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Joint probabilistic matching using m-best solutions

Joint probabilistic matching using m-best solutions

引用

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

作者： Rezatofighi, Seyed Hamid Milan, Anton Zhang, Zhen Shi, Qinfeng Dick, Anthony Reid, Ian School of Computer Science University of Adelaide Australia School of Computer Science and Technology Northwestern Polytechnical University Xian China

ISBN: (纸本)9781467388511

Matching between two sets of objects is typically approached by finding the object pairs that collectively maximize the joint matching score. In this paper, we argue that this single solution does not necessarily lead to the optimal matching accuracy and that general one-to-one assignment problems can be improved by considering multiple hypotheses before computing the final similarity measure. To that end, we propose to utilize the marginal distributions for each entity. Previously, this idea has been neglected mainly because exact marginalization is intractable due to a combinatorial number of all possible matching permutations. Here, we propose a generic approach to efficiently approximate the marginal distributions by exploiting the m-best solutions of the original problem. This approach not only improves the matching solution, but also provides more accurate ranking of the results, because of the extra information included in the marginal distribution. We validate our claim on two distinct objectives: (i) person re-identification and temporal matching modeled as an integer linear program, and (ii) feature point matching using a quadratic cost function. Our experiments confirm that marginalization indeed leads to superior performance compared to the single (nearly) optimal solution, yielding state-of-the-art results in both applications on standard benchmarks.

关键词： pattern recognition

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TI-POOLING: Transformation-invariant pooling for feature learning in convolutional neural networks

TI-POOLING: Transformation-invariant pooling for feature lea...

引用

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

作者： Laptev, Dmitry Savinov, Nikolay Buhmann, Joachim M. Pollefeys, Marc Department of Computer Science ETH Zurich Switzerland

ISBN: (纸本)9781467388511

In this paper we present a deep neural network topology that incorporates a simple to implement transformationinvariant pooling operator (TI-POOLING). This operator is able to efficiently handle prior knowledge on nuisance variations in the data, such as rotation or scale changes. Most current methods usually make use of dataset augmentation to address this issue, but this requires larger number of model parameters and more training data, and results in significantly increased training time and larger chance of under- or overfitting. The main reason for these drawbacks is that that the learned model needs to capture adequate features for all the possible transformations of the input. On the other hand, we formulate features in convolutional neural networks to be transformation-invariant. We achieve that using parallel siamese architectures for the considered transformation set and applying the TI-POOLING operator on their outputs before the fully-connected layers. We show that this topology internally finds the most optimal "canonical" instance of the input image for training and therefore limits the redundancy in learned features. This more efficient use of training data results in better performance on popular benchmark datasets with smaller number of parameters when comparing to standard convolutional neural networks with dataset augmentation and to other baselines.

关键词： Deep neural networks

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A key volume mining deep framework for action recognition

A key volume mining deep framework for action recognition

引用

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

作者： Zhu, Wangjiang Hu, Jie Sun, Gang Cao, Xudong Qiao, Yu Tsinghua University China SenseTime Group Limited China Shenzhen Institutes of Advanced Tecnlogy CAS China

ISBN: (纸本)9781467388511

Recently, deep learning approaches have demonstrated remarkable progresses for action recognition in videos. Most existing deep frameworks equally treat every volume i.e. spatial-temporal video clip, and directly assign a video label to all volumes sampled from it. However, within a video, discriminative actions may occur sparsely in a few key volumes, and most other volumes are irrelevant to the labeled action category. Training with a large proportion of irrelevant volumes will hurt performance. To address this issue, we propose a key volume mining deep framework to identify key volumes and conduct classification simultaneously. Specifically, our framework is trained is optimized in an alternative way integrated to the forward and backward stages of Stochastic Gradient Descent (SGD). In the forward pass, our network mines key volumes for each action class. In the backward pass, it updates network parameters with the help of these mined key volumes. In addition, we propose "Stochastic out" to model key volumes from multi-modalities, and an effective yet simple "unsupervised key volume proposal" method for high quality volume sampling. Our experiments show that action recognition performance can be significantly improved by mining key volumes, and we achieve state-of-the-art performance on HMDB51 and UCF101 (93.1%).

