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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是1501-1510 订阅

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InterActive: Inter-layer activeness propagation

InterActive: Inter-layer activeness propagation

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Xie, Lingxi Zheng, Liang Wang, Jingdong Yuille, Alan Tian, Qi Department of Statistics University of California Los Angeles Los AngelesCA United States Department of Computer Science University of Texas at San Antonio San AntonioTX United States Microsoft Research Beijing China Department of Cognitive Science and Computer Science Johns Hopkins University BaltimoreMD United States

ISBN: (纸本)9781467388511

An increasing number of computer vision tasks can be tackled with deep features, which are the intermediate outputs of a pre-trained Convolutional Neural Network. Despite the astonishing performance, deep features extracted from low-level neurons are still below satisfaction, arguably because they cannot access the spatial context contained in the higher layers. In this paper, we present InterActive, a novel algorithm which computes the activeness of neurons and network connections. Activeness is propagated through a neural network in a top-down manner, carrying highlevel context and improving the descriptive power of lowlevel and mid-level neurons. Visualization indicates that neuron activeness can be interpreted as spatial-weighted neuron responses. We achieve state-of-the-art classification performance on a wide range of image datasets.

关键词： Neural networks

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Consistency of silhouettes and their duals

Consistency of silhouettes and their duals

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Trager, Matthew Hebert, Martial Ponce, Jean Inria France Carnegie Mellon University United States Ecole Normale Superieure PSL Research University France

ISBN: (纸本)9781467388511

Silhouettes provide rich information on three-dimensional shape, since the intersection of the associated visual cones generates the "visual hull", which encloses and approximates the original shape. However, not all silhouettes can actually be projections of the same object in space: this simple observation has implications in object recognition and multi-view segmentation, and has been (often implicitly) used as a basis for camera calibration. In this paper, we investigate the conditions for multiple silhouettes, or more generally arbitrary closed image sets, to be geometrically "consistent". We present this notion as a natural generalization of traditional multi-view geometry, which deals with consistency for points. After discussing some general results, we present a "dual" formulation for consistency, that gives conditions for a family of planar sets to be sections of the same object. Finally, we introduce a more general notion of silhouette "compatibility" under partial knowledge of the camera projections, and point out some possible directions for future research.

关键词： computer vision

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A weighted variational model for simultaneous reflectance and illumination estimation

A weighted variational model for simultaneous reflectance an...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Fu, Xueyang Zeng, Delu Huang, Yue Zhang, Xiao-Ping Ding, Xinghao Fujian Key Laboratory of Sensing and Computing for Smart City Xiamen University China School of Information Science and Engineering Xiamen University China Department of Electrical and Computer Engineering Ryerson University Canada

ISBN: (纸本)9781467388511

We propose a weighted variational model to estimate both the reflectance and the illumination from an observed image. We show that, though it is widely adopted for ease of modeling, the log-transformed image for this task is not ideal. Based on the previous investigation of the logarithmic transformation, a new weighted variational model is proposed for better prior representation, which is imposed in the regularization terms. Different from conventional variational models, the proposed model can preserve the estimated reflectance with more details. Moreover, the proposed model can suppress noise to some extent. An alternating minimization scheme is adopted to solve the proposed model. Experimental results demonstrate the effectiveness of the proposed model with its algorithm. Compared with other variational methods, the proposed method yields comparable or better results on both subjective and objective assessments.

