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检索条件"任意字段=30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017"

共 212 条记录，以下是161-170 订阅

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Temporal Action Co-segmentation in 3D Motion Capture Data and Videos 30

Temporal Action Co-segmentation in 3D Motion Capture Data an...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Papoutsakis, Konstantinos Panagiotakis, Costas Argyros, Antonis A. FORTH Inst Comp Sci Computat Vis & Robot Lab Iraklion Greece Univ Crete Dept Comp Sci Rethimnon Greece TEI Crete Business Adm Dept Agios Nikolaos Iraklion Greece

ISBN: (纸本)9781538604571

Given two action sequences, we are interested in spotting/co-segmenting all pairs of sub-sequences that represent the same action. We propose a totally unsupervised solution to this problem. No a-priori model of the actions is assumed to be available. the number of common sub-sequences may be unknown. the sub-sequences can be located anywhere in the original sequences, may differ in duration and the corresponding actions may be performed by a different person, in different style. We treat this type of temporal action co-segmentation as a stochastic optimization problem that is solved by employing Particle Swarm Optimization (PSO). the objective function that is minimized by PSO capitalizes on Dynamic Time Warping (DTW) to compare two action sub-sequences. Due to the generic problem formulation and solution, the proposed method can be applied to motion capture (i.e., 3D skeletal) data or to conventional RGB videos acquired in the wild. We present extensive quantitative experiments on standard data sets as well as on data sets we introduced in this paper. the obtained results demonstrate that the proposed method achieves a remarkable increase in co-segmentation quality compared to all tested state of the art methods.

关键词： Videos Optimization Image segmentation Linear programming computer vision three-dimensional displays computer science Videotapes three-dimensional displays computer science images computer vision image segmentation linear programming motion capture state of the art Dataset Action Particle swarm optimization

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Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation 30

Noisy Softmax: Improving the Generalization Ability of DCNN ...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Binghui Deng, Weihong Du, Junping Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781538604571

Over the past few years, softmax and SGD have become a commonly used component and the default training strategy in CNN frameworks, respectively. However, when optimizing CNNs with SGD, the saturation behavior behind softmax always gives us an illusion of training well and then is omitted. In this paper, we first emphasize that the early saturation behavior of softmax will impede the exploration of SGD, which sometimes is a reason for model converging at a bad local-minima, then propose Noisy Softmax to mitigating this early saturation issue by injecting annealed noise in softmax during each iteration. this operation based on noise injection aims at postponing the early saturation and further bringing continuous gradients propagation so as to significantly encourage SGD solver to be more exploratory and help to find a better local-minima. this paper empirically verifies the superiority of the early softmax desaturation, and our method indeed improves the generalization ability of CNN model by regularization. We experimentally find that this early desaturation helps optimization in many tasks, yielding state-of-the-art or competitive results on several popular benchmark datasets.

关键词： Training Noise measurement Annealing Standards Robustness computer vision Telecommunications

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Weakly Supervised Dense Video Captioning 30

Weakly Supervised Dense Video Captioning

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Shen, Zhiqiang Li, Jianguo Su, Zhou Li, Minjun Chen, Yurong Jiang, Yu-Gang Xue, Xiangyang Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China Intel Labs China Beijing Peoples R China

ISBN: (纸本)9781538604571

this paper focuses on a novel and challenging vision task, dense video captioning, which aims to automatically describe a video clip with multiple informative and diverse caption sentences. the proposed method is trained without explicit annotation of fine-grained sentence to video regionsequence correspondence, but is only based on weak videolevel sentence annotations. It differs from existing video captioning systems in three technical aspects. First, we propose lexical fully convolutional neural networks (LexicalFCN) with weakly supervised multi-instance multi-label learning to weakly link video regions with lexical labels. Second, we introduce a novel submodular maximization scheme to generate multiple informative and diverse regionsequences based on the Lexical-FCN outputs. A winnertakes- all scheme is adopted to weakly associate sentences to region-sequences in the training phase. third, a sequenceto- sequence learning based language model is trained with the weakly supervised information obtained through the association process. We show that the proposed method can not only produce informative and diverse dense captions, but also outperform state-of-the-art single video captioning methods by a large margin.

