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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281-290 订阅

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Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems 30

Residual Expansion Algorithm: Fast and Effective Optimizatio...

引用

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

作者： Ikami, Daiki Yamasaki, Toshihiko Aizawa, Kiyoharu Univ Tokyo Tokyo Japan

ISBN: (纸本)9781538604571

We propose the residual expansion (RE) algorithm: a global (or near-global) optimization method for nonconvex least squares problems. Unlike most existing nonconvex optimization techniques, the RE algorithm is not based on either stochastic or multi-point searches;therefore, it can achieve fast global optimization. Moreover, the RE algorithm is easy to implement and successful in highdimensional optimization. the RE algorithm exhibits excellent empirical performance in terms of k-means clustering, point-set registration, optimized product quantization, and blind image deblurring.

关键词： Convergence Clustering algorithms Linear programming computer vision Optimization methods Image restoration

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Teaching Compositionality to CNNs 30

Teaching Compositionality to CNNs

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

作者： Stone, Austin Wang, Huayan Stark, Michael Liu, Yi Phoenix, D. Scott George, Dileep Vicarious FPC San Francisco CA 94105 USA

ISBN: (纸本)9781538604571

Convolutional neural networks (CNNs) have shown great success in computer vision, approaching human-level performance when trained for specific tasks via application-specific loss functions. In this paper, we propose a method for augmenting and training CNNs so that their learned features are compositional. It encourages networks to form representations that disentangle objects from their surroundings and from each other, thereby promoting better generalization. Our method is agnostic to the specific details of the underlying CNN to which it is applied and can in principle be used with any CNN. As we show in our experiments, the learned representations lead to feature activations that are more localized and improve performance over non-compositional baselines in object recognition tasks.

关键词： Training Visualization Standards Airplanes computer vision Context modeling

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Pose-Aware Person recognition 30

Pose-Aware Person Recognition

引用

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

作者： Kumar, Vijay Namboodiri, Anoop Paluri, Manohar Jawahar, C. V. IIIT Hyderabad CVIT Hyderabad Andhra Pradesh India Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781538604571

Person recognition methods that use multiple body regions have shown significant improvements over traditional face-based recognition. One of the primary challenges in full-body person recognition is the extreme variation in pose and view point. In this work, (i) we present an approach that tackles pose variations utilizing multiple models that are trained on specific poses, and combined using pose-aware weights during testing. (ii) For learning a person representation, we propose a network that jointly optimizes a single loss over multiple body regions. (iii) Finally, we introduce new benchmarks to evaluate person recognition in diverse scenarios and show significant improvements over previously proposed approaches on all the benchmarks including the photo album setting of PIPA.

关键词： Persons recognition (Psychology) BENCHMARKS Tackles Body Regions photo albums Pipa

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Jointly Learning Energy Expenditures and Activities using Egocentric Multimodal Signals 30

Jointly Learning Energy Expenditures and Activities using Eg...

引用

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

作者： Nakamura, Katsuyuki Yeung, Serena Alahi, Alexandre Li Fei-Fei Stanford Univ Stanford CA 94305 USA Hitachi Ltd Tokyo Japan

ISBN: (纸本)9781538604571

Physiological signals such as heart rate can provide valuable information about an individual's state and activity. However, existing work on computer vision has not yet explored leveraging these signals to enhance egocentric video understanding. In this work, we propose a model for reasoning on multimodal data to jointly predict activities and energy expenditures. We use heart rate signals as privileged self-supervision to derive energy expenditure in a training stage. A multitask objective is used to jointly optimize the two tasks. Additionally, we introduce a dataset that contains 31 hours of egocentric video augmented with heart rate and acceleration signals. this study can lead to new applications such as a visual calorie counter.

关键词： Heart rate Acceleration Accelerometers Wearable sensors Cameras Activity recognition

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One-shot Hyperspectral Imaging using Faced Reflectors 30

One-shot Hyperspectral Imaging using Faced Reflectors

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

作者： Takatani, Tsuyoshi Aoto, Takahito Mukaigawa, Yasuhiro Nara Inst Sci & Technol Ikoma Japan Natl Inst Informat Tokyo Japan

ISBN: (纸本)9781538604571

Hyperspectral imaging is a useful technique for various computer vision tasks such as material recognition. However, such technique usually requires an expensive and professional setup and is time-consuming because a conventional hyperspectral image consists of a large number of observations. In this paper, we propose a novel technique of one-shot hyperspectral imaging using faced reflectors on which color filters are attached. the key idea is based on the principle that each of multiple reflections on the filters has a different spectrum, which allows us to observe multiple intensities through different spectra. Our technique can be implemented either by a coupled mirror or a kaleidoscope geometry. Experimental results show that our technique is capable of accurately capturing a hyperspectral image by using a coupled mirror setup which is readily available.

关键词： hyperspectral imagery reflectors images computer vision Filters multiple reflections Mirrors

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A Message Passing Algorithm for the Minimum Cost Multicut Problem 30

A Message Passing Algorithm for the Minimum Cost Multicut Pr...

