Paper Group NANR 230
Fight Ill-Posedness With Ill-Posedness: Single-Shot Variational Depth Super-Resolution From Shading. Bilevel Distance Metric Learning for Robust Image Recognition. Differentially Private Uniformly Most Powerful Tests for Binomial Data. Large Scale Urban Scene Modeling from MVS Meshes. Deep Models for Arabic Dialect Identification on Benchmarked Dat …
Fight Ill-Posedness With Ill-Posedness: Single-Shot Variational Depth Super-Resolution From Shading
| Title | Fight Ill-Posedness With Ill-Posedness: Single-Shot Variational Depth Super-Resolution From Shading |
| Authors | Bjoern Haefner, Yvain Quéau, Thomas Möllenhoff, Daniel Cremers |
| Abstract | We put forward a principled variational approach for up-sampling a single depth map to the resolution of the companion color image provided by an RGB-D sensor. We combine heterogeneous depth and color data in order to jointly solve the ill-posed depth super-resolution and shape-from-shading problems. The low-frequency geometric information necessary to disambiguate shape-from-shading is extracted from the low-resolution depth measurements and, symmetrically, the high-resolution photometric clues in the RGB image provide the high-frequency information required to disambiguate depth super-resolution. |
| Tasks | Super-Resolution |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Haefner_Fight_Ill-Posedness_With_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Haefner_Fight_Ill-Posedness_With_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/fight-ill-posedness-with-ill-posedness-single |
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Bilevel Distance Metric Learning for Robust Image Recognition
| Title | Bilevel Distance Metric Learning for Robust Image Recognition |
| Authors | Jie Xu, Lei Luo, Cheng Deng, Heng Huang |
| Abstract | Metric learning, aiming to learn a discriminative Mahalanobis distance matrix M that can effectively reflect the similarity between data samples, has been widely studied in various image recognition problems. Most of the existing metric learning methods input the features extracted directly from the original data in the preprocess phase. What’s worse, these features usually take no consideration of the local geometrical structure of the data and the noise existed in the data, thus they may not be optimal for the subsequent metric learning task. In this paper, we integrate both feature extraction and metric learning into one joint optimization framework and propose a new bilevel distance metric learning model. Specifically, the lower level characterizes the intrinsic data structure using graph regularized sparse coefficients, while the upper level forces the data samples from the same class to be close to each other and pushes those from different classes far away. In addition, leveraging the KKT conditions and the alternating direction method (ADM), we derive an efficient algorithm to solve the proposed new model. Extensive experiments on various occluded datasets demonstrate the effectiveness and robustness of our method. |
| Tasks | Metric Learning |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7674-bilevel-distance-metric-learning-for-robust-image-recognition |
| http://papers.nips.cc/paper/7674-bilevel-distance-metric-learning-for-robust-image-recognition.pdf | |
| PWC | https://paperswithcode.com/paper/bilevel-distance-metric-learning-for-robust |
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Differentially Private Uniformly Most Powerful Tests for Binomial Data
| Title | Differentially Private Uniformly Most Powerful Tests for Binomial Data |
| Authors | Jordan Awan, Aleksandra Slavković |
| Abstract | We derive uniformly most powerful (UMP) tests for simple and one-sided hypotheses for a population proportion within the framework of Differential Privacy (DP), optimizing finite sample performance. We show that in general, DP hypothesis tests can be written in terms of linear constraints, and for exchangeable data can always be expressed as a function of the empirical distribution. Using this structure, we prove a ‘Neyman-Pearson lemma’ for binomial data under DP, where the DP-UMP only depends on the sample sum. Our tests can also be stated as a post-processing of a random variable, whose distribution we coin “Truncated-Uniform-Laplace” (Tulap), a generalization of the Staircase and discrete Laplace distributions. Furthermore, we obtain exact p-values, which are easily computed in terms of the Tulap random variable. We show that our results also apply to distribution-free hypothesis tests for continuous data. Our simulation results demonstrate that our tests have exact type I error, and are more powerful than current techniques. |
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| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7675-differentially-private-uniformly-most-powerful-tests-for-binomial-data |
| http://papers.nips.cc/paper/7675-differentially-private-uniformly-most-powerful-tests-for-binomial-data.pdf | |
| PWC | https://paperswithcode.com/paper/differentially-private-uniformly-most |
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Large Scale Urban Scene Modeling from MVS Meshes
| Title | Large Scale Urban Scene Modeling from MVS Meshes |
| Authors | Lingjie Zhu, Shuhan Shen, Xiang Gao, Zhanyi Hu |
