Paper Group ANR 57
Multiple VLAD encoding of CNNs for image classification. Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction. Video retrieval based on deep convolutional neural network. Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel Density Estimation in the Product Space. Cooperative Epistemi …
Multiple VLAD encoding of CNNs for image classification
| Title | Multiple VLAD encoding of CNNs for image classification |
| Authors | Qing Li, Qiang Peng, Chuan Yan |
| Abstract | Despite the effectiveness of convolutional neural networks (CNNs) especially in image classification tasks, the effect of convolution features on learned representations is still limited. It mostly focuses on the salient object of the images, but ignores the variation information on clutter and local. In this paper, we propose a special framework, which is the multiple VLAD encoding method with the CNNs features for image classification. Furthermore, in order to improve the performance of the VLAD coding method, we explore the multiplicity of VLAD encoding with the extension of three kinds of encoding algorithms, which are the VLAD-SA method, the VLAD-LSA and the VLAD-LLC method. Finally, we equip the spatial pyramid patch (SPM) on VLAD encoding to add the spatial information of CNNs feature. In particular, the power of SPM leads our framework to yield better performance compared to the existing method. |
| Tasks | Image Classification |
| Published | 2017-06-30 |
| URL | http://arxiv.org/abs/1707.00058v1 |
| http://arxiv.org/pdf/1707.00058v1.pdf | |
| PWC | https://paperswithcode.com/paper/multiple-vlad-encoding-of-cnns-for-image |
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Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction
| Title | Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction |
| Authors | Mathilde Ménoret, Nicolas Farrugia, Bastien Pasdeloup, Vincent Gripon |
| Abstract | Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals. In the present work, we apply dimensionality reduction techniques based on graph representations of the brain to decode brain activity from real and simulated fMRI datasets. We introduce seven graphs obtained from a) geometric structure and/or b) functional connectivity between brain areas at rest, and compare them when performing dimension reduction for classification. We show that mixed graphs using both a) and b) offer the best performance. We also show that graph sampling methods perform better than classical dimension reduction including Principal Component Analysis (PCA) and Independent Component Analysis (ICA). |
| Tasks | Dimensionality Reduction |
| Published | 2017-03-06 |
| URL | http://arxiv.org/abs/1703.01842v3 |
| http://arxiv.org/pdf/1703.01842v3.pdf | |
| PWC | https://paperswithcode.com/paper/evaluating-graph-signal-processing-for |
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Video retrieval based on deep convolutional neural network
| Title | Video retrieval based on deep convolutional neural network |
| Authors | Yj Dong, JG Li |
| Abstract | Recently, with the enormous growth of online videos, fast video retrieval research has received increasing attention. As an extension of image hashing techniques, traditional video hashing methods mainly depend on hand-crafted features and transform the real-valued features into binary hash codes. As videos provide far more diverse and complex visual information than images, extracting features from videos is much more challenging than that from images. Therefore, high-level semantic features to represent videos are needed rather than low-level hand-crafted methods. In this paper, a deep convolutional neural network is proposed to extract high-level semantic features and a binary hash function is then integrated into this framework to achieve an end-to-end optimization. Particularly, our approach also combines triplet loss function which preserves the relative similarity and difference of videos and classification loss function as the optimization objective. Experiments have been performed on two public datasets and the results demonstrate the superiority of our proposed method compared with other state-of-the-art video retrieval methods. |
| Tasks | Video Retrieval |
| Published | 2017-12-01 |
| URL | http://arxiv.org/abs/1712.00133v1 |
| http://arxiv.org/pdf/1712.00133v1.pdf | |
| PWC | https://paperswithcode.com/paper/video-retrieval-based-on-deep-convolutional |
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Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel Density Estimation in the Product Space
| Title | Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel Density Estimation in the Product Space |
| Authors | Matthias Vestner, Roee Litman, Emanuele Rodolà, Alex Bronstein, Daniel Cremers |
| Abstract | Many algorithms for the computation of correspondences between deformable shapes rely on some variant of nearest neighbor matching in a descriptor space. Such are, for example, various point-wise correspondence recovery algorithms used as a post-processing stage in the functional correspondence framework. Such frequently used techniques implicitly make restrictive assumptions (e.g., near-isometry) on the considered shapes and in practice suffer from lack of accuracy and result in poor surjectivity. We propose an alternative recovery technique capable of guaranteeing a bijective correspondence and producing significantly higher accuracy and smoothness. Unlike other methods our approach does not depend on the assumption that the analyzed shapes are isometric. We derive the proposed method from the statistical framework of kernel density estimation and demonstrate its performance on several challenging deformable 3D shape matching datasets. |