关键词： pattern recognition

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Contour detection in unstructured 3D point clouds

Contour detection in unstructured 3D point clouds

引用

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

作者： Hackel, Timo Wegner, Jan D. Schindler, Konrad Photogrammetry and Remote Sensing ETH Zürich Switzerland

ISBN: (纸本)9781467388511

We describe a method to automatically detect contours, i.e. lines along which the surface orientation sharply changes, in large-scale outdoor point clouds. Contours are important intermediate features for structuring point clouds and converting them into high-quality surface or solid models, and are extensively used in graphics and mapping applications. Yet, detecting them in unstructured, inhomogeneous point clouds turns out to be surprisingly difficult, and existing line detection algorithms largely fail. We approach contour extraction as a two-stage discriminative learning problem. In the first stage, a contour score for each individual point is predicted with a binary classifier, using a set of features extracted from the point's neighborhood. The contour scores serve as a basis to construct an overcomplete graph of candidate contours. The second stage selects an optimal set of contours from the candidates. This amounts to a further binary classification in a higher-order MRF, whose cliques encode a preference for connected contours and penalize loose ends. The method can handle point clouds > 107points in a couple of minutes, and vastly outperforms a baseline that performs Canny-style edge detection on a range image representation of the point cloud.

关键词： computer vision

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Efficient temporal sequence comparison and classification using Gram matrix embeddings on a Riemannian manifold

Efficient temporal sequence comparison and classification us...

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

作者： Zhang, Xikang Wang, Yin Gou, Mengran Sznaier, Mario Camps, Octavia Electrical and Computer Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9781467388511

In this paper we propose a new framework to compare and classify temporal sequences. The proposed approach captures the underlying dynamics of the data while avoiding expensive estimation procedures, making it suitable to process large numbers of sequences. The main idea is to first embed the sequences into a Riemannian manifold by using positive definite regularized Gram matrices of their Hankelets. The advantages of the this approach are: 1) it allows for using non-Euclidean similarity functions on the Positive Definite matrix manifold, which capture better the underlying geometry than directly comparing the sequences or their Hankel matrices; and 2) Gram matrices inherit desirable properties from the underlying Hankel matrices: their rank measure the complexity of the underlying dynamics, and the order and coefficients of the associated regressive models are invariant to affine transformations and varying initial conditions. The benefits of this approach are illustrated with extensive experiments in 3D action recognition using 3D joints sequences. In spite of its simplicity, the performance of this approach is competitive or better than using state-of-art approaches for this problem. Further, these results hold across a variety of metrics, supporting the idea that the improvement stems from the embedding itself, rather than from using one of these metrics.

关键词： Classification (of information)

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The SYNTHIA dataset: A large collection of synthetic images for semantic segmentation of urban scenes

The SYNTHIA dataset: A large collection of synthetic images ...

引用

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

作者： Ros, German Sellart, Laura Materzynska, Joanna Vazquez, David Lopez, Antonio M. Computer Vision Center Edifici O Campus UAB Barcelona Spain Computer Science Dept. Universitat Autonoma de Barcelona Campus UAB Barcelona Spain Faculty of Mathematics University of Vienna Oskar-Morgenstern-Platz Vienna Austria

ISBN: (纸本)9781467388511

vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images; thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this paper, we propose to use a virtual world to automatically generate realistic synthetic images with pixel-level annotations. Then, we address the question of how useful such data can be for semantic segmentation - in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show how the inclusion of SYNTHIA in the training stage significantly improves performance on the semantic segmentation task.

关键词： Image segmentation

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Mining 3D key-pose-motifs for action recognition

Mining 3D key-pose-motifs for action recognition

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

作者： Wang, Chunyu Wang, Yizhou Yuille, Alan L. Sch'l of EECS Peking University Beijing100871 China USA Department of Cognitive Science and Computer Science John Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781467388511

Recognizing an action from a sequence of 3D skeletal poses is a challenging task. First, different actors may perform the same action in various styles. Second, the estimated poses are sometimes inaccurate. These challenges can cause large variations between instances of the same class. Third, the datasets are usually small, with only a few actors performing few repetitions of each action. Hence training complex classifiers risks over-fitting the data. We address this task by mining a set of key-pose-motifs for each action class. A key-pose-motif contains a set of ordered poses, which are required to be close but not necessarily adjacent in the action sequences. The representation is robust to style variations. The key-pose-motifs are represented in terms of a dictionary using soft-quantization to deal with inaccuracies caused by quantization. We propose an efficient algorithm to mine key-pose-motifs taking into account of these probabilities. We classify a sequence by matching it to the motifs of each class and selecting the class that maximizes the matching score. This simple classifier obtains state-ofthe-art performance on two benchmark datasets.

