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检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 21006 条记录，以下是171-180 订阅

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Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views 29

Deep Exemplar 2D-3D Detection by Adapting from Real to Rende...

引用

2016 ieee conference on computer vision and pattern recognition (cvpr)

作者： Massa, Francisco Russell, Bryan C. Aubry, Mathieu Univ Paris Est Ecole Ponts ParisTech LIGM CNRSUMR 8049ENPC F-77455 Marne La Vallee France Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9781467388511

This paper presents an end-to-end convolutional neural network (CNN) for 2D-3D exemplar detection. We demonstrate that the ability to adapt the features of natural images to better align with those of CAD rendered views is critical to the success of our technique. We show that the adaptation can be learned by compositing rendered views of textured object models on natural images. Our approach can be naturally incorporated into a CNN detection pipeline and extends the accuracy and speed benefits from recent advances in deep learning to 2D-3D exemplar detection. We applied our method to two tasks: instance detection, where we evaluated on the IKEA dataset [36], and object category detection, where we out-perform Aubry et al. [3] for "chair" detection on a subset of the Pascal VOC dataset.

关键词： computer aided design

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DeepFashion: Powering Robust Clothes recognition and Retrieval with Rich Annotations 29

DeepFashion: Powering Robust Clothes Recognition and Retriev...

引用

2016 ieee conference on computer vision and pattern recognition (cvpr)

作者： Liu, Ziwei Luo, Ping Qiu, Shi Wang, Xiaogang Tang, Xiaoou Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China SenseTime Grp Ltd Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9781467388511

Recent advances in clothes recognition have been driven by the construction of clothes datasets. Existing datasets are limited in the amount of annotations and are difficult to cope with the various challenges in real-world applications. In this work, we introduce DeepFashion(1), a large-scale clothes dataset with comprehensive annotations. It contains over 800,000 images, which are richly annotated with massive attributes, clothing landmarks, and correspondence of images taken under different scenarios including store, street snapshot, and consumer. Such rich annotations enable the development of powerful algorithms in clothes recognition and facilitating future researches. To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks. The estimated landmarks are then employed to pool or gate the learned features. It is optimized in an iterative manner. Extensive experiments demonstrate the effectiveness of FashionNet and the usefulness of DeepFashion.

关键词： Iterative methods

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Semantic Channels for Fast Pedestrian Detection 29

Semantic Channels for Fast Pedestrian Detection

引用

2016 ieee conference on computer vision and pattern recognition (cvpr)

作者： Costea, Arthur Daniel Nedevschi, Sergiu Tech Univ Cluj Napoca Image Proc & Pattern Recognit Res Ctr Cluj Napoca Romania

ISBN: (纸本)9781467388511

Pedestrian detection and semantic segmentation are high potential tasks for many real-time applications. However most of the top performing approaches provide state of art results at high computational costs. In this work we propose a fast solution for achieving state of art results for both pedestrian detection and semantic segmentation. As baseline for pedestrian detection we use sliding windows over cost efficient multiresolution filtered LUV+HOG channels. We use the same channels for classifying pixels into eight semantic classes. Using short range and long range multiresolution channel features we achieve more robust segmentation results compared to traditional codebook based approaches at much lower computational costs. The resulting segmentations are used as additional semantic channels in order to achieve a more powerful pedestrian detector. To also achieve fast pedestrian detection we employ a multiscale detection scheme based on a single flexible pedestrian model and a single image scale. The proposed solution provides competitive results on both pedestrian detection and semantic segmentation benchmarks at 8 FPS on CPU and at 15 FPS on GPU, being the fastest top performing approach.

关键词： Semantics

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ASP vision: Optically Computing the First Layer of Convolutional Neural Networks using Angle Sensitive Pixels 29

ASP Vision: Optically Computing the First Layer of Convoluti...

引用

2016 ieee conference on computer vision and pattern recognition (cvpr)

作者： Chen, Huaijin G. Jayasuriya, Suren Yang, Jiyue Stephen, Judy Sivaramakrishnan, Sriram Veeraraghavan, Ashok Molnar, Alyosha Rice Univ Houston TX 77251 USA Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781467388511

Deep learning using convolutional neural networks (CNNs) is quickly becoming the state-of-the-art for challenging computer vision applications. However, deep learning's power consumption and bandwidth requirements currently limit its application in embedded and mobile systems with tight energy budgets. In this paper, we explore the energy savings of optically computing the first layer of CNNs. To do so, we utilize bio-inspired Angle Sensitive Pixels (ASPs), custom CMOS diffractive image sensors which act similar to Gabor filter banks in the V1 layer of the human visual cortex. ASPs replace both image sensing and the first layer of a conventional CNN by directly performing optical edge filtering, saving sensing energy, data bandwidth, and CNN FLOPS to compute. Our experimental results (both on synthetic data and a hardware prototype) for a variety of vision tasks such as digit recognition, object recognition, and face identification demonstrate up to 90% reduction in image sensor power consumption and 90% reduction in data bandwidth from sensor to CPU, while achieving similar performance compared to traditional deep learning pipelines.

