2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition最新文献_第10页

Duplex Generative Adversarial Network for Unsupervised Domain Adaptation 无监督域自适应的双生成对抗网络

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00162

Lanqing Hu, Meina Kan, S. Shan, Xilin Chen

{"title":"Duplex Generative Adversarial Network for Unsupervised Domain Adaptation","authors":"Lanqing Hu, Meina Kan, S. Shan, Xilin Chen","doi":"10.1109/CVPR.2018.00162","DOIUrl":"https://doi.org/10.1109/CVPR.2018.00162","url":null,"abstract":"Domain adaptation attempts to transfer the knowledge obtained from the source domain to the target domain, i.e., the domain where the testing data are. The main challenge lies in the distribution discrepancy between source and target domain. Most existing works endeavor to learn domain invariant representation usually by minimizing a distribution distance, e.g., MMD and the discriminator in the recently proposed generative adversarial network (GAN). Following the similar idea of GAN, this work proposes a novel GAN architecture with duplex adversarial discriminators (referred to as DupGAN), which can achieve domain-invariant representation and domain transformation. Specifically, our proposed network consists of three parts, an encoder, a generator and two discriminators. The encoder embeds samples from both domains into the latent representation, and the generator decodes the latent representation to both source and target domains respectively conditioned on a domain code, i.e., achieves domain transformation. The generator is pitted against duplex discriminators, one for source domain and the other for target, to ensure the reality of domain transformation, the latent representation domain invariant and the category information of it preserved as well. Our proposed work achieves the state-of-the-art performance on unsupervised domain adaptation of digit classification and object recognition.","PeriodicalId":6564,"journal":{"name":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78544129","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: 153

Feature Super-Resolution: Make Machine See More Clearly 超分辨率:使机器看得更清楚

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00420

Weimin Tan, Bo Yan, Bahetiyaer Bare

{"title":"Feature Super-Resolution: Make Machine See More Clearly","authors":"Weimin Tan, Bo Yan, Bahetiyaer Bare","doi":"10.1109/CVPR.2018.00420","DOIUrl":"https://doi.org/10.1109/CVPR.2018.00420","url":null,"abstract":"Identifying small size images or small objects is a notoriously challenging problem, as discriminative representations are difficult to learn from the limited information contained in them with poor-quality appearance and unclear object structure. Existing research works usually increase the resolution of low-resolution image in the pixel space in order to provide better visual quality for human viewing. However, the improved performance of such methods is usually limited or even trivial in the case of very small image size (we will show it in this paper explicitly). In this paper, different from image super-resolution (ISR), we propose a novel super-resolution technique called feature super-resolution (FSR), which aims at enhancing the discriminatory power of small size image in order to provide high recognition precision for machine. To achieve this goal, we propose a new Feature Super-Resolution Generative Adversarial Network (FSR-GAN) model that transforms the raw poor features of small size images to highly discriminative ones by performing super-resolution in the feature space. Our FSR-GAN consists of two subnetworks: a feature generator network G and a feature discriminator network D. By training the G and the D networks in an alternative manner, we encourage the G network to discover the latent distribution correlations between small size and large size images and then use G to improve the representations of small images. Extensive experiment results on Oxford5K, Paris, Holidays, and Flick100k datasets demonstrate that the proposed FSR approach can effectively enhance the discriminatory ability of features. Even when the resolution of query images is reduced greatly, e.g., 1/64 original size, the query feature enhanced by our FSR approach achieves surprisingly high retrieval performance at different image resolutions and increases the retrieval precision by 25% compared to the raw query feature.","PeriodicalId":6564,"journal":{"name":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78680638","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: 40