关键词： pattern recognition

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When VLAD met Hilbert

When VLAD met Hilbert

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Harandi, Mehrtash Salzmann, Mathieu Porikli, Fatih NICTA Australian National University Canberra Australia Switzerland

ISBN: (纸本)9781467388511

In many challenging visual recognition tasks where training data is limited, Vectors of Locally Aggregated Descriptors (VLAD) have emerged as powerful image/video representations that compete with or outperform state-ofthe-art approaches. In this paper, we address two fundamental limitations of VLAD: its requirement for the local descriptors to have vector form and its restriction to linear classifiers due to its high-dimensionality. To this end, we introduce a kernelized version of VLAD. This not only lets us inherently exploit more sophisticated classification schemes, but also enables us to efficiently aggregate nonvector descriptors (e.g., manifold-valued data) in the VLAD framework. Furthermore, we propose an approximate formulation that allows us to accelerate the coding process while still benefiting from the properties of kernel VLAD. Our experiments demonstrate the effectiveness of our approach at handling manifold-valued data, such as covariance descriptors, on several classification tasks. Our results also evidence the benefits of our nonlinear VLAD descriptors against the linear ones in Euclidean space using several standard benchmark datasets.

关键词： computer vision

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Slow and steady feature analysis: Higher order temporal coherence in video

Slow and steady feature analysis: Higher order temporal cohe...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Jayaraman, Dinesh Grauman, Kristen UT Austin United States

ISBN: (纸本)9781467388511

How can unlabeled video augment visual learning? Existing methods perform "slow" feature analysis, encouraging the representations of temporally close frames to exhibit only small differences. While this standard approach captures the fact that high-level visual signals change slowly over time, it fails to capture how the visual content changes. We propose to generalize slow feature analysis to "steady" feature analysis. The key idea is to impose a prior that higher order derivatives in the learned feature space must be small. To this end, we train a convolutional neural network with a regularizer on tuples of sequential frames from unlabeled video. It encourages feature changes over time to be smooth, i.e., similar to the most recent changes. Using five diverse datasets, including unlabeled YouTube and KITTI videos, we demonstrate our method's impact on object, scene, and action recognition tasks. We further show that our features learned from unlabeled video can even surpass a standard heavily supervised pretraining approach.

关键词： computer vision

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Scene recognition with CNNs: Objects, scales and dataset bias

Scene recognition with CNNs: Objects, scales and dataset bia...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Herranz, Luis Jiang, Shuqiang Li, Xiangyang Institute of Computer Technology CAS Beijing100190 China

ISBN: (纸本)9781467388511

Since scenes are composed in part of objects, accurate recognition of scenes requires knowledge about both scenes and objects. In this paper we address two related problems: 1) scale induced dataset bias in multi-scale convolutional neural network (CNN) architectures, and 2) how to combine effectively scene-centric and object-centric knowledge (i.e. Places and ImageNet) in CNNs. An earlier attempt, Hybrid-CNN[23], showed that incorporating ImageNet did not help much. Here we propose an alternative method taking the scale into account, resulting in significant recognition gains. By analyzing the response of ImageNet-CNNs and Places-CNNs at different scales we find that both operate in different scale ranges, so using the same network for all the scales induces dataset bias resulting in limited performance. Thus, adapting the feature extractor to each particular scale (i.e. scale-specific CNNs) is crucial to improve recognition, since the objects in the scenes have their specific range of scales. Experimental results show that the recognition accuracy highly depends on the scale, and that simple yet carefully chosen multi-scale combinations of ImageNet-CNNs and Places-CNNs, can push the stateof-the-art recognition accuracy in SUN397 up to 66.26% (and even 70.17% with deeper architectures, comparable to human performance).

关键词： computer vision

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Real-time action recognition with enhanced motion vector CNNs

Real-time action recognition with enhanced motion vector CNN...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Zhang, Bowen Wang, Limin Wang, Zhe Qiao, Yu Wang, Hanli Shenzhen Key Lab of Comp. Vis. and Pat. Rec. Shenzhen Institutes of Advanced Technology CAS China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Computer Vision Lab ETH Zurich Switzerland

ISBN: (纸本)9781467388511

The deep two-stream architecture [23] exhibited excellent performance on video based action recognition. The most computationally expensive step in this approach comes from the calculation of optical flow which prevents it to be real-time. This paper accelerates this architecture by replacing optical flow with motion vector which can be obtained directly from compressed videos without extra calculation. However, motion vector lacks fine structures, and contains noisy and inaccurate motion patterns, leading to the evident degradation of recognition performance. Our key insight for relieving this problem is that optical flow and motion vector are inherent correlated. Transferring the knowledge learned with optical flow CNN to motion vector CNN can significantly boost the performance of the latter. Specifically, we introduce three strategies for this, initialization transfer, supervision transfer and their combination. Experimental results show that our method achieves comparable recognition performance to the state-of-the-art, while our method can process 390.7 frames per second, which is 27 times faster than the original two-stream method.