| Abstract | In this paper we present an effcient modeling framework for large scale urban scenes. Taking surface meshes derived from multi- view-stereo systems as input, our algorithm outputs simplied models with semantics at different levels of detail (LODs). Our key observation is that urban building is usually composed of planar roof tops connected with vertical walls. There are two major steps in our framework: segmentation and building modeling. The scene is first segmented into four classes with a Markov random field combining height and image features. In the following modeling step, various 2D line segments sketching the roof boundaries are detected and slice the plane into faces. Through assigning each face with a roof plane, the final model is constructed by extruding the faces to the corresponding planes. By combining geometric and appearance cues together, the proposed method is robust and fast compared to the state-of-the-art algorithms. |
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| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Lingjie_Zhu_Large_Scale_Urban_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Lingjie_Zhu_Large_Scale_Urban_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/large-scale-urban-scene-modeling-from-mvs |
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Deep Models for Arabic Dialect Identification on Benchmarked Data
| Title | Deep Models for Arabic Dialect Identification on Benchmarked Data |
| Authors | Mohamed Elaraby, Muhammad Abdul-Mageed |
| Abstract | The Arabic Online Commentary (AOC) (Zaidan and Callison-Burch, 2011) is a large-scale repos-itory of Arabic dialects with manual labels for4varieties of the language. Existing dialect iden-tification models exploiting the dataset pre-date the recent boost deep learning brought to NLPand hence the data are not benchmarked for use with deep learning, nor is it clear how much neural networks can help tease the categories in the data apart. We treat these two limitations:We (1) benchmark the data, and (2) empirically test6different deep learning methods on thetask, comparing peformance to several classical machine learning models under different condi-tions (i.e., both binary and multi-way classification). Our experimental results show that variantsof (attention-based) bidirectional recurrent neural networks achieve best accuracy (acc) on thetask, significantly outperforming all competitive baselines. On blind test data, our models reach87.65{%}acc on the binary task (MSA vs. dialects),87.4{%}acc on the 3-way dialect task (Egyptianvs. Gulf vs. Levantine), and82.45{%}acc on the 4-way variants task (MSA vs. Egyptian vs. Gulfvs. Levantine). We release our benchmark for future work on the dataset |
| Tasks | Language Identification, Machine Translation |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-3930/ |
| https://www.aclweb.org/anthology/W18-3930 | |
| PWC | https://paperswithcode.com/paper/deep-models-for-arabic-dialect-identification |
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Learning Graph Convolution Filters from Data Manifold
| Title | Learning Graph Convolution Filters from Data Manifold |
| Authors | Guokun Lai, Hanxiao Liu, Yiming Yang |
| Abstract | Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features. Recently, there has been an increasing interest in extending CNNs to the general spatial domain. Although various types of graph convolution and geometric convolution methods have been proposed, their connections to traditional 2D-convolution are not well-understood. In this paper, we show that depthwise separable convolution is a path to unify the two kinds of convolution methods in one mathematical view, based on which we derive a novel Depthwise Separable Graph Convolution that subsumes existing graph convolution methods as special cases of our formulation. Experiments show that the proposed approach consistently outperforms other graph convolution and geometric convolution baselines on benchmark datasets in multiple domains. |
| Tasks | |
| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=H139Q_gAW |
| https://openreview.net/pdf?id=H139Q_gAW | |
| PWC | https://paperswithcode.com/paper/learning-graph-convolution-filters-from-data |
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Graininess-Aware Deep Feature Learning for Pedestrian Detection
| Title | Graininess-Aware Deep Feature Learning for Pedestrian Detection |
| Authors | Chunze Lin, Jiwen Lu, Gang Wang, Jie Zhou |
| Abstract | In this paper, we propose a graininess-aware deep feature learning method for pedestrian detection. Unlike most existing pedestrian detection methods which only consider low resolution feature maps, we incorporate fine-grained information into convolutional features to make them more discriminative for human body parts. Specifically, we propose a pedestrian attention mechanism which efficiently identifies pedestrian regions. Our method encodes fine-grained attention masks into convolutional feature maps, which significantly suppresses background interference and highlights pedestrians. Hence, our graininess-aware features become more focused on pedestrians, in particular those of small size and with occlusion. We further introduce a zoom-in-zoom-out module, which enhances the features by incorporating local details and context information. We integrate these two modules into a deep neural network, forming an end-to-end trainable pedestrian detector. Comprehensive experimental results on four challenging pedestrian benchmarks demonstrate the effectiveness of the proposed approach. |
| Tasks | Pedestrian Detection |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Chunze_Lin_Graininess-Aware_Deep_Feature_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Chunze_Lin_Graininess-Aware_Deep_Feature_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/graininess-aware-deep-feature-learning-for |