DenseASPP for Semantic Segmentation in Street Scenes 街景语义分割的DenseASPP

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00388

Maoke Yang, Kun Yu, Chi Zhang, Zhiwei Li, Kuiyuan Yang

{"title":"DenseASPP for Semantic Segmentation in Street Scenes","authors":"Maoke Yang, Kun Yu, Chi Zhang, Zhiwei Li, Kuiyuan Yang","doi":"10.1109/CVPR.2018.00388","DOIUrl":"https://doi.org/10.1109/CVPR.2018.00388","url":null,"abstract":"Semantic image segmentation is a basic street scene understanding task in autonomous driving, where each pixel in a high resolution image is categorized into a set of semantic labels. Unlike other scenarios, objects in autonomous driving scene exhibit very large scale changes, which poses great challenges for high-level feature representation in a sense that multi-scale information must be correctly encoded. To remedy this problem, atrous convolution[14]was introduced to generate features with larger receptive fields without sacrificing spatial resolution. Built upon atrous convolution, Atrous Spatial Pyramid Pooling (ASPP)[2] was proposed to concatenate multiple atrous-convolved features using different dilation rates into a final feature representation. Although ASPP is able to generate multi-scale features, we argue the feature resolution in the scale-axis is not dense enough for the autonomous driving scenario. To this end, we propose Densely connected Atrous Spatial Pyramid Pooling (DenseASPP), which connects a set of atrous convolutional layers in a dense way, such that it generates multi-scale features that not only cover a larger scale range, but also cover that scale range densely, without significantly increasing the model size. We evaluate DenseASPP on the street scene benchmark Cityscapes[4] and achieve state-of-the-art performance.","PeriodicalId":6564,"journal":{"name":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76684713","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: 1036

Salience Guided Depth Calibration for Perceptually Optimized Compressive Light Field 3D Display 感知优化压缩光场三维显示的显著性引导深度校准

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00217

Shizheng Wang, Wenjuan Liao, P. Surman, Zhigang Tu, Yuanjin Zheng, Junsong Yuan

{"title":"Salience Guided Depth Calibration for Perceptually Optimized Compressive Light Field 3D Display","authors":"Shizheng Wang, Wenjuan Liao, P. Surman, Zhigang Tu, Yuanjin Zheng, Junsong Yuan","doi":"10.1109/CVPR.2018.00217","DOIUrl":"https://doi.org/10.1109/CVPR.2018.00217","url":null,"abstract":"Multi-layer light field displays are a type of computational three-dimensional (3D) display which has recently gained increasing interest for its holographic-like effect and natural compatibility with 2D displays. However, the major shortcoming, depth limitation, still cannot be overcome in the traditional light field modeling and reconstruction based on multi-layer liquid crystal displays (LCDs). Considering this disadvantage, our paper incorporates a salience guided depth optimization over a limited display range to calibrate the displayed depth and present the maximum area of salience region for multi-layer light field display. Different from previously reported cascaded light field displays that use the fixed initialization plane as the depth center of display content, our method automatically calibrates the depth initialization based on the salience results derived from the proposed contrast enhanced salience detection method. Experiments demonstrate that the proposed method provides a promising advantage in visual perception for the compressive light field displays from both software simulation and prototype demonstration.","PeriodicalId":6564,"journal":{"name":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77902099","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: 13

Re-weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation 无监督域自适应的重加权对抗自适应网络

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00832

Qingchao Chen, Yang Liu, Zhaowen Wang, I. Wassell, K. Chetty

{"title":"Re-weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation","authors":"Qingchao Chen, Yang Liu, Zhaowen Wang, I. Wassell, K. Chetty","doi":"10.1109/CVPR.2018.00832","DOIUrl":"https://doi.org/10.1109/CVPR.2018.00832","url":null,"abstract":"Unsupervised Domain Adaptation (UDA) aims to transfer domain knowledge from existing well-defined tasks to new ones where labels are unavailable. In the real-world applications, as the domain (task) discrepancies are usually uncontrollable, it is significantly motivated to match the feature distributions even if the domain discrepancies are disparate. Additionally, as no label is available in the target domain, how to successfully adapt the classifier from the source to the target domain still remains an open question. In this paper, we propose the Re-weighted Adversarial Adaptation Network (RAAN) to reduce the feature distribution divergence and adapt the classifier when domain discrepancies are disparate. Specifically, to alleviate the need of common supports in matching the feature distribution, we choose to minimize optimal transport (OT) based Earth-Mover (EM) distance and reformulate it to a minimax objective function. Utilizing this, RAAN can be trained in an end-to-end and adversarial manner. To further adapt the classifier, we propose to match the label distribution and embed it into the adversarial training. Finally, after extensive evaluation of our method using UDA datasets of varying difficulty, RAAN achieved the state-of-the-art results and outperformed other methods by a large margin when the domain shifts are disparate.","PeriodicalId":6564,"journal":{"name":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78125182","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: 113