关键词： pattern recognition

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Learning action maps of large environments via first-person vision

Learning action maps of large environments via first-person ...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Rhinehart, Nicholas Kitani, Kris M. Robotics Institute Carnegie Mellon University United States

ISBN: (纸本)9781467388511

When people observe and interact with physical spaces, they are able to associate functionality to regions in the environment. Our goal is to automate dense functional understanding of large spaces by leveraging sparse activity demonstrations recorded from an ego-centric viewpoint. The method we describe enables functionality estimation in large scenes where people have behaved, as well as novel scenes where no behaviors are observed. Our method learns and predicts "Action Maps", which encode the ability for a user to perform activities at various locations. With the usage of an egocentric camera to observe human activities, our method scales with the size of the scene without the need for mounting multiple static surveillance cameras and is well-suited to the task of observing activities up-close. We demonstrate that by capturing appearance-based attributes of the environment and associating these attributes with activity demonstrations, our proposed mathematical framework allows for the prediction of Action Maps in new environments. Additionally, we offer a preliminary glance of the applicability of Action Maps by demonstrating a proof-ofconcept application in which they are used in concert with activity detections to perform localization.

关键词： computer vision

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Rolling shutter camera relative pose: Generalized epipolar geometry

Rolling shutter camera relative pose: Generalized epipolar g...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Dai, Yuchao Li, Hongdong Kneip, Laurent Research School of Engineering Australian National University Australia ARC Centre of Excellence for Robotic Vision ACRV Australia

ISBN: (纸本)9781467388511

The vast majority of modern consumer-grade cameras employ a rolling shutter mechanism. In dynamic geometric computer vision applications such as visual SLAM, the so-called rolling shutter effect therefore needs to be properly taken into account. A dedicated relative pose solver appears to be the first problem to solve, as it is of eminent importance to bootstrap any derivation of multi-view geometry. However, despite its significance, it has received inadequate attention to date. This paper presents a detailed investigation of the geometry of the rolling shutter relative pose problem. We introduce the rolling shutter essential matrix, and establish its link to existing models such as the push-broom cameras, summarized in a clean hierarchy of multi-perspective cameras. The generalization of well-established concepts from epipolar geometry is completed by a definition of the Sampson distance in the rolling shutter case. The work is concluded with a careful investigation of the introduced epipolar geometry for rolling shutter cameras on several dedicated benchmarks.

关键词： Geometry

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Detecting repeating objects using patch correlation analysis

Detecting repeating objects using patch correlation analysis

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Huberman, Inbar Fattal, Raanan School of Computer Science and Engineering Hebrew University of Jerusalem Israel

ISBN: (纸本)9781467388511

In this paper we describe a new method for detecting and counting a repeating object in an image. While the method relies on a fairly sophisticated deformable part model, unlike existing techniques it estimates the model parameters in an unsupervised fashion thus alleviating the need for a user-annotated training data and avoiding the associated specificity. This automatic fitting process is carried out by exploiting the recurrence of small image patches associated with the repeating object and analyzing their spatial correlation. The analysis allows us to reject outlier patches, recover the visual and shape parameters of the part model, and detect the object instances efficiently. In order to achieve a practical system which is able to cope with diverse images, we describe a simple and intuitive active-learning procedure that updates the object classification by querying the user on very few carefully chosen marginal classifications. Evaluation of the new method against the state-of-the-art techniques demonstrates its ability to achieve higher accuracy through a better user experience.

关键词： Object detection

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