2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition最新文献_第2页

Probabilistic Joint Face-Skull Modelling for Facial Reconstruction 面部重建的关节-颅骨概率建模

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00555

Dennis Madsen, M. Lüthi, Andreas Schneider, T. Vetter

{"title":"Probabilistic Joint Face-Skull Modelling for Facial Reconstruction","authors":"Dennis Madsen, M. Lüthi, Andreas Schneider, T. Vetter","doi":"10.1109/CVPR.2018.00555","DOIUrl":"https://doi.org/10.1109/CVPR.2018.00555","url":null,"abstract":"We present a novel method for co-registration of two independent statistical shape models. We solve the problem of aligning a face model to a skull model with stochastic optimization based on Markov Chain Monte Carlo (MCMC). We create a probabilistic joint face-skull model and show how to obtain a distribution of plausible face shapes given a skull shape. Due to environmental and genetic factors, there exists a distribution of possible face shapes arising from the same skull. We pose facial reconstruction as a conditional distribution of plausible face shapes given a skull shape. Because it is very difficult to obtain the distribution directly from MRI or CT data, we create a dataset of artificial face-skull pairs. To do this, we propose to combine three data sources of independent origin to model the joint face-skull distribution: a face shape model, a skull shape model and tissue depth marker information. For a given skull, we compute the posterior distribution of faces matching the tissue depth distribution with Metropolis-Hastings. We estimate the joint face-skull distribution from samples of the posterior. To find faces matching to an unknown skull, we estimate the probability of the face under the joint face-skull model. To our knowledge, we are the first to provide a whole distribution of plausible faces arising from a skull instead of only a single reconstruction. We show how the face-skull model can be used to rank a face dataset and on average successfully identify the correct match in top 30%. The face ranking even works when obtaining the face shapes from 2D images. We furthermore show how the face-skull model can be useful to estimate the skull position in an MR-image.","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":"73181318","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: 15

Interpretable Video Captioning via Trajectory Structured Localization 通过轨迹结构化定位的可解释视频字幕

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00714

X. Wu, Guanbin Li, Qingxing Cao, Qingge Ji, Liang Lin

{"title":"Interpretable Video Captioning via Trajectory Structured Localization","authors":"X. Wu, Guanbin Li, Qingxing Cao, Qingge Ji, Liang Lin","doi":"10.1109/CVPR.2018.00714","DOIUrl":"https://doi.org/10.1109/CVPR.2018.00714","url":null,"abstract":"Automatically describing open-domain videos with natural language are attracting increasing interest in the field of artificial intelligence. Most existing methods simply borrow ideas from image captioning and obtain a compact video representation from an ensemble of global image feature before feeding to an RNN decoder which outputs a sentence of variable length. However, it is not only arduous for the generator to focus on specific salient objects at different time given the global video representation, it is more formidable to capture the fine-grained motion information and the relation between moving instances for more subtle linguistic descriptions. In this paper, we propose a Trajectory Structured Attentional Encoder-Decoder (TSA-ED) neural network framework for more elaborate video captioning which works by integrating local spatial-temporal representation at trajectory level through structured attention mechanism. Our proposed method is based on a LSTM-based encoder-decoder framework, which incorporates an attention modeling scheme to adaptively learn the correlation between sentence structure and the moving objects in videos, and consequently generates more accurate and meticulous statement description in the decoding stage. Experimental results demonstrate that the feature representation and structured attention mechanism based on the trajectory cluster can efficiently obtain the local motion information in the video to help generate a more fine-grained video description, and achieve the state-of-the-art performance on the well-known Charades and MSVD datasets.","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":"79862382","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: 55

Multiple Granularity Group Interaction Prediction 多粒度组交互预测

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00239

Taiping Yao, Minsi Wang, Bingbing Ni, Huawei Wei, Xiaokang Yang

引用次数: 18

Crowd Counting via Adversarial Cross-Scale Consistency Pursuit 通过对抗性跨尺度一致性追求的人群计数

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00550

Zan Shen, Yi Xu, Bingbing Ni, Minsi Wang, Jianguo Hu, Xiaokang Yang

{"title":"Crowd Counting via Adversarial Cross-Scale Consistency Pursuit","authors":"Zan Shen, Yi Xu, Bingbing Ni, Minsi Wang, Jianguo Hu, Xiaokang Yang","doi":"10.1109/CVPR.2018.00550","DOIUrl":"https://doi.org/10.1109/CVPR.2018.00550","url":null,"abstract":"Crowd counting or density estimation is a challenging task in computer vision due to large scale variations, perspective distortions and serious occlusions, etc. Existing methods generally suffer from two issues: 1) the model averaging effects in multi-scale CNNs induced by the widely adopted $$ regression loss; and 2) inconsistent estimation across different scaled inputs. To explicitly address these issues, we propose a novel crowd counting (density estimation) framework called Adversarial Cross-Scale Consistency Pursuit (ACSCP). On one hand, a U-net structured generation network is designed to generate density map from input patch, and an adversarial loss is directly employed to shrink the solution onto a realistic subspace, thus attenuating the blurry effects of density map estimation. On the other hand, we design a novel scale-consistency regularizer which enforces that the sum up of the crowd counts from local patches (i.e., small scale) is coherent with the overall count of their region union (i.e., large scale). The above losses are integrated via a joint training scheme, so as to help boost density estimation performance by further exploring the collaboration between both objectives. Extensive experiments on four benchmarks have well demonstrated the effectiveness of the proposed innovations as well as the superior performance over prior art.","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":"82061516","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: 305

GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation 基于几何神经网络的节理深度和表面法向估计

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00037

Xiaojuan Qi, Renjie Liao, Zhengzhe Liu, R. Urtasun, Jiaya Jia

引用次数: 291

Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation 用于无监督生物医学分割的卷积网络解剖先验

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00968

Adrian V. Dalca, J. Guttag, M. Sabuncu

{"title":"Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation","authors":"Adrian V. Dalca, J. Guttag, M. Sabuncu","doi":"10.1109/CVPR.2018.00968","DOIUrl":"https://doi.org/10.1109/CVPR.2018.00968","url":null,"abstract":"We consider the problem of segmenting a biomedical image into anatomical regions of interest. We specifically address the frequent scenario where we have no paired training data that contains images and their manual segmentations. Instead, we employ unpaired segmentation images that we use to build an anatomical prior. Critically these segmentations can be derived from imaging data from a different dataset and imaging modality than the current task. We introduce a generative probabilistic model that employs the learned prior through a convolutional neural network to compute segmentations in an unsupervised setting. We conducted an empirical analysis of the proposed approach in the context of structural brain MRI segmentation, using a multi-study dataset of more than 14,000 scans. Our results show that an anatomical prior enables fast unsupervised segmentation which is typically not possible using standard convolutional networks. The integration of anatomical priors can facilitate CNN-based anatomical segmentation in a range of novel clinical problems, where few or no annotations are available and thus standard networks are not trainable. The code, model definitions and model weights are freely available at http://github.com/adalca/neuron.","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":"76386875","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: 129

Wrapped Gaussian Process Regression on Riemannian Manifolds 黎曼流形上的缠绕高斯过程回归

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00585

Anton Mallasto, Aasa Feragen

引用次数: 28

A Constrained Deep Neural Network for Ordinal Regression 有序回归的约束深度神经网络

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00093

Yanzhu Liu, A. Kong, C. Goh

{"title":"A Constrained Deep Neural Network for Ordinal Regression","authors":"Yanzhu Liu, A. Kong, C. Goh","doi":"10.1109/CVPR.2018.00093","DOIUrl":"https://doi.org/10.1109/CVPR.2018.00093","url":null,"abstract":"Ordinal regression is a supervised learning problem aiming to classify instances into ordinal categories. It is challenging to automatically extract high-level features for representing intraclass information and interclass ordinal relationship simultaneously. This paper proposes a constrained optimization formulation for the ordinal regression problem which minimizes the negative loglikelihood for multiple categories constrained by the order relationship between instances. Mathematically, it is equivalent to an unconstrained formulation with a pairwise regularizer. An implementation based on the CNN framework is proposed to solve the problem such that high-level features can be extracted automatically, and the optimal solution can be learned through the traditional back-propagation method. The proposed pairwise constraints make the algorithm work even on small datasets, and a proposed efficient implementation make it be scalable for large datasets. Experimental results on four real-world benchmarks demonstrate that the proposed algorithm outperforms the traditional deep learning approaches and other state-of-the-art approaches based on hand-crafted features.","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":"76062685","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

The Best of Both Worlds: Combining CNNs and Geometric Constraints for Hierarchical Motion Segmentation 两全其美:结合cnn和几何约束进行分层运动分割

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00060

Pia Bideau, Aruni RoyChowdhury, Rakesh R Menon, E. Learned-Miller

引用次数: 38

Weakly Supervised Facial Action Unit Recognition Through Adversarial Training 通过对抗性训练的弱监督面部动作单元识别

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pub Date : 2018-06-01 DOI: 10.1109/CVPR.2018.00233

Guozhu Peng, Shangfei Wang

{"title":"Weakly Supervised Facial Action Unit Recognition Through Adversarial Training","authors":"Guozhu Peng, Shangfei Wang","doi":"10.1109/CVPR.2018.00233","DOIUrl":"https://doi.org/10.1109/CVPR.2018.00233","url":null,"abstract":"Current works on facial action unit (AU) recognition typically require fully AU-annotated facial images for supervised AU classifier training. AU annotation is a time-consuming, expensive, and error-prone process. While AUs are hard to annotate, facial expression is relatively easy to label. Furthermore, there exist strong probabilistic dependencies between expressions and AUs as well as dependencies among AUs. Such dependencies are referred to as domain knowledge. In this paper, we propose a novel AU recognition method that learns AU classifiers from domain knowledge and expression-annotated facial images through adversarial training. Specifically, we first generate pseudo AU labels according to the probabilistic dependencies between expressions and AUs as well as correlations among AUs summarized from domain knowledge. Then we propose a weakly supervised AU recognition method via an adversarial process, in which we simultaneously train two models: a recognition model R, which learns AU classifiers, and a discrimination model D, which estimates the probability that AU labels generated from domain knowledge rather than the recognized AU labels from R. The training procedure for R maximizes the probability of D making a mistake. By leveraging the adversarial mechanism, the distribution of recognized AUs is closed to AU prior distribution from domain knowledge. Furthermore, the proposed weakly supervised AU recognition can be extended to semi-supervised learning scenarios with partially AU-annotated images. Experimental results on three benchmark databases demonstrate that the proposed method successfully leverages the summarized domain knowledge to weakly supervised AU classifier learning through an adversarial process, and thus achieves 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":"87995762","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: 51