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Deep canonical time warping

Deep canonical time warping

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

作者： Trigeorgis, George Nicolaou, Mihalis A. Zafeiriou, Stefanos Schuller, Björn W. Imperial College London United Kingdom Goldsmiths University of London United Kingdom Center for Machine Vision and Signal Analysis University of Oulu Finland

ISBN: (纸本)9781467388511

Machine learning algorithms for the analysis of timeseries often depend on the assumption that the utilised data are temporally aligned. Any temporal discrepancies arising in the data is certain to lead to ill-generalisable models, which in turn fail to correctly capture the properties of the task at hand. The temporal alignment of time-series is thus a crucial challenge manifesting in a multitude of applications. Nevertheless, the vast majority of algorithms oriented towards the temporal alignment of time-series are applied directly on the observation space, or utilise simple linear projections. Thus, they fail to capture complex, hierarchical non-linear representations which may prove to be beneficial towards the task of temporal alignment, particularly when dealing with multi-modal data (e.g., aligning visual and acoustic information). To this end, we present the Deep Canonical Time Warping (DCTW), a method which automatically learns complex non-linear representations of multiple time-series, generated such that (i) they are highly correlated, and (ii) temporally in alignment. By means of experiments on four real datasets, we show that the representations learnt via the proposed DCTW significantly outperform state-of-the-art methods in temporal alignment, elegantly handling scenarios with highly heterogeneous features, such as the temporal alignment of acoustic and visual features.

关键词： Alignment

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Estimating correspondences of deformable objects "in-the-wild"

Estimating correspondences of deformable objects "in-the-wil...

引用

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

作者： Zhou, Yuxiang Antonakos, Epameinondas Alabort-I-medina, Joan Roussos, Anastasios Zafeiriou, Stefanos Department of Computing Imperial College London United Kingdom Center for Machine Vision and Signal Analysis University of Oulu Finland

ISBN: (纸本)9781467388511

During the past few years we have witnessed the development of many methodologies for building and fitting Statistical Deformable Models (SDMs). The construction of accurate SDMs requires careful annotation of images with regards to a consistent set of landmarks. However, the manual annotation of a large amount of images is a tedious, laborious and expensive procedure. Furthermore, for several deformable objects, e.g. human body, it is difficult to define a consistent set of landmarks, and, thus, it becomes impossible to train humans in order to accurately annotate a collection of images. Nevertheless, for the majority of objects, it is possible to extract the shape by object segmentation or even by shape drawing. In this paper, we show for the first time, to the best of our knowledge, that it is possible to construct SDMs by putting object shapes in dense correspondence. Such SDMs can be built with much less effort for a large battery of objects. Additionally, we show that, by sampling the dense model, a part-based SDM can be learned with its parts being in correspondence. We employ our framework to develop SDMs of human arms and legs, which can be used for the segmentation of the outline of the human body, as well as to provide better and more consistent annotations for body joints.

关键词： Deformation

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Face2Face: Real-time face capture and reenactment of RGB videos

Face2Face: Real-time face capture and reenactment of RGB vid...

引用

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

作者： Thies, Justus Zollhöfer, Michael Stamminger, Marc Theobalt, Christian Nießner, Matthias University of Erlangen-Nuremberg Germany Max-Planck-Institute for Informatics Germany Stanford University United States

ISBN: (纸本)9781467388511

We present a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). The source sequence is also a monocular video stream, captured live with a commodity webcam. Our goal is to animate the facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. To this end, we first address the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling. At run time, we track facial expressions of both source and target video using a dense photometric consistency measure. Reenactment is then achieved by fast and efficient deformation transfer between source and target. The mouth interior that best matches the re-targeted expression is retrieved from the target sequence and warped to produce an accurate fit. Finally, we convincingly re-render the synthesized target face on top of the corresponding video stream such that it seamlessly blends with the real-world illumination. We demonstrate our method in a live setup, where Youtube videos are reenacted in real time.

