi-fold: more reasonably structure correlation representability, less processing time, and less artifacts in the overlapped regions. The proposed method is extensively evaluated on several benchmarks, and significantly outperforms state-of-the-art MSI denoising methods.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"32 1","pages":"5901-5909"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84796285","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: 141

G2DeNet: Global Gaussian Distribution Embedding Network and Its Application to Visual Recognition G2DeNet:全局高斯分布嵌入网络及其在视觉识别中的应用

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.689

Qilong Wang, P. Li, Lei Zhang

{"title":"G2DeNet: Global Gaussian Distribution Embedding Network and Its Application to Visual Recognition","authors":"Qilong Wang, P. Li, Lei Zhang","doi":"10.1109/CVPR.2017.689","DOIUrl":"https://doi.org/10.1109/CVPR.2017.689","url":null,"abstract":"Recently, plugging trainable structural layers into deep convolutional neural networks (CNNs) as image representations has made promising progress. However, there has been little work on inserting parametric probability distributions, which can effectively model feature statistics, into deep CNNs in an end-to-end manner. This paper proposes a Global Gaussian Distribution embedding Network (G2DeNet) to take a step towards addressing this problem. The core of G2DeNet is a novel trainable layer of a global Gaussian as an image representation plugged into deep CNNs for end-to-end learning. The challenge is that the proposed layer involves Gaussian distributions whose space is not a linear space, which makes its forward and backward propagations be non-intuitive and non-trivial. To tackle this issue, we employ a Gaussian embedding strategy which respects the structures of both Riemannian manifold and smooth group of Gaussians. Based on this strategy, we construct the proposed global Gaussian embedding layer and decompose it into two sub-layers: the matrix partition sub-layer decoupling the mean vector and covariance matrix entangled in the embedding matrix, and the square-rooted, symmetric positive definite matrix sub-layer. In this way, we can derive the partial derivatives associated with the proposed structural layer and thus allow backpropagation of gradients. Experimental results on large scale region classification and fine-grained recognition tasks show that G2DeNet is superior to its counterparts, capable of achieving state-of-the-art performance.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"198 1","pages":"6507-6516"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90359214","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: 99

Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition 看得近看得好:用于细粒度图像识别的循环注意卷积神经网络

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.476

Jianlong Fu, Heliang Zheng, Tao Mei

{"title":"Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition","authors":"Jianlong Fu, Heliang Zheng, Tao Mei","doi":"10.1109/CVPR.2017.476","DOIUrl":"https://doi.org/10.1109/CVPR.2017.476","url":null,"abstract":"Recognizing fine-grained categories (e.g., bird species) is difficult due to the challenges of discriminative region localization and fine-grained feature learning. Existing approaches predominantly solve these challenges independently, while neglecting the fact that region detection and fine-grained feature learning are mutually correlated and thus can reinforce each other. In this paper, we propose a novel recurrent attention convolutional neural network (RA-CNN) which recursively learns discriminative region attention and region-based feature representation at multiple scales in a mutual reinforced way. The learning at each scale consists of a classification sub-network and an attention proposal sub-network (APN). The APN starts from full images, and iteratively generates region attention from coarse to fine by taking previous prediction as a reference, while the finer scale network takes as input an amplified attended region from previous scale in a recurrent way. The proposed RA-CNN is optimized by an intra-scale classification loss and an inter-scale ranking loss, to mutually learn accurate region attention and fine-grained representation. RA-CNN does not need bounding box/part annotations and can be trained end-to-end. We conduct comprehensive experiments and show that RA-CNN achieves the best performance in three fine-grained tasks, with relative accuracy gains of 3.3%, 3.7%, 3.8%, on CUB Birds, Stanford Dogs and Stanford Cars, respectively.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"15 1","pages":"4476-4484"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73415819","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: 1037

Image Deblurring via Extreme Channels Prior 图像去模糊通过极端通道先验

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.738

Yanyang Yan, Wenqi Ren, Yuanfang Guo, Rui Wang, Xiaochun Cao

引用次数: 265

SST: Single-Stream Temporal Action Proposals SST:单流暂时行动建议

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.675

S. Buch, Victor Escorcia, Chuanqi Shen, Bernard Ghanem, Juan Carlos Niebles

引用次数: 376

Face Normals "In-the-Wild" Using Fully Convolutional Networks 使用完全卷积网络“在野外”面对法线

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.44

George Trigeorgis, Patrick Snape, Iasonas Kokkinos, S. Zafeiriou

引用次数: 40

Object Co-skeletonization with Co-segmentation 对象协同骨架化与协同分割

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.413

Koteswar Rao Jerripothula, Jianfei Cai, Jiangbo Lu, Junsong Yuan

{"title":"Object Co-skeletonization with Co-segmentation","authors":"Koteswar Rao Jerripothula, Jianfei Cai, Jiangbo Lu, Junsong Yuan","doi":"10.1109/CVPR.2017.413","DOIUrl":"https://doi.org/10.1109/CVPR.2017.413","url":null,"abstract":"Recent advances in the joint processing of images have certainly shown its advantages over the individual processing. Different from the existing works geared towards co-segmentation or co-localization, in this paper, we explore a new joint processing topic: co-skeletonization, which is defined as joint skeleton extraction of common objects in a set of semantically similar images. Object skeletonization in real world images is a challenging problem, because there is no prior knowledge of the objects shape if we consider only a single image. This motivates us to resort to the idea of object co-skeletonization hoping that the commonness prior existing across the similar images may help, just as it does for other joint processing problems such as co-segmentation. Noting that skeleton can provide good scribbles for segmentation, and skeletonization, in turn, needs good segmentation, we propose a coupled framework for co-skeletonization and co-segmentation tasks so that they are well informed by each other, and benefit each other synergistically. Since it is a new problem, we also construct a benchmark dataset for the co-skeletonization task. Extensive experiments demonstrate that proposed method achieves very competitive results.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"41 1","pages":"3881-3889"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89260881","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: 54

StyleNet: Generating Attractive Visual Captions with Styles StyleNet:用样式生成吸引人的视觉标题

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.108

Chuang Gan, Zhe Gan, Xiaodong He, Jianfeng Gao, L. Deng

引用次数: 243

SGM-Nets: Semi-Global Matching with Neural Networks SGM-Nets:神经网络的半全局匹配

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.703

A. Seki, M. Pollefeys

引用次数: 220