2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)最新文献

Semantically Coherent Co-Segmentation and Reconstruction of Dynamic Scenes 动态场景的语义连贯共分割与重构

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-11-09 DOI: 10.1109/CVPR.2017.592

A. Mustafa, A. Hilton

{"title":"Semantically Coherent Co-Segmentation and Reconstruction of Dynamic Scenes","authors":"A. Mustafa, A. Hilton","doi":"10.1109/CVPR.2017.592","DOIUrl":"https://doi.org/10.1109/CVPR.2017.592","url":null,"abstract":"In this paper we propose a framework for spatially and temporally coherent semantic co-segmentation and reconstruction of complex dynamic scenes from multiple static or moving cameras. Semantic co-segmentation exploits the coherence in semantic class labels both spatially, between views at a single time instant, and temporally, between widely spaced time instants of dynamic objects with similar shape and appearance. We demonstrate that semantic coherence results in improved segmentation and reconstruction for complex scenes. A joint formulation is proposed for semantically coherent object-based co-segmentation and reconstruction of scenes by enforcing consistent semantic labelling between views and over time. Semantic tracklets are introduced to enforce temporal coherence in semantic labelling and reconstruction between widely spaced instances of dynamic objects. Tracklets of dynamic objects enable unsupervised learning of appearance and shape priors that are exploited in joint segmentation and reconstruction. Evaluation on challenging indoor and outdoor sequences with hand-held moving cameras shows improved accuracy in segmentation, temporally coherent semantic labelling and 3D reconstruction of dynamic scenes.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"27 1","pages":"5583-5592"},"PeriodicalIF":0.0,"publicationDate":"2017-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83062724","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: 48

FFTLasso: Large-Scale LASSO in the Fourier Domain FFTLasso:傅里叶域的大规模LASSO

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-11-09 DOI: 10.1109/CVPR.2017.465

Adel Bibi, Hani Itani, Bernard Ghanem

{"title":"FFTLasso: Large-Scale LASSO in the Fourier Domain","authors":"Adel Bibi, Hani Itani, Bernard Ghanem","doi":"10.1109/CVPR.2017.465","DOIUrl":"https://doi.org/10.1109/CVPR.2017.465","url":null,"abstract":"In this paper, we revisit the LASSO sparse representation problem, which has been studied and used in a variety of different areas, ranging from signal processing and information theory to computer vision and machine learning. In the vision community, it found its way into many important applications, including face recognition, tracking, super resolution, image denoising, to name a few. Despite advances in efficient sparse algorithms, solving large-scale LASSO problems remains a challenge. To circumvent this difficulty, people tend to downsample and subsample the problem (e.g. via dimensionality reduction) to maintain a manageable sized LASSO, which usually comes at the cost of losing solution accuracy. This paper proposes a novel circulant reformulation of the LASSO that lifts the problem to a higher dimension, where ADMM can be efficiently applied to its dual form. Because of this lifting, all optimization variables are updated using only basic element-wise operations, the most computationally expensive of which is a 1D FFT. In this way, there is no need for a linear system solver nor matrix-vector multiplication. Since all operations in our FFTLasso method are element-wise, the subproblems are completely independent and can be trivially parallelized (e.g. on a GPU). The attractive computational properties of FFTLasso are verified by extensive experiments on synthetic and real data and on the face recognition task. They demonstrate that FFTLasso scales much more effectively than a state-of-the-art solver.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"1 1","pages":"4371-4380"},"PeriodicalIF":0.0,"publicationDate":"2017-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74128107","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: 3

Coarse-to-Fine Segmentation with Shape-Tailored Continuum Scale Spaces 形状定制连续尺度空间的粗到精分割

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-11-09 DOI: 10.1109/CVPR.2017.188

Naeemullah Khan, Byung-Woo Hong, A. Yezzi, G. Sundaramoorthi

引用次数: 8

Multi-way Multi-level Kernel Modeling for Neuroimaging Classification 神经成像分类的多路多级核建模

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-11-06 DOI: 10.1109/CVPR.2017.724