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

作者： Swoboda, Paul Andres, Bjoern IST Austria Klosterneuburg Austria MPI Informat Saarbrucken Germany

ISBN: (纸本)9781538604571

We propose a dual decomposition and linear program relaxation of the NP-hard minimum cost multicut problem. Unlike other polyhedral relaxations of the multicut polytope, it is amenable to efficient optimization by message passing. Like other polyhedral relaxations, it can be tightened efficiently by cutting planes. We define an algorithm that alternates between message passing and efficient separation of cycle- and odd-wheel inequalities. this algorithm is more efficient than state-of-the-art algorithms based on linear programming, including algorithms written in the framework of leading commercial software, as we show in experiments with large instances of the problem from applications in computer vision, biomedical image analysis and data mining.

关键词： Wheels Software algorithms computer vision Image segmentation Message passing Optimization Algorithm design and analysis message passing Algorithm design and analysis Software algorithms images computer vision Wheels image segmentation multicut linear programming Commercial software

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Discovering Causal Signals in Images 30

Discovering Causal Signals in Images

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

作者： Lopez-Paz, David Nishihara, Robert Chintala, Soumith Schoelkopf, Bernhard Bottou, Leon Facebook AI Res Menlo Pk CA 94029 USA Univ Calif Berkeley Berkeley CA USA MPI Intelligent Syst Stuttgart Germany

ISBN: (纸本)9781538604571

this paper establishes the existence of observable footprints that reveal the "causal dispositions" of the object categories appearing in collections of images. We achieve this goal in two steps. First, we take a learning approach to observational causal discovery, and build a classifier that achieves state-of-the-art performance on finding the causal direction between pairs of random variables, given samples from their joint distribution. Second, we use our causal direction classifier to effectively distinguish between features of objects and features of their contexts in collections of static images. Our experiments demonstrate the existence of a relation between the direction of causality and the difference between objects and their contexts, and by the same token, the existence of observable signals that reveal the causal dispositions of objects.

关键词： Automobiles Bridges computer vision Wheels Facebook Artificial intelligence Random variables

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Spatiotemporal Multiplier Networks for Video Action recognition 30

Spatiotemporal Multiplier Networks for Video Action Recognit...

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

作者： Feichtenhofer, Christoph Pinz, Axel Wildes, Richard P. Graz Univ Technol Graz Austria York Univ Toronto ON Canada

ISBN: (纸本)9781538604571

this paper presents a general ConvNet architecture for video action recognition based on multiplicative interactions of spacetime features. Our model combines the appearance and motion pathways of a two-stream architecture by motion gating and is trained end-to-end. We theoretically motivate multiplicative gating functions for residual networks and empirically study their effect on classification accuracy. To capture long-term dependencies we inject identity mapping kernels for learning temporal relationships. Our architecture is fully convolutional in spacetime and able to evaluate a video in a single forward pass. Empirical investigation reveals that our model produces state-of-the-art results on two standard action recognition datasets.

关键词： Videotapes Spacetime ARCHITECTURE Building construction end-to-end recognition (Psychology) Multiplying circuits Identity mapping Multiplication

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the More You Know: Using Knowledge Graphs for Image Classification 30

The More You Know: Using Knowledge Graphs for Image Classifi...

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

作者： Marino, Kenneth Salakhutdinov, Ruslan Gupta, Abhinav Carnegie Mellon Univ 5000 Forbes Ave Pittsburgh PA 15213 USA

ISBN: (纸本)9781538604571

One characteristic that sets humans apart from modern learning-based computer vision algorithms is the ability to acquire knowledge about the world and use that knowledge to reason about the visual world. Humans can learn about the characteristics of objects and the relationships that occur between them to learn a large variety of visual concepts, often with few examples. this paper investigates the use of structured prior knowledge in the form of knowledge graphs and shows that using this knowledge improves performance on image classification. We build on recent work on end-to-end learning on graphs, introducing the Graph Search Neural Network as a way of efficiently incorporating large knowledge graphs into a vision classification pipeline. We show in a number of experiments that our method outperforms standard neural network baselines for multi-label classification.

关键词： Propagation losses conferences computer vision pattern recognition Mice Automobiles Bicycles

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Nonnegative Matrix Underapproximation for Robust Multiple Model Fitting 30

Nonnegative Matrix <i>Underapproximation</i> for Robust Mult...

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

作者： Tepper, Mariano Sapiro, Guillermo Simons Fdn Flatiron Inst New York NY 10010 USA Duke Univ ECE Durham NC 27706 USA Duke Univ Durham NC 27706 USA

ISBN: (纸本)9781538604571

In this work, we introduce a highly efficient algorithm to address the nonnegative matrix underapproximation (NMU) problem, i.e., nonnegative matrix factorization (NMF) with an additional underapproximation constraint. NMU results are interesting as, compared to traditional NMF, they present additional sparsity and part-based behavior, explaining unique data features. To show these features in practice, we first present an application to the analysis of climate data. We then present an NMU-based algorithm to robustly fit multiple parametric models to a dataset. the proposed approach delivers state-of-the-art results for the estimation of multiple fundamental matrices and homographies, outperforming other alternatives in the literature and exemplifying the use of efficient NMU computations.

关键词： Robustness Meteorology computer vision Computational modeling Parametric statistics pattern recognition Data models data models Computational modeling Meteorology images computer vision pattern recognition Parametric statistics mode matching Nonnegative NMU gene Robustness efficient algorithm Climatic data NMF protocol

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