2017 IEEE International Conference on Computer Vision (ICCV)最新文献_第6页

Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios? 边界框、分割和目标坐标:识别对自动驾驶场景中3D场景流估计有多重要?

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.281

Aseem Behl, O. Jafari, Siva Karthik Mustikovela, Hassan Abu Alhaija, C. Rother, Andreas Geiger

{"title":"Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?","authors":"Aseem Behl, O. Jafari, Siva Karthik Mustikovela, Hassan Abu Alhaija, C. Rother, Andreas Geiger","doi":"10.1109/ICCV.2017.281","DOIUrl":"https://doi.org/10.1109/ICCV.2017.281","url":null,"abstract":"Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e.g., at texture-less or reflective surfaces. However, these challenges are omnipresent in dynamic road scenes, which is the focus of this work. Our main contribution is to overcome these 3D motion estimation problems by exploiting recognition. In particular, we investigate the importance of recognition granularity, from coarse 2D bounding box estimates over 2D instance segmentations to fine-grained 3D object part predictions. We compute these cues using CNNs trained on a newly annotated dataset of stereo images and integrate them into a CRF-based model for robust 3D scene flow estimation - an approach we term Instance Scene Flow. We analyze the importance of each recognition cue in an ablation study and observe that the instance segmentation cue is by far strongest, in our setting. We demonstrate the effectiveness of our method on the challenging KITTI 2015 scene flow benchmark where we achieve state-of-the-art performance at the time of submission.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"79 1","pages":"2593-2602"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90838920","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: 130

PanNet: A Deep Network Architecture for Pan-Sharpening PanNet:用于泛锐化的深度网络架构

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.193

Junfeng Yang, Xueyang Fu, Yuwen Hu, Yue Huang, Xinghao Ding, J. Paisley

引用次数: 427

Filter Selection for Hyperspectral Estimation 高光谱估计的滤波器选择

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.342

Boaz Arad, O. Ben-Shahar

引用次数: 35

SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-training on Indoor Segmentation? SceneNet RGB-D: 5M合成图像在室内分割上能胜过通用ImageNet预训练吗?

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.292

J. McCormac, Ankur Handa, Stefan Leutenegger, A. Davison

{"title":"SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-training on Indoor Segmentation?","authors":"J. McCormac, Ankur Handa, Stefan Leutenegger, A. Davison","doi":"10.1109/ICCV.2017.292","DOIUrl":"https://doi.org/10.1109/ICCV.2017.292","url":null,"abstract":"We introduce SceneNet RGB-D, a dataset providing pixel-perfect ground truth for scene understanding problems such as semantic segmentation, instance segmentation, and object detection. It also provides perfect camera poses and depth data, allowing investigation into geometric computer vision problems such as optical flow, camera pose estimation, and 3D scene labelling tasks. Random sampling permits virtually unlimited scene configurations, and here we provide 5M rendered RGB-D images from 16K randomly generated 3D trajectories in synthetic layouts, with random but physically simulated object configurations. We compare the semantic segmentation performance of network weights produced from pretraining on RGB images from our dataset against generic VGG-16 ImageNet weights. After fine-tuning on the SUN RGB-D and NYUv2 real-world datasets we find in both cases that the synthetically pre-trained network outperforms the VGG-16 weights. When synthetic pre-training includes a depth channel (something ImageNet cannot natively provide) the performance is greater still. This suggests that large-scale high-quality synthetic RGB datasets with task-specific labels can be more useful for pretraining than real-world generic pre-training such as ImageNet. We host the dataset at http://robotvault. bitbucket.io/scenenet-rgbd.html.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"60 1","pages":"2697-2706"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89487958","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: 263

Semantic Line Detection and Its Applications 语义线检测及其应用

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.350

Jun-Tae Lee, Han-Ul Kim, Chulwoo Lee, Chang-Su Kim

引用次数: 36

Interleaved Group Convolutions 交错群卷积

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.469

Ting Zhang, Guo-Jun Qi, Bin Xiao, Jingdong Wang

引用次数: 216

Weakly Supervised Manifold Learning for Dense Semantic Object Correspondence 密集语义对象对应的弱监督流形学习

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.192

Utkarsh Gaur, B. S. Manjunath

{"title":"Weakly Supervised Manifold Learning for Dense Semantic Object Correspondence","authors":"Utkarsh Gaur, B. S. Manjunath","doi":"10.1109/ICCV.2017.192","DOIUrl":"https://doi.org/10.1109/ICCV.2017.192","url":null,"abstract":"The goal of the semantic object correspondence problem is to compute dense association maps for a pair of images such that the same object parts get matched for very different appearing object instances. Our method builds on the recent findings that deep convolutional neural networks (DCNNs) implicitly learn a latent model of object parts even when trained for classification. We also leverage a key correspondence problem insight that the geometric structure between object parts is consistent across multiple object instances. These two concepts are then combined in the form of a novel optimization scheme. This optimization learns a feature embedding by rewarding for projecting features closer on the manifold if they have low feature-space distance. Simultaneously, the optimization penalizes feature clusters whose geometric structure is inconsistent with the observed geometric structure of object parts. In this manner, by accounting for feature space similarities and feature neighborhood context together, a manifold is learned where features belonging to semantically similar object parts cluster together. We also describe transferring these embedded features to the sister tasks of semantic keypoint classification and localization task via a Siamese DCNN. We provide qualitative results on the Pascal VOC 2012 images and quantitative results on the Pascal Berkeley dataset where we improve on the state of the art by over 5% on classification and over 9% on localization tasks.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"68 1","pages":"1744-1752"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79550154","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

Should We Encode Rain Streaks in Video as Deterministic or Stochastic? 我们应该将视频中的雨条纹编码为确定性还是随机?