关键词： Rendering (computer graphics)

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6D dynamic camera relocalization from single reference image

6D dynamic camera relocalization from single reference image

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

作者： Feng, Wei Tian, Fei-Peng Zhang, Qian Sun, Jizhou School of Computer Science and Technology Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Tianjin China

ISBN: (纸本)9781467388511

Dynamic relocalization of 6D camera pose from single reference image is a costly and challenging task that requires delicate hand-eye calibration and precision positioning platform to do 3D mechanical rotation and translation. In this paper, we show that high-quality camera relocalization can be achieved in a much less expensive way. Based on inexpensive platform with unreliable absolute repositioning accuracy (ARA), we propose a hand-eye calibration free strategy to actively relocate camera into the same 6D pose that produces the input reference image, by sequentially correcting 3D relative rotation and translation. We theoretically prove that, by this strategy, both rotational and translational relative pose can be effectively reduced to zero, with bounded unknown hand-eye pose displacement. To conquer 3D rotation and translation ambiguity, this theoretical strategy is further revised to a practical relocalization algorithm with faster convergence rate and more reliability by jointly adjusting 3D relative rotation and translation. Extensive experiments validate the effectiveness and superior accuracy of the proposed approach on laboratory tests and challenging real-world applications.

关键词： Rotation

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Beyond local search: Tracking objects everywhere with instance-specific proposals

Beyond local search: Tracking objects everywhere with instan...

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

作者： Zhu, Gao Porikli, Fatih Li, Hongdong Australian National University Australia NICTA Australia ARC Centre of Excellence for Robotic Vision Australia

ISBN: (纸本)9781467388511

Most tracking-by-detection methods employ a local search window around the predicted object location in the current frame assuming the previous location is accurate, the trajectory is smooth, and the computational capacity permits a search radius that can accommodate the maximum speed yet small enough to reduce mismatches. These, however, may not be valid always, in particular for fast and irregularly moving objects. Here, we present an object tracker that is not limited to a local search window and has ability to probe efficiently the entire frame. Our method generates a small number of "high-quality" proposals by a novel instance-specific objectness measure and evaluates them against the object model that can be adopted from an existing tracking-by-detection approach as a core tracker. During the tracking process, we update the object model concentrating on hard false-positives supplied by the proposals, which help suppressing distractors caused by difficult background clutters, and learn how to re-rank proposals according to the object model. Since we reduce significantly the number of hypotheses the core tracker evaluates, we can use richer object descriptors and stronger detector. Our method outperforms most recent state-of-the-art trackers on popular tracking benchmarks, and provides improved robustness for fast moving objects as well as for ultra lowframe-rate videos.

关键词： Object detection

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Volumetric 3D tracking by detection

Volumetric 3D tracking by detection

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

作者： Huang, Chun-Hao Allain, Benjamin Franco, Jean-Sébastien Navab, Nassir Ilic, Slobodan Boyer, Edmond Technische Universität München Germany Inria LJK Univ. Grenoble Alpes France

ISBN: (纸本)9781467388511

In this paper, we propose a new framework for 3D tracking by detection based on fully volumetric representations. On one hand, 3D tracking by detection has shown robust use in the context of interaction (Kinect) and surface tracking. On the other hand, volumetric representations have recently been proven efficient both for building 3D features and for addressing the 3D tracking problem. We leverage these benefits by unifying both families of approaches into a single, fully volumetric tracking-by-detection framework. We use a centroidal Voronoi tessellation (CVT) representation to compactly tessellate shapes with optimal discretization, construct a feature space, and perform the tracking according to the correspondences provided by trained random forests. Our results show improved tracking and training computational efficiency and improved memory performance. This in turn enables the use of larger training databases than state of the art approaches, which we leverage by proposing a cross-tracking subject training scheme to benefit from all subject sequences for all tracking situations, thus yielding better detection and less overfitting.

关键词： Palmprint recognition

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Hierarchically gated deep networks for semantic segmentation

Hierarchically gated deep networks for semantic segmentation

引用

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

作者： Qi, Guo-Jun Department of Computer Science University of Central Florida United States

ISBN: (纸本)9781467388511

Semantic segmentation aims to parse the scene structure of images by annotating the labels to each pixel so that images can be segmented into different regions. While image structures usually have various scales, it is difficult to use a single scale to model the spatial contexts for all individual pixels. Multi-scale Convolutional Neural Networks (CNNs) and their variants have made striking success for modeling the global scene structure for an image. However, they are limited in labeling fine-grained local structures like pixels and patches, since spatial contexts might be blindly mixed up without appropriately customizing their scales. To address this challenge, we develop a novel paradigm of multiscale deep network to model spatial contexts surrounding different pixels at various scales. It builds multiple layers of memory cells, learning feature representations for individual pixels at their customized scales by hierarchically absorbing relevant spatial contexts via memory gates between layers. Such Hierarchically Gated Deep Networks (HGDNs) can customize a suitable scale for each pixel, thereby delivering better performance on labeling scene structures of various scales. We conduct the experiments on two datasets, and show competitive results compared with the other multi-scale deep networks on the semantic segmentation task.