关键词： Visualization Training Motion segmentation Vocabulary computer vision Neural networks captions Motion segmentation images computer vision Neural network Visualization Vocabulary Functional training Videotapes video clips Training modelling languages

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Deep Hashing Network for Unsupervised Domain Adaptation 30

Deep Hashing Network for Unsupervised Domain Adaptation

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Venkateswara, Hemanth Eusebio, Jose Chakraborty, Shayok Panchanathan, Sethuraman Arizona State Univ Ctr Cognit Ubiquitous Comp Tempe AZ 85287 USA

ISBN: (纸本)9781538604571

In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a large amount of labeled data, which is an expensive process in terms of time, labor and human expertise. Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain. Further, the explosive growth of digital data has posed a fundamental challenge concerning its storage and retrieval. Due to its storage and retrieval efficiency, recent years have witnessed a wide application of hashing in a variety of computer vision applications. In this paper, we first introduce a new dataset, Office-Home, to evaluate domain adaptation algorithms. the dataset contains images of a variety of everyday objects from multiple domains. We then propose a novel deep learning framework that can exploit labeled source data and unlabeled target data to learn informative hash codes, to accurately classify unseen target data. To the best of our knowledge, this is the first research effort to exploit the feature learning capabilities of deep neural networks to learn representative hash codes to address the domain adaptation problem. Our extensive empirical studies on multiple transfer tasks corroborate the usefulness of the framework in learning efficient hash codes which outperform existing competitive baselines for unsupervised domain adaptation.

关键词： Neural networks Adaptation models Machine learning computer vision Training Data models

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Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach 30

Query-Focused Video Summarization: Dataset, Evaluation, and ...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sharghi, Aidean Laurel, Jacob S. Gong, Boqing Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA Univ Alabama Birmingham Dept Comp Sci Birmingham AL 35294 USA

ISBN: (纸本)9781538604571

Recent years have witnessed a resurgence of interest in video summarization. However, one of the main obstacles to the research on video summarization is the user subjectivity - users have various preferences over the summaries. the subjectiveness causes at least two problems. First, no single video summarizer fits all users unless it interacts with and adapts to the individual users. Second, it is very challenging to evaluate the performance of a video summarizer. To tackle the first problem, we explore the recently proposed query-focused video summarization which introduces user preferences in the form of text queries about the video into the summarization process. We propose a memory network parameterized sequential determinantal point process in order to attend the user query onto different video frames and shots. To address the second challenge, we contend that a good evaluation metric for video summarization should focus on the semantic information that humans can perceive rather than the visual features or temporal overlaps. To this end, we collect dense per-video-shot concept annotations, compile a new dataset, and suggest an efficient evaluation method defined upon the concept annotations. We conduct extensive experiments contrasting our video summarizer to existing ones and present detailed analyses about the dataset and the new evaluation method.

关键词： Semantics Measurement Visualization Automobiles Dictionaries computer vision Knowledge discovery Videotapes Dictionaries as Topic data mining Automobiles Semantics images computer vision Visualization evaluation method Dataset Cars semiotics

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Deep Learning Human Mind for Automated Visual Classification 30

Deep Learning Human Mind for Automated Visual Classification

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Spampinato, C. Palazzo, S. Kavasidis, I. Giordano, D. Souly, N. Shah, M. Dept Elect Elect & Comp Engn PeRCeiVe Lab Viale Andrea Doria 6 I-95125 Catania Italy Univ Cent Florida Ctr Res Comp Vis 4328 Scorpius StHEC 245D Orlando FL 32816 USA

ISBN: (纸本)9781538604571

What if we could effectively read the mind and transfer human visual capabilities to computer vision methods? In this paper, we aim at addressing this question by developing the first visual object classifier driven by human brain signals. In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories in a reading the mind effort. Afterward, we transfer the learned capabilities to machines by training a Convolutional Neural Network (CNN)-based regressor to project images onto the learned manifold, thus allowing machines to employ human brain-based features for automated visual classification. We use a 128-channel EEG with active electrodes to record brain activity of several subjects while looking at images of 40 ImageNet object classes. the proposed RNN-based approach for discriminating object classes using brain signals reaches an average accuracy of about 83%, which greatly outperforms existing methods attempting to learn EEG visual object representations. As for automated object categorization, our human brain-driven approach obtains competitive performance, comparable to those achieved by powerful CNN models and it is also able to generalize over different visual datasets.

关键词： images Visualization Mind wetware active electrode brain activity machine computer vision Automation recurrent neural nets Electroencephalography Human

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LCR-Net: Localization-Classification-Regression for Human Pose 30

LCR-Net: Localization-Classification-Regression for Human Po...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Rogez, Gregory Weinzaepfel, Philippe Schmid, Cordelia INRIA Villeneuve Dascq France Xerox Res Ctr Europe Grenoble France

ISBN: (纸本)9781538604571

We propose an end-to-end architecture for joint 2D and 3D human pose estimation in natural images. Key to our approach is the generation and scoring of a number of pose proposals per image, which allows us to predict 2D and 3D pose of multiple people simultaneously. Hence, our approach does not require an approximate localization of the humans for initialization. Our architecture, named LCR-Net, contains 3 main components: 1) the pose proposal generator that suggests potential poses at different locations in the image;2) a classifier that scores the different pose proposals;and 3) a regressor that refines pose proposals both in 2D and 3D. All three stages share the convolutional feature layers and are trained jointly. the final pose estimation is obtained by integrating over neighboring pose hypotheses, which is shown to improve over a standard non maximum suppression algorithm. Our approach significantly outperforms the state of the art in 3D pose estimation on Human3.6M, a controlled environment. Moreover, it shows promising results on real images for both single and multiperson subsets of the MPII 2D pose benchmark.