Mesoscopic Facial Geometry Inference Using Deep Neural Networks 基于深度神经网络的介观面部几何推理

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00877

Loc Huynh, Weikai Chen, Shunsuke Saito, Jun Xing, Koki Nagano, Andrew Jones, P. Debevec, Hao Li

{"title":"Mesoscopic Facial Geometry Inference Using Deep Neural Networks","authors":"Loc Huynh, Weikai Chen, Shunsuke Saito, Jun Xing, Koki Nagano, Andrew Jones, P. Debevec, Hao Li","doi":"10.1109/CVPR.2018.00877","DOIUrl":"https://doi.org/10.1109/CVPR.2018.00877","url":null,"abstract":"We present a learning-based approach for synthesizing facial geometry at medium and fine scales from diffusely-lit facial texture maps. When applied to an image sequence, the synthesized detail is temporally coherent. Unlike current state-of-the-art methods [17, 5], which assume \"dark is deep\", our model is trained with measured facial detail collected using polarized gradient illumination in a Light Stage [20]. This enables us to produce plausible facial detail across the entire face, including where previous approaches may incorrectly interpret dark features as concavities such as at moles, hair stubble, and occluded pores. Instead of directly inferring 3D geometry, we propose to encode fine details in high-resolution displacement maps which are learned through a hybrid network adopting the state-of-the-art image-to-image translation network [29] and super resolution network [43]. To effectively capture geometric detail at both mid- and high frequencies, we factorize the learning into two separate sub-networks, enabling the full range of facial detail to be modeled. Results from our learning-based approach compare favorably with a high-quality active facial scanhening technique, and require only a single passive lighting condition without a complex scanning setup.","PeriodicalId":6564,"journal":{"name":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76900806","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: 63

PointGrid: A Deep Network for 3D Shape Understanding PointGrid:用于三维形状理解的深度网络

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00959

Truc Le, Y. Duan

引用次数: 290

Towards Pose Invariant Face Recognition in the Wild 面向姿态不变的野外人脸识别

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00235

Jian Zhao, Yu Cheng, Yan Xu, Lin Xiong, Jianshu Li, F. Zhao, J. Karlekar, Sugiri Pranata, Shengmei Shen, Junliang Xing, Shuicheng Yan, Jiashi Feng

{"title":"Towards Pose Invariant Face Recognition in the Wild","authors":"Jian Zhao, Yu Cheng, Yan Xu, Lin Xiong, Jianshu Li, F. Zhao, J. Karlekar, Sugiri Pranata, Shengmei Shen, Junliang Xing, Shuicheng Yan, Jiashi Feng","doi":"10.1109/CVPR.2018.00235","DOIUrl":"https://doi.org/10.1109/CVPR.2018.00235","url":null,"abstract":"Pose variation is one key challenge in face recognition. As opposed to current techniques for pose invariant face recognition, which either directly extract pose invariant features for recognition, or first normalize profile face images to frontal pose before feature extraction, we argue that it is more desirable to perform both tasks jointly to allow them to benefit from each other. To this end, we propose a Pose Invariant Model (PIM) for face recognition in the wild, with three distinct novelties. First, PIM is a novel and unified deep architecture, containing a Face Frontalization sub-Net (FFN) and a Discriminative Learning sub-Net (DLN), which are jointly learned from end to end. Second, FFN is a well-designed dual-path Generative Adversarial Network (GAN) which simultaneously perceives global structures and local details, incorporated with an unsupervised cross-domain adversarial training and a \"learning to learn\" strategy for high-fidelity and identity-preserving frontal view synthesis. Third, DLN is a generic Convolutional Neural Network (CNN) for face recognition with our enforced cross-entropy optimization strategy for learning discriminative yet generalized feature representation. Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks demonstrate the superiority of the proposed model over the state-of-the-arts.","PeriodicalId":6564,"journal":{"name":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75221520","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: 185

Camera Pose Estimation with Unknown Principal Point 未知主点的相机姿态估计

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00315

Viktor Larsson, Z. Kukelova, Yinqiang Zheng

引用次数: 25

Statistical Tomography of Microscopic Life 微观生命的统计断层扫描

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00671

Aviad Levis, Y. Schechner, R. Talmon

引用次数: 9