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.275

Wei Wei, Lixuan Yi, Qi Xie, Qian Zhao, Deyu Meng, Zongben Xu

{"title":"Should We Encode Rain Streaks in Video as Deterministic or Stochastic?","authors":"Wei Wei, Lixuan Yi, Qi Xie, Qian Zhao, Deyu Meng, Zongben Xu","doi":"10.1109/ICCV.2017.275","DOIUrl":"https://doi.org/10.1109/ICCV.2017.275","url":null,"abstract":"Videos taken in the wild sometimes contain unexpected rain streaks, which brings difficulty in subsequent video processing tasks. Rain streak removal in a video (RSRV) is thus an important issue and has been attracting much attention in computer vision. Different from previous RSRV methods formulating rain streaks as a deterministic message, this work first encodes the rains in a stochastic manner, i.e., a patch-based mixture of Gaussians. Such modification makes the proposed model capable of finely adapting a wider range of rain variations instead of certain types of rain configurations as traditional. By integrating with the spatiotemporal smoothness configuration of moving objects and low-rank structure of background scene, we propose a concise model for RSRV, containing one likelihood term imposed on the rain streak layer and two prior terms on the moving object and background scene layers of the video. Experiments implemented on videos with synthetic and real rains verify the superiority of the proposed method, as compared with the state-of-the-art methods, both visually and quantitatively in various performance metrics.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"58 1","pages":"2535-2544"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83934869","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: 115

Realistic Dynamic Facial Textures from a Single Image Using GANs 使用gan从单个图像中获得逼真的动态面部纹理

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.580

Kyle Olszewski, Zimo Li, Chao Yang, Yi Zhou, Ronald Yu, Zeng Huang, Sitao Xiang, Shunsuke Saito, Pushmeet Kohli, Hao Li

{"title":"Realistic Dynamic Facial Textures from a Single Image Using GANs","authors":"Kyle Olszewski, Zimo Li, Chao Yang, Yi Zhou, Ronald Yu, Zeng Huang, Sitao Xiang, Shunsuke Saito, Pushmeet Kohli, Hao Li","doi":"10.1109/ICCV.2017.580","DOIUrl":"https://doi.org/10.1109/ICCV.2017.580","url":null,"abstract":"We present a novel method to realistically puppeteer and animate a face from a single RGB image using a source video sequence. We begin by fitting a multilinear PCA model to obtain the 3D geometry and a single texture of the target face. In order for the animation to be realistic, however, we need dynamic per-frame textures that capture subtle wrinkles and deformations corresponding to the animated facial expressions. This problem is highly undercon-strained, as dynamic textures cannot be obtained directly from a single image. Furthermore, if the target face has a closed mouth, it is not possible to obtain actual images of the mouth interior. To address this issue, we train a Deep Generative Network that can infer realistic per-frame texture deformations, including the mouth interior, of the target identity using the per-frame source textures and the single target texture. By retargeting the PCA expression geometry from the source, as well as using the newly inferred texture, we can both animate the face and perform video face replacement on the source video using the target appearance.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"29 1","pages":"5439-5448"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77398188","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: 80

Efficient Online Local Metric Adaptation via Negative Samples for Person Re-identification 基于负样本的高效在线局部度量自适应人的再识别

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.265

Jiahuan Zhou, Pei Yu, Wei Tang, Ying Wu

{"title":"Efficient Online Local Metric Adaptation via Negative Samples for Person Re-identification","authors":"Jiahuan Zhou, Pei Yu, Wei Tang, Ying Wu","doi":"10.1109/ICCV.2017.265","DOIUrl":"https://doi.org/10.1109/ICCV.2017.265","url":null,"abstract":"Many existing person re-identification (PRID) methods typically attempt to train a faithful global metric offline to cover the enormous visual appearance variations, so as to directly use it online on various probes for identity match- ing. However, their need for a huge set of positive training pairs is very demanding in practice. In contrast to these methods, this paper advocates a different paradigm: part of the learning can be performed online but with nominal costs, so as to achieve online metric adaptation for different input probes. A major challenge here is that no positive training pairs are available for the probe anymore. By only exploiting easily-available negative samples, we propose a novel solution to achieve local metric adaptation effectively and efficiently. For each probe at the test time, it learns a strictly positive semi-definite dedicated local metric. Comparing to offline global metric learning, its com- putational cost is negligible. The insight of this new method is that the local hard negative samples can actually provide tight constraints to fine tune the metric locally. This new local metric adaptation method is generally applicable, as it can be used on top of any global metric to enhance its performance. In addition, this paper gives in-depth the- oretical analysis and justification of the new method. We prove that our new method guarantees the reduction of the classification error asymptotically, and prove that it actually learns the optimal local metric to best approximate the asymptotic case by a finite number of training data. Extensive experiments and comparative studies on almost all major benchmarks (VIPeR, QMUL GRID, CUHK Campus, CUHK03 and Market-1501) have confirmed the effectiveness and superiority of our method.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"20 1","pages":"2439-2447"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87609331","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: 80