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Real-time depth refinement for specular objects

Real-time depth refinement for specular objects

引用

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

作者： El, Roy Or Hershkovitz, Rom Wetzler, Aaron Rosman, Guy Bruckstein, Alfred M. Kimmel, Ron Technion Israel Institute of Technology Israel Computer Science and Artificial Intelligence Lab MIT Israel

ISBN: (纸本)9781467388511

The introduction of consumer RGB-D scanners set off a major boost in 3D computer vision research. Yet, the precision of existing depth scanners is not accurate enough to recover fine details of a scanned object. While modern shading based depth refinement methods have been proven to work well with Lambertian objects, they break down in the presence of specularities. We present a novel shape from shading framework that addresses this issue and enhances both diffuse and specular objects' depth profiles. We take advantage of the built-in monochromatic IR projector and IR images of the RGB-D scanners and present a lighting model that accounts for the specular regions in the input image. Using this model, we reconstruct the depth map in real-time. Both quantitative tests and visual evaluations prove that the proposed method produces state of the art depth reconstruction results.

关键词： computer vision

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Pose-Aware face recognition in the wild

Pose-Aware face recognition in the wild

引用

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

作者： Masi, Iacopo Rawls, Stephen Medioni, Gérard Natarajan, Prem Los AngelesCA United States Marina Del ReyCA United States

ISBN: (纸本)9781467388511

We propose a method to push the frontiers of unconstrained face recognition in the wild, focusing on the problem of extreme pose variations. As opposed to current techniques which either expect a single model to learn pose invariance through massive amounts of training data, or which normalize images to a single frontal pose, our method explicitly tackles pose variation by using multiple posespecific models and rendered face images. We leverage deep Convolutional Neural Networks (CNNs) to learn discriminative representations we call Pose-Aware Models (PAMs) using 500K images from the CASIA WebFace dataset. We present a comparative evaluation on the new IARPA Janus Benchmark A (IJB-A) and PIPA datasets. On these datasets PAMs achieve remarkably better performance than commercial products and surprisingly also outperform methods that are specifically fine-tuned on the target dataset.

关键词： Face recognition

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End-to-end learning of action detection from frame glimpses in videos

End-to-end learning of action detection from frame glimpses ...

引用

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

作者： Yeung, Serena Russakovsky, Olga Mori, Greg Fei-Fei, Li Stanford University United States Carnegie Mellon University United States Simon Fraser University United States

ISBN: (纸本)9781467388511

In this work we introduce a fully end-to-end approach for action detection in videos that learns to directly predict the temporal bounds of actions. Our intuition is that the process of detecting actions is naturally one of observation and refinement: observing moments in video, and refining hypotheses about when an action is occurring. Based on this insight, we formulate our model as a recurrent neural network-based agent that interacts with a video over time. The agent observes video frames and decides both where to look next and when to emit a prediction. Since backpropagation is not adequate in this non-differentiable setting, we use REINFORCE to learn the agent's decision policy. Our model achieves state-of-the-art results on the THUMOS'14 and ActivityNet datasets while observing only a fraction (2% or less) of the video frames.

关键词： computer vision

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Dynamic image networks for action recognition

Dynamic image networks for action recognition

引用

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

作者： Bilen, Hakan Fernando, Basura Gavves, Efstratios Vedaldi, Andrea Gould, Stephen University of Oxford United Kingdom Australian National University Australia QUVA Lab. University of Amsterdam Netherlands

ISBN: (纸本)9781467388511

We introduce the concept of dynamic image, a novel compact representation of videos useful for video analysis especially when convolutional neural networks (CNNs) are used. The dynamic image is based on the rank pooling concept and is obtained through the parameters of a ranking machine that encodes the temporal evolution of the frames of the video. Dynamic images are obtained by directly applying rank pooling on the raw image pixels of a video producing a single RGB image per video. This idea is simple but powerful as it enables the use of existing CNN models directly on video data with fine-tuning. We present an efficient and effective approximate rank pooling operator, speeding it up orders of magnitude compared to rank pooling. Our new approximate rank pooling CNN layer allows us to generalize dynamic images to dynamic feature maps and we demonstrate the power of our new representations on standard benchmarks in action recognition achieving state-of-the-art performance.

关键词： computer vision

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Structure inference machines: Recurrent neural networks for analyzing relations in group activity recognition

Structure inference machines: Recurrent neural networks for ...

引用

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

作者： Deng, Zhiwei Vahdat, Arash Hu, Hexiang Mori, Greg School of Computer Science Simon Fraser University Canada

ISBN: (纸本)9781467388511

Rich semantic relations are important in a variety of visual recognition problems. As a concrete example, group activity recognition involves the interactions and relative spatial relations of a set of people in a scene. State of the art recognition methods center on deep learning approaches for training highly effective, complex classifiers for interpreting images. However, bridging the relatively low-level concepts output by these methods to interpret higher-level compositional scenes remains a challenge. Graphical models are a standard tool for this task. In this paper, we propose a method to integrate graphical models and deep neural networks into a joint framework. Instead of using a traditional inference method, we use a sequential inference modeled by a recurrent neural network. Beyond this, the appropriate structure for inference can be learned by imposing gates on edges between nodes. Empirical results on group activity recognition demonstrate the potential of this model to handle highly structured learning tasks.

