2019 IEEE/CVF International Conference on Computer Vision (ICCV)最新文献_第3页

View Independent Generative Adversarial Network for Novel View Synthesis 新颖视点合成的视点独立生成对抗网络

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00788

Xiaogang Xu, Ying-Cong Chen, Jiaya Jia

引用次数: 34

Probabilistic Deep Ordinal Regression Based on Gaussian Processes 基于高斯过程的概率深度有序回归

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00540

Yanzhu Liu, Fan Wang, A. Kong

{"title":"Probabilistic Deep Ordinal Regression Based on Gaussian Processes","authors":"Yanzhu Liu, Fan Wang, A. Kong","doi":"10.1109/ICCV.2019.00540","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00540","url":null,"abstract":"With excellent representation power for complex data, deep neural networks (DNNs) based approaches are state-of-the-art for ordinal regression problem which aims to classify instances into ordinal categories. However, DNNs are not able to capture uncertainties and produce probabilistic interpretations. As a probabilistic model, Gaussian Processes (GPs) on the other hand offers uncertainty information, which is nonetheless lack of scalability for large datasets. This paper adapts traditional GPs regression for ordinal regression problem by using both conjugate and non-conjugate ordinal likelihood. Based on that, it proposes a deep neural network with a GPs layer on the top, which is trained end-to-end by the stochastic gradient descent method for both neural network parameters and GPs parameters. The parameters in the ordinal likelihood function are learned as neural network parameters so that the proposed framework is able to produce fitted likelihood functions for training sets and make probabilistic predictions for test points. Experimental results on three real-world benchmarks -- image aesthetics rating, historical image grading and age group estimation -- demonstrate that in terms of mean absolute error, the proposed approach outperforms state-of-the-art ordinal regression approaches and provides the confidence for predictions.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"48 1","pages":"5300-5308"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82134279","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: 18

Joint Prediction for Kinematic Trajectories in Vehicle-Pedestrian-Mixed Scenes 车辆-行人-混合场景运动轨迹联合预测

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.01048

Huikun Bi, Zhong Fang, Tianlu Mao, Zhaoqi Wang, Z. Deng

{"title":"Joint Prediction for Kinematic Trajectories in Vehicle-Pedestrian-Mixed Scenes","authors":"Huikun Bi, Zhong Fang, Tianlu Mao, Zhaoqi Wang, Z. Deng","doi":"10.1109/ICCV.2019.01048","DOIUrl":"https://doi.org/10.1109/ICCV.2019.01048","url":null,"abstract":"Trajectory prediction for objects is challenging and critical for various applications (e.g., autonomous driving, and anomaly detection). Most of the existing methods focus on homogeneous pedestrian trajectories prediction, where pedestrians are treated as particles without size. However, they fall short of handling crowded vehicle-pedestrian-mixed scenes directly since vehicles, limited with kinematics in reality, should be treated as rigid, non-particle objects ideally. In this paper, we tackle this problem using separate LSTMs for heterogeneous vehicles and pedestrians. Specifically, we use an oriented bounding box to represent each vehicle, calculated based on its position and orientation, to denote its kinematic trajectories. We then propose a framework called VP-LSTM to predict the kinematic trajectories of both vehicles and pedestrians simultaneously. In order to evaluate our model, a large dataset containing the trajectories of both vehicles and pedestrians in vehicle-pedestrian-mixed scenes is specially built. Through comparisons between our method with state-of-the-art approaches, we show the effectiveness and advantages of our method on kinematic trajectories prediction in vehicle-pedestrian-mixed scenes.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"51 1","pages":"10382-10391"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82267191","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: 28

GEOBIT: A Geodesic-Based Binary Descriptor Invariant to Non-Rigid Deformations for RGB-D Images GEOBIT:基于测地线的RGB-D图像非刚性变形二值描述符不变性

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.01010

Erickson R. Nascimento, Guilherme A. Potje, Renato Martins, Felipe C. Chamone, M. Campos, R. Bajcsy

{"title":"GEOBIT: A Geodesic-Based Binary Descriptor Invariant to Non-Rigid Deformations for RGB-D Images","authors":"Erickson R. Nascimento, Guilherme A. Potje, Renato Martins, Felipe C. Chamone, M. Campos, R. Bajcsy","doi":"10.1109/ICCV.2019.01010","DOIUrl":"https://doi.org/10.1109/ICCV.2019.01010","url":null,"abstract":"At the core of most three-dimensional alignment and tracking tasks resides the critical problem of point correspondence. In this context, the design of descriptors that efficiently and uniquely identifies keypoints, to be matched, is of central importance. Numerous descriptors have been developed for dealing with affine/perspective warps, but few can also handle non-rigid deformations. In this paper, we introduce a novel binary RGB-D descriptor invariant to isometric deformations. Our method uses geodesic isocurves on smooth textured manifolds. It combines appearance and geometric information from RGB-D images to tackle non-rigid transformations. We used our descriptor to track multiple textured depth maps and demonstrate that it produces reliable feature descriptors even in the presence of strong non-rigid deformations and depth noise. The experiments show that our descriptor outperforms different state-of-the-art descriptors in both precision-recall and recognition rate metrics. We also provide to the community a new dataset composed of annotated RGB-D images of different objects (shirts, cloths, paintings, bags), subjected to strong non-rigid deformations, to evaluate point correspondence algorithms.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"85 1","pages":"10003-10011"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76050172","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

