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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是721-730 订阅

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A Cause and Effect Analysis of Motion Trajectories for Modeling Actions 27

A Cause and Effect Analysis of Motion Trajectories for Model...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Narayan, Sanath Ramakrishnan, Kalpathi R. Indian Inst Sci Dept Elect Engn Bangalore 560012 Karnataka India

ISBN: (纸本)9781479951178

An action is typically composed of different parts of the object moving in particular sequences. the presence of different motions (represented as a 1D histogram) has been used in the traditional bag-of-words (BoW) approach for recognizing actions. However the interactions among the motions also form a crucial part of an action. Different object-parts have varying degrees of interactions with the other parts during an action cycle. It is these interactions we want to quantify in order to bring in additional information about the actions. In this paper we propose a causality based approach for quantifying the interactions to aid action classification. Granger causality is used to compute the cause and effect relationships for pairs of motion trajectories of a video. A 2D histogram descriptor for the video is constructed using these pairwise measures. Our proposed method of obtaining pairwise measures for videos is also applicable for large datasets. We have conducted experiments on challenging action recognition databases such as HMDB51 and UCF50 and shown that our causality descriptor helps in encoding additional information regarding the actions and performs on par with the state-of-the art approaches. Due to the complementary nature, a further increase in performance can be observed by combining our approach with state-of-the-art approaches.

关键词： Action recognition Granger Causality Interaction

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DeepFace: Closing the Gap to Human-Level Performance in Face Verification 27

DeepFace: Closing the Gap to Human-Level Performance in Face...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Taigman, Yaniv Yang, Ming Ranzato, Marc'Aurelio Wolf, Lior Facebook AI Res Menlo Pk CA 94205 USA Tel Aviv Univ IL-69978 Tel Aviv Israel

ISBN: (纸本)9781479951178

In modern face recognition, the conventional pipeline consists of four stages: detect double right arrow align double right arrow represent double right arrow classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. this deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4,000 identities. the learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.

关键词： Face recognition

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Sign Spotting using Hierarchical Sequential patterns with Temporal Intervals 27

Sign Spotting using Hierarchical Sequential Patterns with Te...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ong, Eng-Jon Koller, Oscar Pugeault, Nicolas Bowden, Richard Univ Surrey CVSSP Guildford GU27XH Surrey England

ISBN: (纸本)9781479951178

this paper tackles the problem of spotting a set of signs occuring in videos with sequences of signs. To achieve this, we propose to model the spatio-temporal signatures of a sign using an extension of sequential patterns that contain temporal intervals called Sequential Interval patterns (SIP). We then propose a novel multi-class classifier that organises different sequential interval patterns in a hierarchical tree structure called a Hierarchical SIP Tree (HSP-Tree). this allows one to exploit any subsequence sharing that exists between different SIPs of different classes. Multiple trees are then combined together into a forest of HSP-Trees resulting in a strong classifier that can be used to spot signs. We then show how the HSP-Forest can be used to spot sequences of signs that occur in an input video. We have evaluated the method on both concatenated sequences of isolated signs and continuous sign sequences. We also show that the proposed method is superior in robustness and accuracy to a state of the art sign recogniser when applied to spotting a sequence of signs.

关键词： Machine Learning Sequential pattern Learning Sign recognition Temporal Intervals

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Human Shape and Pose Tracking Using Keyframes 27

Human Shape and Pose Tracking Using Keyframes

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Huang, Chun-Hao Boyer, Edmond Navab, Nassir Ilic, Slobodan Tech Univ Munich D-80290 Munich Germany LJK INRIA Grenoble Rhone Alpes Grenoble France