关键词： Pixels

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Refining Architectures of Deep Convolutional Neural Networks 29

Refining Architectures of Deep Convolutional Neural Networks

引用

2016 ieee conference on computer vision and pattern recognition (cvpr)

作者： Shankar, Sukrit Robertson, Duncan Ioannou, Yani Criminisi, Antonio Cipolla, Roberto Univ Cambridge Machine Intelligence Lab Cambridge England Microsoft Res Cambridge Cambridge England

ISBN: (纸本)9781467388511

Deep Convolutional Neural Networks (CNNs) have recently evinced immense success for various image recognition tasks [11, 27]. However, a question of paramount importance is somewhat unanswered in deep learning research - is the selected CNN optimal for the dataset in terms of accuracy and model size? In this paper, we intend to answer this question and introduce a novel strategy that alters the architecture of a given CNN for a specified dataset, to potentially enhance the original accuracy while possibly reducing the model size. We use two operations for architecture refinement, viz. stretching and symmetrical splitting. Stretching increases the number of hidden units (nodes) in a given CNN layer, while a symmetrical split of say K between two layers separates the input and output channels into K equal groups, and connects only the corresponding input-output channel groups. Our procedure starts with a pre-trained CNN for a given dataset, and optimally decides the stretch and split factors across the network to refine the architecture. We empirically demonstrate the necessity of the two operations. We evaluate our approach on two natural scenes attributes datasets, SUN Attributes [16] and CAMIT-NSAD [20], with architectures of GoogleNet and VGG-11, that are quite contrasting in their construction. We justify our choice of datasets, and show that they are interestingly distinct from each other, and together pose a challenge to our architectural refinement algorithm. Our results substantiate the usefulness of the proposed method.

关键词： Image recognition

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Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds 29

Kernel Sparse Subspace Clustering on Symmetric Positive Defi...

引用

2016 ieee conference on computer vision and pattern recognition (cvpr)

作者： Yin, Ming Guo, Yi Gao, Junbin He, Zhaoshui Xie, Shengli Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Western Sydney Univ Sch Comp Engn & Math Parramatta NSW 2150 Australia Univ Sydney Sch Business Camperdown NSW 2006 Australia

ISBN: (纸本)9781467388511

Sparse subspace clustering (SSC), as one of the most successful subspace clustering methods, has achieved notable clustering accuracy in computer vision tasks. However, SSC applies only to vector data in Euclidean space. Unfortunately there is still no satisfactory approach to solve subspace clustering by self-expressive principle for symmetric positive definite (SPD) matrices which is very useful in computer vision. In this paper, by embedding the SPD matrices into a Reproducing Kernel Hilbert Space (RKHS), a kernel subspace clustering method is constructed on the SPD manifold through an appropriate Log-Euclidean kernel, termed as kernel sparse subspace clustering on the SPD Riemannian manifold(KSSCR). By exploiting the intrinsic Riemannian geometry within data, KSSCR can effectively characterize the geodesic distance between SPD matrices to uncover the underlying subspace structure. Experimental results on several famous datasets demonstrate that the proposed method achieves better clustering results than the state-of-the-art approaches.

关键词： computer vision

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The Solution Path Algorithm for Identity-Aware Multi-Object Tracking 29

The Solution Path Algorithm for Identity-Aware Multi-Object ...

引用

2016 ieee conference on computer vision and pattern recognition (cvpr)

作者： Yu, Shoou-I Meng, Deyu Zuo, Wangmeng Hauptmann, Alexander Carnegie Mellon Univ Pittsburgh PA 15213 USA Xi An Jiao Tong Univ Xian Peoples R China Harbin Inst Technol Harbin Peoples R China

ISBN: (纸本)9781467388511

We propose an identity-aware multi-object tracker based on the solution path algorithm. Our tracker not only produces identity-coherent trajectories based on cues such as face recognition, but also has the ability to pinpoint potential tracking errors. The tracker is formulated as a quadratic optimization problem with l(0) norm constraints, which we propose to solve with the solution path algorithm. The algorithm successively solves the same optimization problem but under different l(p) norm constraints, where p gradually decreases from 1 to 0. Inspired by the success of the solution path algorithm in various machine learning tasks, this strategy is expected to converge to a better local minimum than directly minimizing the hardly solvable l(0) norm or the roughly approximated l(1) norm constraints. Furthermore, the acquired solution path complies with the "decision making process" of the tracker, which provides more insight to locating potential tracking errors. Experiments show that not only is our proposed tracker effective, but also the solution path enables automatic pinpointing of potential tracking failures, which can be readily utilized in an active learning framework to improve identity-aware multi-object tracking.