| Tasks | Density Estimation |
| Published | 2017-01-03 |
| URL | http://arxiv.org/abs/1701.00669v2 |
| http://arxiv.org/pdf/1701.00669v2.pdf | |
| PWC | https://paperswithcode.com/paper/product-manifold-filter-non-rigid-shape |
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Cooperative Epistemic Multi-Agent Planning for Implicit Coordination
| Title | Cooperative Epistemic Multi-Agent Planning for Implicit Coordination |
| Authors | Thorsten Engesser, Thomas Bolander, Robert Mattmüller, Bernhard Nebel |
| Abstract | Epistemic planning can be used for decision making in multi-agent situations with distributed knowledge and capabilities. Recently, Dynamic Epistemic Logic (DEL) has been shown to provide a very natural and expressive framework for epistemic planning. We extend the DEL-based epistemic planning framework to include perspective shifts, allowing us to define new notions of sequential and conditional planning with implicit coordination. With these, it is possible to solve planning tasks with joint goals in a decentralized manner without the agents having to negotiate about and commit to a joint policy at plan time. First we define the central planning notions and sketch the implementation of a planning system built on those notions. Afterwards we provide some case studies in order to evaluate the planner empirically and to show that the concept is useful for multi-agent systems in practice. |
| Tasks | Decision Making |
| Published | 2017-03-07 |
| URL | http://arxiv.org/abs/1703.02196v1 |
| http://arxiv.org/pdf/1703.02196v1.pdf | |
| PWC | https://paperswithcode.com/paper/cooperative-epistemic-multi-agent-planning |
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Arabic Character Segmentation Using Projection Based Approach with Profile’s Amplitude Filter
| Title | Arabic Character Segmentation Using Projection Based Approach with Profile’s Amplitude Filter |
| Authors | Mahmoud A. A. Mousa, Mohammed S. Sayed, Mahmoud I. Abdalla |
| Abstract | Arabic is one of the languages that present special challenges to Optical character recognition (OCR). The main challenge in Arabic is that it is mostly cursive. Therefore, a segmentation process must be carried out to determine where the character begins and where it ends. This step is essential for character recognition. This paper presents Arabic character segmentation algorithm. The proposed algorithm uses the projection-based approach concepts to separate lines, words, and characters. This is done using profile’s amplitude filter and simple edge tool to find characters separations. Our algorithm shows promising performance when applied on different machine printed documents with different Arabic fonts. |
| Tasks | Optical Character Recognition |
| Published | 2017-07-04 |
| URL | http://arxiv.org/abs/1707.00800v1 |
| http://arxiv.org/pdf/1707.00800v1.pdf | |
| PWC | https://paperswithcode.com/paper/arabic-character-segmentation-using |
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Fusing Multifaceted Transaction Data for User Modeling and Demographic Prediction
| Title | Fusing Multifaceted Transaction Data for User Modeling and Demographic Prediction |
| Authors | Yehezkel S. Resheff, Moni Shahar |
| Abstract | Inferring user characteristics such as demographic attributes is of the utmost importance in many user-centric applications. Demographic data is an enabler of personalization, identity security, and other applications. Despite that, this data is sensitive and often hard to obtain. Previous work has shown that purchase history can be used for multi-task prediction of many demographic fields such as gender and marital status. Here we present an embedding based method to integrate multifaceted sequences of transaction data, together with auxiliary relational tables, for better user modeling and demographic prediction. |
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| Published | 2017-12-19 |
| URL | http://arxiv.org/abs/1712.07230v1 |
| http://arxiv.org/pdf/1712.07230v1.pdf | |
| PWC | https://paperswithcode.com/paper/fusing-multifaceted-transaction-data-for-user |
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Identifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain Adaptation
| Title | Identifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain Adaptation |
| Authors | Greg Durrett, Jonathan K. Kummerfeld, Taylor Berg-Kirkpatrick, Rebecca S. Portnoff, Sadia Afroz, Damon McCoy, Kirill Levchenko, Vern Paxson |
| Abstract | One weakness of machine-learned NLP models is that they typically perform poorly on out-of-domain data. In this work, we study the task of identifying products being bought and sold in online cybercrime forums, which exhibits particularly challenging cross-domain effects. We formulate a task that represents a hybrid of slot-filling information extraction and named entity recognition and annotate data from four different forums. Each of these forums constitutes its own “fine-grained domain” in that the forums cover different market sectors with different properties, even though all forums are in the broad domain of cybercrime. We characterize these domain differences in the context of a learning-based system: supervised models see decreased accuracy when applied to new forums, and standard techniques for semi-supervised learning and domain adaptation have limited effectiveness on this data, which suggests the need to improve these techniques. We release a dataset of 1,938 annotated posts from across the four forums. |
| Tasks | Domain Adaptation, Named Entity Recognition, Slot Filling |