关键词： Two dimensional displays three-dimensional displays Proposals Pose estimation computer architecture Standards Training pose estimation three-dimensional displays computer Architecture Functional training Human state of the art images end-to-end

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On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation 30th

On-the-Fly Adaptation of Regression Forests for Online Camer...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Cavallari, Tommaso Golodetz, Stuart Lord, Nicholas A. Valentin, Julien Di Stefano, Luigi Torr, Philip H. S. Univ Bologna Dept Comp Sci & Engn Bologna Italy Univ Oxford Dept Engn Sci Oxford England Perceptiveio Inc San Jose CA USA

ISBN: (纸本)9781538604571

Camera relocalisation is an important problem in computer vision, with applications in simultaneous localisation and mapping, virtual/augmented reality and navigation. Common techniques either match the current image against keyframes with known poses coming from a tracker, or establish 2D-to-3D correspondences between keypoints in the current image and points in the scene in order to estimate the camera pose. Recently, regression forests have become a popular alternative to establish such correspondences. they achieve accurate results, but must be trained offline on the target scene, preventing relocalisation in new environments. In this paper, we show how to circumvent this limitation by adapting a pre-trained forest to a new scene on the fly. Our adapted forests achieve relocalisation performance that is on par with that of offline forests, and our approach runs in under 150ms, making it desirable for real-time systems that require online relocalisation.

关键词： Cameras three-dimensional displays Training Vegetation Reservoirs Pose estimation Image reconstruction reservoirs Current Pick Functional training OFF LINE computer vision pose estimation image reconstruction Forest Dams Reservoir waters vegetation images simultaneous localization and mapping Cameras three-dimensional displays

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Learning Object Interactions and Descriptions for Semantic Image Segmentation 30

Learning Object Interactions and Descriptions for Semantic I...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Guangrun Luo, Ping Lin, Liang Wang, Xiaogang Sun Yat Sen Univ Guangzhou Guangdong Peoples R China Chinese Univ Hong Kong Shatin Hong Kong Peoples R China SenseTime Grp Ltd Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Comp Vis & Pat Rec Beijing Peoples R China

ISBN: (纸本)9781538604571

Recent advanced deep convolutional networks (CNNs) achieved great successes in many computer vision tasks, because of their compelling learning complexity and the presences of large-scale labeled data. However, as obtaining per-pixel annotations is expensive, performances of CNNs in semantic image segmentation are not fully exploited. this work significantly increases segmentation accuracy of CNNs by learning from an Image Descriptions in the Wild (IDW) dataset. Unlike previous image captioning datasets, where captions were manually and densely annotated, images and their descriptions in IDW are automatically downloaded from Internet without any manual cleaning and refinement. An IDW-CNN is proposed to jointly train IDW and existing image segmentation dataset such as Pascal VOC 2012 (VOC). It has two appealing properties. First, knowledge from different datasets can be fully explored and transferred from each other to improve performance. Second, segmentation accuracy in VOC can be constantly increased when selecting more data from IDW. Extensive experiments demonstrate the effectiveness and scalability of IDW-CNN, which outperforms existing best-performing system by 12% on VOC12 test set.

关键词： image segmentation captions Volatile Organic Compounds images Dataset Learning Objects Semantics computer vision Scalability

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Consensus Maximization with Linear Matrix Inequality Constraints 30

Consensus Maximization with Linear Matrix Inequality Constra...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Speciale, Pablo Paudel, Danda P. Oswald, Martin R. Kroeger, Till Gool, Luc V. Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Swiss Fed Inst Technol D ITET Comp Vis Lab Zurich Switzerland Microsoft Corp Redmond WA 98052 USA Katholieke Univ Leuven ESAT PSI VISICS Leuven Belgium

ISBN: (纸本)9781538604571

Consensus maximization has proven to be a useful tool for robust estimation. While randomized methods like RANSAC are fast, they do not guarantee global optimality and fail to manage large amounts of outliers. On the other hand, global methods are commonly slow because they do not exploit the structure of the problem at hand. In this paper, we show that the solution space can be reduced by introducing Linear Matrix Inequality (LMI) constraints. this leads to significant speed ups of the optimization time even for large amounts of outliers, while maintaining global optimality. We study several cases in which the objective variables have a special structure, such as rotation, scaled-rotation, and essential matrices, which are posed as LMI constraints. this is very useful in several standard computer vision problems, such as estimating Similarity Transformations, Absolute Poses, and Relative Poses, for which we obtain compelling results on both synthetic and real datasets. With up to 90 percent outlier rate, where RANSAC often fails, our constrained approach is consistently faster than the non-constrained one - while finding the same global solution.

关键词： outliers Solution space linear matrix inequalities Special structures estimation theory Interim Local Management Interface Hand images computer vision Speeding optimality Posing

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