ISBN: (纸本)9781479951178

this paper considers human tracking in multi-view setups and investigates a robust strategy that learns online key poses to drive a shape tracking method. the interest arises in realistic dynamic scenes where occlusions or segmentation errors occur. the corrupted observations present missing data and outliers that deteriorate tracking results. We propose to use key poses of the tracked person as multiple reference models. In contrast to many existing approaches that rely on a single reference model, multiple templates represent a larger variability of human poses. they provide therefore better initial hypotheses when tracking with noisy data. Our approach identifies these reference models online as distinctive keyframes during tracking. the most suitable one is then chosen as the reference at each frame. In addition, taking advantage of the proximity between successive frames, an efficient outlier handling technique is proposed to prevent from associating the model to irrelevant outliers. the two strategies are successfully experimented with a surface deformation framework that recovers both the pose and the shape. Evaluations on existing datasets also demonstrate their benefits with respect to the state of the art.

关键词： Gesture recognition

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Human Action recognition across Datasets by Foreground-weighted Histogram Decomposition 27

Human Action Recognition across Datasets by Foreground-weigh...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Sultani, Waqas Saleemi, Imran Univ Cent Florida Ctr Comp Vis Res Orlando FL 32816 USA

ISBN: (纸本)9781479951178

this paper attempts to address the problem of recognizing human actions while training and testing on distinct datasets, when test videos are neither labeled nor available during training. In this scenario, learning of a joint vocabulary, or domain transfer techniques are not applicable. We first explore reasons for poor classifier performance when tested on novel datasets, and quantify the effect of scene backgrounds on action representations and recognition. Using only the background features and partitioning of gist feature space, we show that the background scenes in recent datasets are quite discriminative and can be used classify an action with reasonable accuracy. We then propose a new process to obtain a measure of confidence in each pixel of the video being a foreground region, using motion, appearance, and saliency together in a 3D MRF based framework. We also propose multiple ways to exploit the foreground confidence: to improve bag-of-words vocabulary, histogram representation of a video, and a novel histogram decomposition based representation and kernel. We used these foreground confidences to recognize actions trained on one data set and test on a different data set. We have performed extensive experiments on several datasets that improve cross dataset recognition accuracy as compared to baseline methods.

关键词： action recognition cross dataset dataset bias foreground weighted representation

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Describing Textures in the Wild 27

Describing Textures in the Wild

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Cimpoi, Mircea Maji, Subhransu Kokkinos, Iasonas Mohamed, Sammy Vedaldi, Andrea Univ Oxford Oxford OX1 2JD England Toyota Technol Inst Nagoya Aichi Japan Ecole Cent Paris INRIA Saclay Paris France

ISBN: (纸本)9781479951178

patterns and textures are key characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at supporting this dimension in image understanding, we address the problem of describing textures with semantic attributes. We identify a vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected "in the wild". the resulting Describable Textures Dataset (DTD) is a basis to seek the best representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and Deep Convolutional-network Activation Features (DeCAF), and show that surprisingly, they both outperform specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that our describable attributes are excellent texture descriptors, transferring between datasets and tasks;in particular, combined with IFV and DeCAF, they significantly outperform the state-of-the-art by more than 10% on both FMD and Kth-TIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images.

关键词： Fisher Vector attribute convolutional neural network material recognition texture

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Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders 27

Who Do I Look Like? Determining Parent-Offspring Resemblance...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Dehghan, Afshin Ortiz, Enrique G. Villegas, Ruben Shah, Mubarak Univ Cent Florida Ctr Comp Vis Res Orlando FL 32816 USA

ISBN: (纸本)9781479951178

Recent years have seen a major push for face recognition technology due to the large expansion of image sharing on social networks. In this paper, we consider the difficult task of determining parent-offspring resemblance using deep learning to answer the question "Who do I look like?" Although humans can perform this job at a rate higher than chance, it is not clear how they do it [2]. However, recent studies in anthropology [24] have determined which features tend to be the most discriminative. In this study, we aim to not only create an accurate system for resemblance detection, but bridge the gap between studies in anthropology with computer vision techniques. Further, we aim to answer two key questions: 1) Do offspring resemble their parents? and 2) Do offspring resemble one parent more than the other? We propose an algorithm that fuses the features and metrics discovered via gated autoencoders with a discriminative neural network layer that learns the optimal, or what we call genetic, features to delineate parent-offspring relationships. We further analyze the correlation between our automatically detected features and those found in anthropological studies. Meanwhile, our method outperforms the state-of-the-art in kinship verification by 3-10% depending on the relationship using specific (father-son, mother-daughter, etc.) and generic models.