关键词： Recurrent neural networks

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Efficient globally optimal 2D-to-3D deformable shape matching

Efficient globally optimal 2D-to-3D deformable shape matchin...

引用

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

作者： Lähner, Zorah Rodolà, Emanuele Schmidt, Frank R. Bronstein, Michael M. Cremers, Daniel TU München Germany Università della Svizzera Italiana Italy

ISBN: (纸本)9781467388511

We propose the first algorithm for non-rigid 2D-to-3D shape matching, where the input is a 2D query shape as well as a 3D target shape and the output is a continuous matching curve represented as a closed contour on the 3D shape. We cast the problem as finding the shortest circular path on the product 3-manifold of the two shapes. We prove that the optimal matching can be computed in polynomial time with a (worst-case) complexity of O(mn2log(n)), wherem and n denote the number of vertices on the 2D and the 3D shape respectively. Quantitative evaluation confirms that the method provides excellent results for sketch-based deformable 3D shape retrieval.

关键词： computer vision

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Large-scale location recognition and the geometric burstiness problem

Large-scale location recognition and the geometric burstines...

引用

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

作者： Sattler, Torsten Havlena, Michal Schindler, Konrad Pollefeys, Marc Department of Computer Science ETH Zürich Switzerland Computer Vision Laboratory ETH Zürich Switzerland Institute of Geodesy and Photogrammetry ETH Zürich Switzerland

ISBN: (纸本)9781467388511

Visual location recognition is the task of determining the place depicted in a query image from a given database of geo-tagged images. Location recognition is often cast as an image retrieval problem and recent research has almost exclusively focused on improving the chance that a relevant database image is ranked high enough after retrieval. The implicit assumption is that the number of inliers found by spatial verification can be used to distinguish between a related and an unrelated database photo with high precision. In this paper, we show that this assumption does not hold for large datasets due to the appearance of geometric bursts, i.e., sets of visual elements appearing in similar geometric configurations in unrelated database photos. We propose algorithms for detecting and handling geometric bursts. Although conceptually simple, using the proposed weighting schemes dramatically improves the recall that can be achieved when high precision is required compared to the standard re-ranking based on the inlier count. Our approach is easy to implement and can easily be integrated into existing location recognition systems.

关键词： Image enhancement

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Learning aligned cross-modal representations from weakly aligned data

Learning aligned cross-modal representations from weakly ali...

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

作者： Castrejón, Lluís Aytar, Yusuf Vondrick, Carl Pirsiavash, Hamed Torralba, Antonio University of Toronto Canada MIT CSAIL United States University of Maryland Baltimore County United States

ISBN: (纸本)9781467388511

People can recognize scenes across many different modalities beyond natural images. In this paper, we investigate how to learn cross-modal scene representations that transfer across modalities. To study this problem, we introduce a new cross-modal scene dataset. While convolutional neural networks can categorize cross-modal scenes well, they also learn an intermediate representation not aligned across modalities, which is undesirable for crossmodal transfer applications. We present methods to regularize cross-modal convolutional neural networks so that they have a shared representation that is agnostic of the modality. Our experiments suggest that our scene representation can help transfer representations across modalities for retrieval. Moreover, our visualizations suggest that units emerge in the shared representation that tend to activate on consistent concepts independently of the modality.

关键词： computer vision

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Recognizing micro-actions and reactions from paired egocentric videos

Recognizing micro-actions and reactions from paired egocentr...

引用

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

作者： Yonetani, Ryo Kitani, Kris M. Sato, Yoichi University of Tokyo Tokyo Japan Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9781467388511

We aim to understand the dynamics of social interactions between two people by recognizing their actions and reactions using a head-mounted camera. Our work will impact several first-person vision tasks that need the detailed understanding of social interactions, such as automatic video summarization of group events and assistive systems. To recognize micro-level actions and reactions, such as slight shifts in attention, subtle nodding, or small hand actions, where only subtle body motion is apparent, we propose to use paired egocentric videos recorded by two interacting people. We show that the first-person and second-person points-of-view features of two people, enabled by paired egocentric videos, are complementary and essential for reliably recognizing micro-actions and reactions. We also build a new dataset of dyadic (two-persons) interactions that comprises more than 1000 pairs of egocentric videos to enable systematic evaluations on the task of micro-action and reaction recognition.

关键词： computer vision

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Kernel approximation via empirical orthogonal decomposition for unsupervised feature learning

Kernel approximation via empirical orthogonal decomposition ...

引用

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

作者： Mukuta, Yusuke Harada, Tatsuya University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan

ISBN: (纸本)9781467388511

Kernel approximation methods are important tools for various machine learning problems. There are two major methods used to approximate the kernel function: the Nyström method and the random features method. However, the Nyström method requires relatively high-complexity post-processing to calculate a solution and the random features method does not provide sufficient generalization performance. In this paper, we propose a method that has good generalization performance without high-complexity postprocessing via empirical orthogonal decomposition using the probability distribution estimated from training data. We provide a bound for the approximation error of the proposed method. Our experiments show that the proposed method is better than the random features method and comparable with the Nyström method in terms of the approximation error and classification accuracy. We also show that hierarchical feature extraction using our kernel approximation demonstrates better performance than the existing methods.

关键词： computer vision

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