Lifang He, Chun-Ta Lu, Hao Ding, Shen Wang, L. Shen, Philip S. Yu, A. Ragin

{"title":"Multi-way Multi-level Kernel Modeling for Neuroimaging Classification","authors":"Lifang He, Chun-Ta Lu, Hao Ding, Shen Wang, L. Shen, Philip S. Yu, A. Ragin","doi":"10.1109/CVPR.2017.724","DOIUrl":"https://doi.org/10.1109/CVPR.2017.724","url":null,"abstract":"Owing to prominence as a diagnostic tool for probing the neural correlates of cognition, neuroimaging tensor data has been the focus of intense investigation. Although many supervised tensor learning approaches have been proposed, they either cannot capture the nonlinear relationships of tensor data or cannot preserve the complex multi-way structural information. In this paper, we propose a Multi-way Multi-level Kernel (MMK) model that can extract discriminative, nonlinear and structural preserving representations of tensor data. Specifically, we introduce a kernelized CP tensor factorization technique, which is equivalent to performing the low-rank tensor factorization in a possibly much higher dimensional space that is implicitly defined by the kernel function. We further employ a multi-way nonlinear feature mapping to derive the dual structural preserving kernels, which are used in conjunction with kernel machines (e.g., SVM). Extensive experiments on real-world neuroimages demonstrate that the proposed MMK method can effectively boost the classification performance on diverse brain disorders (i.e., Alzheimers disease, ADHD, and HIV).","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"8 1","pages":"6846-6854"},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79881631","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 Gap Detection and Inpainting of Line Drawings 接缝缝隙检测与线形图补漆

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-11-06 DOI: 10.1109/CVPR.2017.611

Kazuma Sasaki, S. Iizuka, E. Simo-Serra, H. Ishikawa

引用次数: 37

Wetness and Color from a Single Multispectral Image 单幅多光谱图像的湿度和颜色

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-11-06 DOI: 10.1109/CVPR.2017.42

Mihoko Shimano, Hiroki Okawa, Yuta Asano, Ryoma Bise, K. Nishino, Imari Sato

{"title":"Wetness and Color from a Single Multispectral Image","authors":"Mihoko Shimano, Hiroki Okawa, Yuta Asano, Ryoma Bise, K. Nishino, Imari Sato","doi":"10.1109/CVPR.2017.42","DOIUrl":"https://doi.org/10.1109/CVPR.2017.42","url":null,"abstract":"Visual recognition of wet surfaces and their degrees of wetness is important for many computer vision applications. It can inform slippery spots on a road to autonomous vehicles, muddy areas of a trail to humanoid robots, and the freshness of groceries to us. In the past, monochromatic appearance change, the fact that surfaces darken when wet, has been modeled to recognize wet surfaces. In this paper, we show that color change, particularly in its spectral behavior, carries rich information about a wet surface. We derive an analytical spectral appearance model of wet surfaces that expresses the characteristic spectral sharpening due to multiple scattering and absorption in the surface. We derive a novel method for estimating key parameters of this spectral appearance model, which enables the recovery of the original surface color and the degree of wetness from a single observation. Applied to a multispectral image, the method estimates the spatial map of wetness together with the dry spectral distribution of the surface. To our knowledge, this work is the first to model and leverage the spectral characteristics of wet surfaces to revert its appearance. We conduct comprehensive experimental validation with a number of wet real surfaces. The results demonstrate the accuracy of our model and the effectiveness of our method for surface wetness and color estimation.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"195 1","pages":"321-329"},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79813625","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: 5

Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification 学习身体和潜在部位的深度上下文感知特征，用于人的再识别

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-10-18 DOI: 10.1109/CVPR.2017.782

Dangwei Li, Xiaotang Chen, Z. Zhang, Kaiqi Huang

{"title":"Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification","authors":"Dangwei Li, Xiaotang Chen, Z. Zhang, Kaiqi Huang","doi":"10.1109/CVPR.2017.782","DOIUrl":"https://doi.org/10.1109/CVPR.2017.782","url":null,"abstract":"Person Re-identification (ReID) is to identify the same person across different cameras. It is a challenging task due to the large variations in person pose, occlusion, background clutter, etc. How to extract powerful features is a fundamental problem in ReID and is still an open problem today. In this paper, we design a Multi-Scale Context-Aware Network (MSCAN) to learn powerful features over full body and body parts, which can well capture the local context knowledge by stacking multi-scale convolutions in each layer. Moreover, instead of using predefined rigid parts, we propose to learn and localize deformable pedestrian parts using Spatial Transformer Networks (STN) with novel spatial constraints. The learned body parts can release some difficulties, e.g. pose variations and background clutters, in part-based representation. Finally, we integrate the representation learning processes of full body and body parts into a unified framework for person ReID through multi-class person identification tasks. Extensive evaluations on current challenging large-scale person ReID datasets, including the image-based Market1501, CUHK03 and sequence-based MARS datasets, show that the proposed method achieves the state-of-the-art results.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"116 1","pages":"7398-7407"},"PeriodicalIF":0.0,"publicationDate":"2017-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80351792","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: 621

