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Beyond f-formations: Determining social involvement in free standing conversing groups from static images

Beyond f-formations: Determining social involvement in free ...

引用

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

作者： Zhang, Lu Hung, Hayley Delft University of Technology Mekelweg 2 Delft Netherlands University of Twente Drienerlolaan 5 Enschede Netherlands

ISBN: (纸本)9781467388511

In this paper, we present the first attempt to analyse differing levels of social involvement in free standing conversing groups (or the so-called F-formations) from static images. In addition, we enrich state-of-the-art F-formation modelling by learning a frustum of attention that accounts for the spatial context. That is, F-formation configurations vary with respect to the arrangement of furniture and the non-uniform crowdedness in the space during mingling scenarios. The majority of prior works have considered the labelling of conversing group as an objective task, requiring only a single annotator. However, we show that by embracing the subjectivity of social involvement, we not only generate a richer model of the social interactions in a scene but also significantly improve F-formation detection. We carry out extensive experimental validation of our proposed approach by collecting a novel set of multi-annotator labels of involvement on the publicly available Idiap Poster Data; the only multi-annotator labelled database of free standing conversing groups that is currently available.

关键词： computer vision

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Discriminative multi-modal feature fusion for RGBD indoor scene recognition

Discriminative multi-modal feature fusion for RGBD indoor sc...

引用

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

作者： Zhu, Hongyuan Weibel, Jean-Baptiste Lu, Shijian I2R AStar Singapore Singapore Georgia Tech United States

ISBN: (纸本)9781467388511

RGBD scene recognition has attracted increasingly attention due to the rapid development of depth sensors and their wide application scenarios. While many research has been conducted, most work used hand-crafted features which are difficult to capture high-level semantic structures. Recently, the feature extracted from deep convolutional neural network has produced state-of-the-art results for various computer vision tasks, which inspire researchers to explore incorporating CNN learned features for RGBD scene understanding. On the other hand, most existing work combines rgb and depth features without adequately exploiting the consistency and complementary information between them. Inspired by some recent work on RGBD object recognition using multi-modal feature fusion, we introduce a novel discriminative multi-modal fusion framework for rgbd scene recognition for the first time which simultaneously considers the inter- and intra-modality correlation for all samples and meanwhile regularizing the learned features to be discriminative and compact. The results from the multimodal layer can be back-propagated to the lower CNN layers, hence the parameters of the CNN layers and multimodal layers are updated iteratively until convergence. Experiments on the recently proposed large scale SUN RGB-D datasets show that our method achieved the state-of-the-art without any image segmentation.

关键词： computer vision

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Structured prediction of unobserved voxels from a single depth image

Structured prediction of unobserved voxels from a single dep...

引用

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

作者： Firman, Michael Aodha, Oisin Mac Julier, Simon Brostow, Gabriel J. University College London United Kingdom

ISBN: (纸本)9781467388511

Building a complete 3D model of a scene, given only a single depth image, is underconstrained. To gain a full volumetric model, one needs either multiple views, or a single view together with a library of unambiguous 3D models that will fit the shape of each individual object in the scene. We hypothesize that objects of dissimilar semantic classes often share similar 3D shape components, enabling a limited dataset to model the shape of a wide range of objects, and hence estimate their hidden geometry. Exploring this hypothesis, we propose an algorithm that can complete the unobserved geometry of tabletop-sized objects, based on a supervised model trained on already available volumetric elements. Our model maps from a local observation in a single depth image to an estimate of the surface shape in the surrounding neighborhood. We validate our approach both qualitatively and quantitatively on a range of indoor object collections and challenging real scenes.

关键词： computer vision

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MovieQA: Understanding stories in movies through question-answering

MovieQA: Understanding stories in movies through question-an...

引用

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

作者： Tapaswi, Makarand Zhu, Yukun Stiefelhagen, Rainer Torralba, Antonio Urtasun, Raquel Fidler, Sanja Karlsruhe Institute of Technology Germany Massachusetts Institute of Technology United States University of Toronto Canada

ISBN: (纸本)9781467388511

We introduce the MovieQA dataset which aims to evaluate automatic story comprehension from both video and text. The dataset consists of 14,944 questions about 408 movies with high semantic diversity. The questions range from simpler "Who" did "What" to "Whom", to "Why" and "How" certain events occurred. Each question comes with a set of five possible answers; a correct one and four deceiving answers provided by human annotators. Our dataset is unique in that it contains multiple sources of information - video clips, plots, subtitles, scripts, and DVS [32]. We analyze our data through various statistics and methods. We further extend existing QA techniques to show that question-answering with such open-ended semantics is hard. We make this data set public along with an evaluation benchmark to encourage inspiring work in this challenging domain.

关键词： computer vision

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Unsupervised learning of discriminative attributes and visual representations

Unsupervised learning of discriminative attributes and visua...

引用

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

作者： Huang, Chen Lo, Chen Change Tang, Xiaoou Department of Information Engineering Chinese University of Hong Kong Hong Kong SenseTime Group Limited China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781467388511

Attributes offer useful mid-level features to interpret visual data. While most attribute learning methods are supervised by costly human-generated labels, we introduce a simple yet powerful unsupervised approach to learn and predict visual attributes directly from data. Given a large unlabeled image collection as input, we train deep Convolutional Neural Networks (CNNs) to output a set of discriminative, binary attributes often with semantic meanings. Specifically, we first train a CNN coupled with unsupervised discriminative clustering, and then use the cluster membership as a soft supervision to discover shared attributes from the clusters while maximizing their separability. The learned attributes are shown to be capable of encoding rich imagery properties from both natural images and contour patches. The visual representations learned in this way are also transferrable to other tasks such as object detection. We show other convincing results on the related tasks of image retrieval and classification, and contour detection.