关键词： Face recognition

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Occlusion-free Face Alignment: Deep Regression Networks Coupled with De-corrupt AutoEncoders 29

Occlusion-free Face Alignment: Deep Regression Networks Coup...

引用

2016 ieee conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Jie Kan, Meina Shan, Shiguang Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China

ISBN: (纸本)9781467388511

Face alignment or facial landmark detection plays an important role in many computer vision applications, e.g., face recognition, facial expression recognition, face animation, etc. However, the performance of face alignment system degenerates severely when occlusions occur. In this work, we propose a novel face alignment method, which cascades several Deep Regression networks coupled with De-corrupt Autoencoders (denoted as DRDA) to explicitly handle partial occlusion problem. Different from the previous works that can only detect occlusions and discard the occluded parts, our proposed de-corrupt autoencoder network can automatically recover the genuine appearance for the occluded parts and the recovered parts can be leveraged together with those non-occluded parts for more accurate alignment. By coupling de-corrupt autoencoders with deep regression networks, a deep alignment model robust to partial occlusions is achieved. Besides, our method can localize occluded regions rather than merely predict whether the landmarks are occluded. Experiments on two challenging occluded face datasets demonstrate that our method significantly outperforms the state-of-the-art methods.

关键词： Face recognition

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Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data Is Continuous and Weakly Labelled 29

Deep Hand: How to Train a CNN on 1 Million Hand Images When ...

引用

2016 ieee conference on computer vision and pattern recognition (cvpr)

作者： Koller, Oscar Ney, Hermann Bowden, Richard Rhein Westfal TH Aachen Human Language Technol & Pattern Recog Aachen Germany Univ Surrey Ctr Vis Speech & Signal Proc Guildford Surrey England

ISBN: (纸本)9781467388511

This work presents a new approach to learning a frame-based classifier on weakly labelled sequence data by embedding a CNN within an iterative EM algorithm. This allows the CNN to be trained on a vast number of example images when only loose sequence level information is available for the source videos. Although we demonstrate this in the context of hand shape recognition, the approach has wider application to any video recognition task where frame level labelling is not available. The iterative EM algorithm leverages the discriminative ability of the CNN to iteratively refine the frame level annotation and subsequent training of the CNN. By embedding the classifier within an EM framework the CNN can easily be trained on 1 million hand images. We demonstrate that the final classifier generalises over both individuals and data sets. The algorithm is evaluated on over 3000 manually labelled hand shape images of 60 different classes which will be released to the community. Furthermore, we demonstrate its use in continuous sign language recognition on two publicly available large sign language data sets, where it outperforms the current state-of-the-art by a large margin. To our knowledge no previous work has explored expectation maximization without Gaussian mixture models to exploit weak sequence labels for sign language recognition.

关键词： Iterative methods

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Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking 29

Adaptive Decontamination of the Training Set: A Unified Form...

引用

2016 ieee conference on computer vision and pattern recognition (cvpr)

作者： Danelljan, Martin Hager, Gustav Khan, Fahad Shahbaz Felsberg, Michael Linkoping Univ Dept Elect Engn Comp Vis Lab Linkoping Sweden

ISBN: (纸本)9781467388511

Tracking-by-detection methods have demonstrated competitive performance in recent years. In these approaches, the tracking model heavily relies on the quality of the training set. Due to the limited amount of labeled training data, additional samples need to be extracted and labeled by the tracker itself. This often leads to the inclusion of corrupted training samples, due to occlusions, misalignments and other perturbations. Existing tracking-by-detection methods either ignore this problem, or employ a separate component for managing the training set. We propose a novel generic approach for alleviating the problem of corrupted training samples in tracking-by-detection frameworks. Our approach dynamically manages the training set by estimating the quality of the samples. Contrary to existing approaches, we propose a unified formulation by minimizing a single loss over both the target appearance model and the sample quality weights. The joint formulation enables corrupted samples to be down-weighted while increasing the impact of correct ones. Experiments are performed on three benchmarks: OTB-2015 with 100 videos, VOT-2015 with 60 videos, and Temple-Color with 128 videos. On the OTB-2015, our unified formulation significantly improves the baseline, with a gain of 3.8% in mean overlap precision. Finally, our method achieves state-of-the-art results on all three datasets.

关键词： computer vision

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