关键词： Classification (of information)

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Recurrent face aging

Recurrent face aging

引用

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

作者： Wang, Wei Cui, Zhen Yan, Yan Feng, Jiashi Yan, Shuicheng Shu, Xiangbo Sebe, Nicu Department of Information Engineering and Computer Science University of Trento Italy Department of Electrical and Computer Engineering National University of Singapore Singapore Research Center for Learning Science Southeast University Nanjing China 360 Artificial Intelligence Institute China

ISBN: (纸本)9781467388511

Modeling the aging process of human face is important for cross-age face verification and recognition. In this paper, we introduce a recurrent face aging (RFA) framework based on a recurrent neural network which can identify the ages of people from 0 to 80. Due to the lack of labeled face data of the same person captured in a long range of ages, traditional face aging models usually split the ages into discrete groups and learn a one-step face feature transformation for each pair of adjacent age groups. However, those methods neglect the in-between evolving states between the adjacent age groups and the synthesized faces often suffer from severe ghosting artifacts. Since human face aging is a smooth progression, it is more appropriate to age the face by going through smooth transition states. In this way, the ghosting artifacts can be effectively eliminated and the intermediate aged faces between two discrete age groups can also be obtained. Towards this target, we employ a twolayer gated recurrent unit as the basic recurrent module whose bottom layer encodes a young face to a latent representation and the top layer decodes the representation to a corresponding older face. The experimental results demonstrate our proposed RFA provides better aging faces over other state-of-the-art age progression methods.

关键词： pattern recognition

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3D semantic parsing of large-scale indoor spaces

3D semantic parsing of large-scale indoor spaces

引用

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

作者： Armeni, Iro Sener, Ozan Zamir, Amir R. Jiang, Helen Brilakis, Ioannis Fischer, Martin Savarese, Silvio Stanford University United States Cornell University United States University of Cambridge United Kingdom

ISBN: (纸本)9781467388511

In this paper, we propose a method for semantic parsing the 3D point cloud of an entire building using a hierarchical approach: first, the raw data is parsed into semantically meaningful spaces (e.g. rooms, etc) that are aligned into a canonical reference coordinate system. Second, the spaces are parsed into their structural and building elements (e.g. walls, columns, etc). Performing these with a strong notation of global 3D space is the backbone of our method. The alignment in the first step injects strong 3D priors from the canonical coordinate system into the second step for discovering elements. This allows diverse challenging scenarios as man-made indoor spaces often show recurrent geometric patterns while the appearance features can change drastically. We also argue that identification of structural elements in indoor spaces is essentially a detection problem, rather than segmentation which is commonly used. We evaluated our method on a new dataset of several buildings with a covered area of over 6, 000m2and over 215 million points, demonstrating robust results readily useful for practical applications.

关键词： computer vision

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ReD-SFA: Relation discovery based slow feature analysis for trajectory clustering

ReD-SFA: Relation discovery based slow feature analysis for ...

引用

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

作者： Zhang, Zhang Huang, Kaiqi Tan, Tieniu Yang, Peipei Li, Jun CRIPAC NLPR Institute of Automation Chinese Academy of Sciences China Centre for Quantum Computation and Intelligent Systems University of Technology Sydney Australia

ISBN: (纸本)9781467388511

For spectral embedding/clustering, it is still an open problem on how to construct an relation graph to reflect the intrinsic structures in data. In this paper, we proposed an approach, named Relation Discovery based Slow Feature Analysis (ReD-SFA), for feature learning and graph construction simultaneously. Given an initial graph with only a few nearest but most reliable pairwise relations, new reliable relations are discovered by an assumption of reliability preservation, i.e., the reliable relations will preserve their reliabilities in the learnt projection subspace. We formulate the idea as a cross entropy (CE) minimization problem to reduce the discrepancy between two Bernoulli distributions parameterized by the updated distances and the existing relation graph respectively. Furthermore, to overcome the imbalanced distribution of samples, a Boosting-like strategy is proposed to balance the discovered relations over all clusters. To evaluate the proposed method, extensive experiments are performed with various trajectory clustering tasks, including motion segmentation, time series clustering and crowd detection. The results demonstrate that ReD-SFA can discover reliable intra-cluster relations with high precision, and competitive clustering performance can be achieved in comparison with state-of-the-art.

关键词： pattern recognition

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