Agile Depth Sensing Using Triangulation Light Curtains 使用三角光幕的敏捷深度传感

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00799

Joseph R. Bartels, Jian Wang, W. Whittaker, S. Narasimhan

引用次数: 20

Stacked Cross Refinement Network for Edge-Aware Salient Object Detection 边缘感知显著目标检测的堆叠交叉细化网络

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00736

Zhe Wu, Li Su, Qingming Huang

{"title":"Stacked Cross Refinement Network for Edge-Aware Salient Object Detection","authors":"Zhe Wu, Li Su, Qingming Huang","doi":"10.1109/ICCV.2019.00736","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00736","url":null,"abstract":"Salient object detection is a fundamental computer vision task. The majority of existing algorithms focus on aggregating multi-level features of pre-trained convolutional neural networks. Moreover, some researchers attempt to utilize edge information for auxiliary training. However, existing edge-aware models design unidirectional frameworks which only use edge features to improve the segmentation features. Motivated by the logical interrelations between binary segmentation and edge maps, we propose a novel Stacked Cross Refinement Network (SCRN) for salient object detection in this paper. Our framework aims to simultaneously refine multi-level features of salient object detection and edge detection by stacking Cross Refinement Unit (CRU). According to the logical interrelations, the CRU designs two direction-specific integration operations, and bidirectionally passes messages between the two tasks. Incorporating the refined edge-preserving features with the typical U-Net, our model detects salient objects accurately. Extensive experiments conducted on six benchmark datasets demonstrate that our method outperforms existing state-of-the-art algorithms in both accuracy and efficiency. Besides, the attribute-based performance on the SOC dataset show that the proposed model ranks first in the majority of challenging scenes. Code can be found at https://github.com/wuzhe71/SCAN.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"9 1","pages":"7263-7272"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88062889","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: 277

Attribute Manipulation Generative Adversarial Networks for Fashion Images 时尚图像属性操作生成对抗网络

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.01064

Kenan E. Ak, A. Kassim, Joo-Hwee Lim, J. Y. Tham

{"title":"Attribute Manipulation Generative Adversarial Networks for Fashion Images","authors":"Kenan E. Ak, A. Kassim, Joo-Hwee Lim, J. Y. Tham","doi":"10.1109/ICCV.2019.01064","DOIUrl":"https://doi.org/10.1109/ICCV.2019.01064","url":null,"abstract":"Recent advances in Generative Adversarial Networks (GANs) have made it possible to conduct multi-domain image-to-image translation using a single generative network. While recent methods such as Ganimation and SaGAN are able to conduct translations on attribute-relevant regions using attention, they do not perform well when the number of attributes increases as the training of attention masks mostly rely on classification losses. To address this and other limitations, we introduce Attribute Manipulation Generative Adversarial Networks (AMGAN) for fashion images. While AMGAN's generator network uses class activation maps (CAMs) to empower its attention mechanism, it also exploits perceptual losses by assigning reference (target) images based on attribute similarities. AMGAN incorporates an additional discriminator network that focuses on attribute-relevant regions to detect unrealistic translations. Additionally, AMGAN can be controlled to perform attribute manipulations on specific regions such as the sleeve or torso regions. Experiments show that AMGAN outperforms state-of-the-art methods using traditional evaluation metrics as well as an alternative one that is based on image retrieval.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"24 1","pages":"10540-10549"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86518855","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: 69

Learning Joint 2D-3D Representations for Depth Completion 学习深度完成的联合2D-3D表示

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.01012

Yuxiang Chen, Binh Yang, Ming Liang, R. Urtasun

引用次数: 127

On the Global Optima of Kernelized Adversarial Representation Learning 核化对抗表示学习的全局最优

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00806

Bashir Sadeghi, R. Yu, Vishnu Naresh Boddeti

{"title":"On the Global Optima of Kernelized Adversarial Representation Learning","authors":"Bashir Sadeghi, R. Yu, Vishnu Naresh Boddeti","doi":"10.1109/ICCV.2019.00806","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00806","url":null,"abstract":"Adversarial representation learning is a promising paradigm for obtaining data representations that are invariant to certain sensitive attributes while retaining the information necessary for predicting target attributes. Existing approaches solve this problem through iterative adversarial minimax optimization and lack theoretical guarantees. In this paper, we first study the ``linear\" form of this problem i.e., the setting where all the players are linear functions. We show that the resulting optimization problem is both non-convex and non-differentiable. We obtain an exact closed-form expression for its global optima through spectral learning and provide performance guarantees in terms of analytical bounds on the achievable utility and invariance. We then extend this solution and analysis to non-linear functions through kernel representation. Numerical experiments on UCI, Extended Yale B and CIFAR-100 datasets indicate that, (a) practically, our solution is ideal for ``imparting\" provable invariance to any biased pre-trained data representation, and (b) the global optima of the ``kernel\" form can provide a comparable trade-off between utility and invariance in comparison to iterative minimax optimization of existing deep neural network based approaches, but with provable guarantees.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"22 8 1","pages":"7970-7978"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82923046","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: 26

Deep Supervised Hashing With Anchor Graph 锚图的深度监督哈希

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00989

Yudong Chen, Zhihui Lai, Yujuan Ding, Kaiyi Lin, W. Wong

引用次数: 48