| Published | 2017-08-31 |
| URL | http://arxiv.org/abs/1708.09609v1 |
| http://arxiv.org/pdf/1708.09609v1.pdf | |
| PWC | https://paperswithcode.com/paper/identifying-products-in-online-cybercrime |
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Improved Inception-Residual Convolutional Neural Network for Object Recognition
| Title | Improved Inception-Residual Convolutional Neural Network for Object Recognition |
| Authors | Md Zahangir Alom, Mahmudul Hasan, Chris Yakopcic, Tarek M. Taha, Vijayan K. Asari |
| Abstract | Machine learning and computer vision have driven many of the greatest advances in the modeling of Deep Convolutional Neural Networks (DCNNs). Nowadays, most of the research has been focused on improving recognition accuracy with better DCNN models and learning approaches. The recurrent convolutional approach is not applied very much, other than in a few DCNN architectures. On the other hand, Inception-v4 and Residual networks have promptly become popular among computer the vision community. In this paper, we introduce a new DCNN model called the Inception Recurrent Residual Convolutional Neural Network (IRRCNN), which utilizes the power of the Recurrent Convolutional Neural Network (RCNN), the Inception network, and the Residual network. This approach improves the recognition accuracy of the Inception-residual network with same number of network parameters. In addition, this proposed architecture generalizes the Inception network, the RCNN, and the Residual network with significantly improved training accuracy. We have empirically evaluated the performance of the IRRCNN model on different benchmarks including CIFAR-10, CIFAR-100, TinyImageNet-200, and CU3D-100. The experimental results show higher recognition accuracy against most of the popular DCNN models including the RCNN. We have also investigated the performance of the IRRCNN approach against the Equivalent Inception Network (EIN) and the Equivalent Inception Residual Network (EIRN) counterpart on the CIFAR-100 dataset. We report around 4.53%, 4.49% and 3.56% improvement in classification accuracy compared with the RCNN, EIN, and EIRN on the CIFAR-100 dataset respectively. Furthermore, the experiment has been conducted on the TinyImageNet-200 and CU3D-100 datasets where the IRRCNN provides better testing accuracy compared to the Inception Recurrent CNN (IRCNN), the EIN, and the EIRN. |
| Tasks | Object Recognition |
| Published | 2017-12-28 |
| URL | http://arxiv.org/abs/1712.09888v1 |
| http://arxiv.org/pdf/1712.09888v1.pdf | |
| PWC | https://paperswithcode.com/paper/improved-inception-residual-convolutional |
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Distance-based Camera Network Topology Inference for Person Re-identification
| Title | Distance-based Camera Network Topology Inference for Person Re-identification |
| Authors | Yeong-Jun Cho, Kuk-Jin Yoon |
| Abstract | In this paper, we propose a novel distance-based camera network topology inference method for efficient person re-identification. To this end, we first calibrate each camera and estimate relative scales between cameras. Using the calibration results of multiple cameras, we calculate the speed of each person and infer the distance between cameras to generate distance-based camera network topology. The proposed distance-based topology can be applied adaptively to each person according to its speed and handle diverse transition time of people between non-overlapping cameras. To validate the proposed method, we tested the proposed method using an open person re-identification dataset and compared to state-of-the-art methods. The experimental results show that the proposed method is effective for person re-identification in the large-scale camera network with various people transition time. |
| Tasks | Calibration, Person Re-Identification |
| Published | 2017-12-01 |
| URL | http://arxiv.org/abs/1712.00158v1 |
| http://arxiv.org/pdf/1712.00158v1.pdf | |
| PWC | https://paperswithcode.com/paper/distance-based-camera-network-topology |
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Community Detection in Partially Observable Social Networks
| Title | Community Detection in Partially Observable Social Networks |
| Authors | Cong Tran, Won-Yong Shin, Andreas Spitz |
| Abstract | The discovery of community structures in social networks has gained significant attention since it is a fundamental problem in understanding the networks’ topology and functions. However, most social network data are collected from partially observable networks with both missing nodes and edges. In this paper, we address a new problem of detecting overlapping community structures in the context of such an incomplete network, where communities in the network are allowed to overlap since nodes belong to multiple communities at once. To solve this problem, we introduce KroMFac, a new framework that conducts community detection via regularized nonnegative matrix factorization (NMF) based on the Kronecker graph model. Specifically, from an interred Kronecker generative parameter metrix, we first estimate the missing part of the network. As our major contribution to the proposed framework, to improve community detection accuracy, we then characterize and select influential nodes (which tend to have high degrees) by ranking, and add them to the existing graph. Finally, we uncover the community structures by solving the regularized NMF-aided optimization problem in terms of maximizing the likelihood of the underlying graph. Furthermore, adopting normalized mutual information (NMI), we empirically show superiority of our KroMFac approach over two baseline schemes by using both synthetic and real-world networks. |