A Low Power, Fully Event-Based Gesture Recognition System 一个低功耗，完全基于事件的手势识别系统

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-25 DOI: 10.1109/CVPR.2017.781

A. Amir, B. Taba, David J. Berg, T. Melano, J. McKinstry, C. D. Nolfo, T. Nayak, Alexander Andreopoulos, Guillaume J. Garreau, Marcela Mendoza, J. Kusnitz, M. DeBole, Steven K. Esser, T. Delbrück, M. Flickner, D. Modha

{"title":"A Low Power, Fully Event-Based Gesture Recognition System","authors":"A. Amir, B. Taba, David J. Berg, T. Melano, J. McKinstry, C. D. Nolfo, T. Nayak, Alexander Andreopoulos, Guillaume J. Garreau, Marcela Mendoza, J. Kusnitz, M. DeBole, Steven K. Esser, T. Delbrück, M. Flickner, D. Modha","doi":"10.1109/CVPR.2017.781","DOIUrl":"https://doi.org/10.1109/CVPR.2017.781","url":null,"abstract":"We present the first gesture recognition system implemented end-to-end on event-based hardware, using a TrueNorth neurosynaptic processor to recognize hand gestures in real-time at low power from events streamed live by a Dynamic Vision Sensor (DVS). The biologically inspired DVS transmits data only when a pixel detects a change, unlike traditional frame-based cameras which sample every pixel at a fixed frame rate. This sparse, asynchronous data representation lets event-based cameras operate at much lower power than frame-based cameras. However, much of the energy efficiency is lost if, as in previous work, the event stream is interpreted by conventional synchronous processors. Here, for the first time, we process a live DVS event stream using TrueNorth, a natively event-based processor with 1 million spiking neurons. Configured here as a convolutional neural network (CNN), the TrueNorth chip identifies the onset of a gesture with a latency of 105 ms while consuming less than 200 mW. The CNN achieves 96.5% out-of-sample accuracy on a newly collected DVS dataset (DvsGesture) comprising 11 hand gesture categories from 29 subjects under 3 illumination conditions.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"120 1","pages":"7388-7397"},"PeriodicalIF":0.0,"publicationDate":"2017-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87774856","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: 505

Temporal Residual Networks for Dynamic Scene Recognition 动态场景识别的时间残差网络

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-22 DOI: 10.1109/CVPR.2017.786

Christoph Feichtenhofer, A. Pinz, Richard P. Wildes

{"title":"Temporal Residual Networks for Dynamic Scene Recognition","authors":"Christoph Feichtenhofer, A. Pinz, Richard P. Wildes","doi":"10.1109/CVPR.2017.786","DOIUrl":"https://doi.org/10.1109/CVPR.2017.786","url":null,"abstract":"This paper combines three contributions to establish a new state-of-the-art in dynamic scene recognition. First, we present a novel ConvNet architecture based on temporal residual units that is fully convolutional in spacetime. Our model augments spatial ResNets with convolutions across time to hierarchically add temporal residuals as the depth of the network increases. Second, existing approaches to video-based recognition are categorized and a baseline of seven previously top performing algorithms is selected for comparative evaluation on dynamic scenes. Third, we introduce a new and challenging video database of dynamic scenes that more than doubles the size of those previously available. This dataset is explicitly split into two subsets of equal size that contain videos with and without camera motion to allow for systematic study of how this variable interacts with the defining dynamics of the scene per se. Our evaluations verify the particular strengths and weaknesses of the baseline algorithms with respect to various scene classes and camera motion parameters. Finally, our temporal ResNet boosts recognition performance and establishes a new state-of-the-art on dynamic scene recognition, as well as on the complementary task of action recognition.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"24 1","pages":"7435-7444"},"PeriodicalIF":0.0,"publicationDate":"2017-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85336856","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: 75

Consistent-Aware Deep Learning for Person Re-identification in a Camera Network 摄像机网络中一致性感知深度学习的人物再识别

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.362

Ji Lin, Liangliang Ren, Jiwen Lu, Jianjiang Feng, Jie Zhou

引用次数: 119