关键词： pattern recognition

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引用

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

作者： Chen, Dapeng Yuan, Zejian Chen, Badong Zheng, Nanning Xi'an Jiaotong University China

ISBN: (纸本)9781467388511

Pose variation remains one of the major factors that adversely affect the accuracy of person re-identification. Such variation is not arbitrary as body parts (e.g. head, torso, legs) have relative stable spatial distribution. Breaking down the variability of global appearance regarding the spatial distribution potentially benefits the person matching. We therefore learn a novel similarity function, which consists of multiple sub-similarity measurements with each taking in charge of a subregion. In particular, we take advantage of the recently proposed polynomial feature map to describe the matching within each subregion, and inject all the feature maps into a unified framework. The framework not only outputs similarity measurements for different regions, but also makes a better consistency among them. Our framework can collaborate local similarities as well as global similarity to exploit their complementary strength. It is flexible to incorporate multiple visual cues to further elevate the performance. In experiments, we analyze the effectiveness of the major components. The results on four datasets show significant and consistent improvements over the state-of-the-art methods.

关键词： pattern recognition

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Mixture of bilateral-projection two-dimensional probabilistic principal component analysis

Mixture of bilateral-projection two-dimensional probabilisti...

引用

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

作者： Ju, Fujiao Sun, Yanfeng Gao, Junbin Liu, Simeng Hu, Yongli Yin, Baocai College of Metropolitan Transportation Beijing University of Technology Beijing100124 China Discipline of Business Analytics University of Sydney Business School University of Sydney SydneyNSW2006 Australia Faculty of Electronic Information and Electrical Engineering College of Computer Science Dalian University of Technology Dalian116024 China

ISBN: (纸本)9781467388511

The probabilistic principal component analysis (PPCA) is built upon a global linear mapping, with which it is insufficient to model complex data variation. This paper proposes a mixture of bilateral-projection probabilistic principal component analysis model (mixB2DPPCA) on 2D data. With multi-components in the mixture, this model can be seen as a 'soft' cluster algorithm and has capability of modeling data with complex structures. A Bayesian inference scheme has been proposed based on the variational EM (Expectation-Maximization) approach for learning model parameters. Experiments on some publicly available databases show that the performance of mixB2DPPCA has been largely improved, resulting in more accurate reconstruction errors and recognition rates than the existing PCA-based algorithms.

关键词： Principal component analysis

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Unconstrained face alignment via cascaded compositional learning

Unconstrained face alignment via cascaded compositional lear...

引用

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

作者： Zhu, Shizhan Li, Cheng Loy, Chen Change Tang, Xiaoou Department of Information Engineering Chinese University of Hong Kong Hong Kong SenseTime Group Limited China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781467388511

We present a practical approach to address the problem of unconstrained face alignment for a single image. In our unconstrained problem, we need to deal with large shape and appearance variations under extreme head poses and rich shape deformation. To equip cascaded regressors with the capability to handle global shape variation and irregular appearance-shape relation in the unconstrained scenario, we partition the optimisation space into multiple domains of homogeneous descent, and predict a shape as a composition of estimations from multiple domain-specific regressors. With a specially formulated learning objective and a novel tree splitting function, our approach is capable of estimating a robust and meaningful composition. In addition to achieving state-of-the-art accuracy over existing approaches, our framework is also an efficient solution (350 FPS), thanks to the on-the-fly domain exclusion mechanism and the capability of leveraging the fast pixel feature.

关键词： pattern recognition

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Rethinking the inception architecture for computer vision

Rethinking the inception architecture for computer vision

引用

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

作者： Szegedy, Christian Vanhoucke, Vincent Ioffe, Sergey Shlens, Jon Wojna, Zbigniew Google Inc. United States University College London United Kingdom

ISBN: (纸本)9781467388511

Convolutional networks are at the core of most stateof-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21.2% top-1 and 5.6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3.5% top-5 error and 17.3% top-1 error on the validation set and 3.6% top-5 error on the official test set.

关键词： computer vision

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D3: Deep Dual-Domain based fast restoration of JPEG-compressed images

D3: Deep Dual-Domain based fast restoration of JPEG-compress...

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

作者： Wang, Zhangyang Liu, Ding Chang, Shiyu Ling, Qing Yang, Yingzhen Huang, Thomas S. Beckman Institute University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Automation University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9781467388511

In this paper, we design a Deep Dual-Domain (D3) based fast restoration model to remove artifacts of JPEG compressed images. It leverages the large learning capacity of deep networks, as well as the problem-specific expertise that was hardly incorporated in the past design of deep architectures. For the latter, we take into consideration both the prior knowledge of the JPEG compression scheme, and the successful practice of the sparsity-based dual-domain approach. We further design the One-Step Sparse Inference (1-SI) module, as an efficient and lightweighted feed-forward approximation of sparse coding. Extensive experiments verify the superiority of the proposed D3model over several state-of-the-art methods. Specifically, our best model is capable of outperforming the latest deep model for around 1 dB in PSNR, and is 30 times faster.

关键词： pattern recognition

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