关键词： Pixels

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DCAN: Deep contour-aware networks for accurate gland segmentation

DCAN: Deep contour-aware networks for accurate gland segment...

引用

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

作者： Chen, Hao Qi, Xiaojuan Yu, Lequan Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781467388511

The morphology of glands has been used routinely by pathologists to assess the malignancy degree of adenocarcinomas. Accurate segmentation of glands from histology images is a crucial step to obtain reliable morphological statistics for quantitative diagnosis. In this paper, we proposed an efficient deep contour-aware network (DCAN) to solve this challenging problem under a unified multi-task learning framework. In the proposed network, multi-level contextual features from the hierarchical architecture are explored with auxiliary supervision for accurate gland segmentation. When incorporated with multi-task regularization during the training, the discriminative capability of intermediate features can be further improved. Moreover, our network can not only output accurate probability maps of glands, but also depict clear contours simultaneously for separating clustered objects, which further boosts the gland segmentation performance. This unified framework can be efficient when applied to large-scale histopathological data without resorting to additional steps to generate contours based on low-level cues for post-separating. Our method won the 2015 MICCAI Gland Segmentation Challenge out of 13 competitive teams, surpassing all the other methods by a significant margin.

关键词： Image segmentation

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FANNG: Fast approximate nearest neighbour graphs

FANNG: Fast approximate nearest neighbour graphs

引用

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

作者： Harwood, Ben Drummond, Tom Department of Electrical and Computer Systems Engineering Monash University VIC Australia

ISBN: (纸本)9781467388511

We present a new method for approximate nearest neighbour search on large datasets of high dimensional feature vectors, such as SIFT or GIST descriptors. Our approach constructs a directed graph that can be efficiently explored for nearest neighbour queries. Each vertex in this graph represents a feature vector from the dataset being searched. The directed edges are computed by exploiting the fact that, for these datasets, the intrinsic dimensionality of the local manifold-like structure formed by the elements of the dataset is significantly lower than the embedding space. We also provide an efficient search algorithm that uses this graph to rapidly find the nearest neighbour to a query with high probability. We show how the method can be adapted to give a strong guarantee of 100% recall where the query is within a threshold distance of its nearest neighbour. We demonstrate that our method is significantly more efficient than existing state of the art methods. In particular, our GPU implementation can deliver 90% recall for queries on a data set of 1 million SIFT descriptors at a rate of over 1.2 million queries per second on a Titan X. Finally we also demonstrate how our method scales to datasets of 5M and 20M entries.

关键词： Nearest neighbor search

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Multimodal spontaneous emotion corpus for human behavior analysis

Multimodal spontaneous emotion corpus for human behavior ana...

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

作者： Zhang, Zheng Girard, Jeffrey M. Wu, Yue Zhang, Xing Liu, Peng Ciftci, Umur Canavan, Shaun Reale, Michael Horowitz, Andrew Yang, Huiyuan Cohn, Jeffrey F. Ji, Qiang Yin, Lijun Binghamton University United States University of Pittsburgh United States Rensselaer Polytechnic Institute United States

ISBN: (纸本)9781467388511

Emotion is expressed in multiple modalities, yet most research has considered at most one or two. This stems in part from the lack of large, diverse, well-annotated, multimodal databases with which to develop and test algorithms. We present a well-annotated, multimodal, multidimensional spontaneous emotion corpus of 140 participants. Emotion inductions were highly varied. Data were acquired from a variety of sensors of the face that included high-resolution 3D dynamic imaging, high-resolution 2D video, and thermal (infrared) sensing, and contact physiological sensors that included electrical conductivity of the skin, respiration, blood pressure, and heart rate. Facial expression was annotated for both the occurrence and intensity of facial action units from 2D video by experts in the Facial Action Coding System (FACS). The corpus further includes derived features from 3D, 2D, and IR (infrared) sensors and baseline results for facial expression and action unit detection. The entire corpus will be made available to the research community.

关键词： computer keyboards

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