关键词： Deep Learning Face recognition Gated-autoencoder Kinship recognition Parent-Offspring Resemblence Facial recognition Anthropology GATING images Social Networks Parents Machine Learning computer vision descendant jobs Chance Answer Metronidazole

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Gait recognition under Speed Transition 27

Gait Recognition under Speed Transition

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Mansur, Al Makihara, Yasushi Aqmar, Rasyid Yagi, Yasushi Osaka Univ Inst Sci & Ind Res Ibaraki Osaka 5670047 Japan

ISBN: (纸本)9781479951178

this paper describes a method of gait recognition from image sequences wherein a subject is accelerating or decelerating. As a speed change occurs due to a change of pitch (the first-order derivative of a phase, namely, a gait stance) and/or stride, we model this speed change using a cylindrical manifold whose azimuth and height corresponds to the phase and the stride, respectively. A radial basis function (RBF) interpolation framework is used to learn subject specific mapping matrices for mapping from manifold to image space. Given an input image sequence of speed transited gait of a test subject, we estimate the mapping matrix of the test subject as well as the phase and stride sequence using an energy minimization framework considering the following three points: (1) fitness of the synthesized images to the input image sequence as well as to an eigenspace constructed by exemplars of training subjects;(2) smoothness of the phase and the stride sequence;and (3) pitch and stride fitness to the pitch-stride preference model. Using the estimated mapping matrix, we synthesize a constant-speed gait image sequence, and extract a conventional period-based gait feature from it for matching. We conducted experiments using real speed transited gait image sequences with 179 subjects and demonstrated the effectiveness of the proposed method.

关键词： image sequences Variable speed Locomotion Gait recognition Gait Phase MAPPING(MAthEMATICAL) Manifolds azimuth ENERGY MINIMISATION

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A Piggyback Representation for Action recognition

A Piggyback Representation for Action Recognition

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wolf, Lior Hanani, Yair Hassner, Tal Tel Aviv Univ Balvatnik Sch Comp Sci IL-69978 Tel Aviv Israel Open Univ Israel Dept Math & Comp Sci Raanana Israel

ISBN: (纸本)9781479943098

In video understanding, the spatial patterns formed by local space-time interest points hold discriminative information. We encode these spatial regularities using a word2vec neural network, a recently proposed tool in the field of text processing. then, building upon recent accumulator based image representation solutions, input videos are represented in a hybrid manner: the appearance of local space time interest points is used to collect and associate the learned descriptors, which capture the spatial patterns. Promising results are shown on recent action recognition benchmarks, using well established methods as the underlying appearance descriptors.

关键词： Text processing

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A Stream Algebra for computer vision Pipelines

A Stream Algebra for Computer Vision Pipelines

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Helala, Mohamed A. Pu, Ken Q. Qureshi, Faisal Z. Univ Ontario Inst Technol Fac Sci Oshawa ON Canada

ISBN: (纸本)9781479943098

Recent interest in developing online computer vision algorithms is spurred in part by a growth of applications capable of generating large volumes of images and videos. these applications are rich sources of images and video streams. Online vision algorithms for managing, processing and analyzing these streams need to rely upon streaming concepts, such as pipelines, to ensure timely and incremental processing of data. this paper is a first attempt at defining a formal stream algebra that provides a mathematical description of vision pipelines and describes the distributed manipulation of image and video streams. We also show how our algebra can effectively describe the vision pipelines of two state of the art techniques.

关键词： Image and Video Streams Stream Algebra computer vision Pipelines Stream Operators

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