| Tasks | Community Detection |
| Published | 2017-12-30 |
| URL | https://arxiv.org/abs/1801.00132v6 |
| https://arxiv.org/pdf/1801.00132v6.pdf | |
| PWC | https://paperswithcode.com/paper/community-detection-in-partially-observable |
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Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network
| Title | Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network |
| Authors | Zhengchu Guo, Lei Shi, Qiang Wu |
| Abstract | Distributed learning is an effective way to analyze big data. In distributed regression, a typical approach is to divide the big data into multiple blocks, apply a base regression algorithm on each of them, and then simply average the output functions learnt from these blocks. Since the average process will decrease the variance, not the bias, bias correction is expected to improve the learning performance if the base regression algorithm is a biased one. Regularization kernel network is an effective and widely used method for nonlinear regression analysis. In this paper we will investigate a bias corrected version of regularization kernel network. We derive the error bounds when it is applied to a single data set and when it is applied as a base algorithm in distributed regression. We show that, under certain appropriate conditions, the optimal learning rates can be reached in both situations. |
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| Published | 2017-08-07 |
| URL | http://arxiv.org/abs/1708.01960v1 |
| http://arxiv.org/pdf/1708.01960v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-theory-of-distributed-regression |
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Joint Screening Tests for LASSO
| Title | Joint Screening Tests for LASSO |
| Authors | C. Herzet, A. Drémeau |
| Abstract | This paper focusses on “safe” screening techniques for the LASSO problem. Motivated by the need for low-complexity algorithms, we propose a new approach, dubbed “joint” screening test, allowing to screen a set of atoms by carrying out one single test. The approach is particularized to two different sets of atoms, respectively expressed as sphere and dome regions. After presenting the mathematical derivations of the tests, we elaborate on their relative effectiveness and discuss the practical use of such procedures. |
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| Published | 2017-10-26 |
| URL | http://arxiv.org/abs/1710.09809v2 |
| http://arxiv.org/pdf/1710.09809v2.pdf | |
| PWC | https://paperswithcode.com/paper/joint-screening-tests-for-lasso |
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Effects of Stop Words Elimination for Arabic Information Retrieval: A Comparative Study
| Title | Effects of Stop Words Elimination for Arabic Information Retrieval: A Comparative Study |
| Authors | Ibrahim Abu El-Khair |
| Abstract | The effectiveness of three stop words lists for Arabic Information Retrieval—General Stoplist, Corpus-Based Stoplist, Combined Stoplist —were investigated in this study. Three popular weighting schemes were examined: the inverse document frequency weight, probabilistic weighting, and statistical language modelling. The Idea is to combine the statistical approaches with linguistic approaches to reach an optimal performance, and compare their effect on retrieval. The LDC (Linguistic Data Consortium) Arabic Newswire data set was used with the Lemur Toolkit. The Best Match weighting scheme used in the Okapi retrieval system had the best overall performance of the three weighting algorithms used in the study, stoplists improved retrieval effectiveness especially when used with the BM25 weight. The overall performance of a general stoplist was better than the other two lists. |
| Tasks | Information Retrieval, Language Modelling |
| Published | 2017-02-07 |
| URL | http://arxiv.org/abs/1702.01925v1 |
| http://arxiv.org/pdf/1702.01925v1.pdf | |
| PWC | https://paperswithcode.com/paper/effects-of-stop-words-elimination-for-arabic |
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Articulation rate in Swedish child-directed speech increases as a function of the age of the child even when surprisal is controlled for
| Title | Articulation rate in Swedish child-directed speech increases as a function of the age of the child even when surprisal is controlled for |
| Authors | Johan Sjons, Thomas Hörberg, Robert Östling, Johannes Bjerva |
| Abstract | In earlier work, we have shown that articulation rate in Swedish child-directed speech (CDS) increases as a function of the age of the child, even when utterance length and differences in articulation rate between subjects are controlled for. In this paper we show on utterance level in spontaneous Swedish speech that i) for the youngest children, articulation rate in CDS is lower than in adult-directed speech (ADS), ii) there is a significant negative correlation between articulation rate and surprisal (the negative log probability) in ADS, and iii) the increase in articulation rate in Swedish CDS as a function of the age of the child holds, even when surprisal along with utterance length and differences in articulation rate between speakers are controlled for. These results indicate that adults adjust their articulation rate to make it fit the linguistic capacity of the child. |
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| Published | 2017-06-10 |
| URL | http://arxiv.org/abs/1706.03216v2 |
| http://arxiv.org/pdf/1706.03216v2.pdf | |
| PWC | https://paperswithcode.com/paper/articulation-rate-in-swedish-child-directed |
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