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Incorporating Context into Language Encoding Models for fMRI
| Title | Incorporating Context into Language Encoding Models for fMRI |
| Authors | Shailee Jain, Alexander Huth |
| Abstract | Language encoding models help explain language processing in the human brain by learning functions that predict brain responses from the language stimuli that elicited them. Current word embedding-based approaches treat each stimulus word independently and thus ignore the influence of context on language understanding. In this work we instead build encoding models using rich contextual representations derived from an LSTM language model. Our models show a significant improvement in encoding performance relative to state-of-the-art embeddings in nearly every brain area. By varying the amount of context used in the models and providing the models with distorted context, we show that this improvement is due to a combination of better word embeddings learned by the LSTM language model and contextual information. We are also able to use our models to map context sensitivity across the cortex. These results suggest that LSTM language models learn high-level representations that are related to representations in the human brain. |
| Tasks | Language Modelling, Word Embeddings |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7897-incorporating-context-into-language-encoding-models-for-fmri |
| http://papers.nips.cc/paper/7897-incorporating-context-into-language-encoding-models-for-fmri.pdf | |
| PWC | https://paperswithcode.com/paper/incorporating-context-into-language-encoding |
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Generating a Gold Standard for a Swedish Sentiment Lexicon
| Title | Generating a Gold Standard for a Swedish Sentiment Lexicon |
| Authors | Jacobo Rouces, Nina Tahmasebi, Lars Borin, Stian R{\o}dven Eide |
| Abstract | |
| Tasks | Lemmatization, Machine Translation, Morphological Analysis, Sentiment Analysis |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1426/ |
| https://www.aclweb.org/anthology/L18-1426 | |
| PWC | https://paperswithcode.com/paper/generating-a-gold-standard-for-a-swedish |
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Retrieval of the Best Counterargument without Prior Topic Knowledge
| Title | Retrieval of the Best Counterargument without Prior Topic Knowledge |
| Authors | Henning Wachsmuth, Shahbaz Syed, Benno Stein |
| Abstract | Given any argument on any controversial topic, how to counter it? This question implies the challenging retrieval task of finding the best counterargument. Since prior knowledge of a topic cannot be expected in general, we hypothesize the best counterargument to invoke the same aspects as the argument while having the opposite stance. To operationalize our hypothesis, we simultaneously model the similarity and dissimilarity of pairs of arguments, based on the words and embeddings of the arguments{'} premises and conclusions. A salient property of our model is its independence from the topic at hand, i.e., it applies to arbitrary arguments. We evaluate different model variations on millions of argument pairs derived from the web portal idebate.org. Systematic ranking experiments suggest that our hypothesis is true for many arguments: For 7.6 candidates with opposing stance on average, we rank the best counterargument highest with 60{%} accuracy. Even among all 2801 test set pairs as candidates, we still find the best one about every third time. |
| Tasks | |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-1023/ |
| https://www.aclweb.org/anthology/P18-1023 | |
| PWC | https://paperswithcode.com/paper/retrieval-of-the-best-counterargument-without |
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Analysis of Rhythmic Phrasing: Feature Engineering vs. Representation Learning for Classifying Readout Poetry
| Title | Analysis of Rhythmic Phrasing: Feature Engineering vs. Representation Learning for Classifying Readout Poetry |
| Authors | Timo Baumann, Hussein Hussein, Burkhard Meyer-Sickendiek |
| Abstract | We show how to classify the phrasing of readout poems with the help of machine learning algorithms that use manually engineered features or automatically learn representations. We investigate modern and postmodern poems from the webpage lyrikline, and focus on two exemplary rhythmical patterns in order to detect the rhythmic phrasing: The Parlando and the Variable Foot. These rhythmical patterns have been compared by using two important theoretical works: The Generative Theory of Tonal Music and the Rhythmic Phrasing in English Verse. Using both, we focus on a combination of four different features: The grouping structure, the metrical structure, the time-span-variation, and the prolongation in order to detect the rhythmic phrasing in the two rhythmical types. We use manually engineered features based on text-speech alignment and parsing for classification. We also train a neural network to learn its own representation based on text, speech and audio during pauses. The neural network outperforms manual feature engineering, reaching an f-measure of 0.85. |
| Tasks | Feature Engineering, Representation Learning |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4505/ |
| https://www.aclweb.org/anthology/W18-4505 | |
| PWC | https://paperswithcode.com/paper/analysis-of-rhythmic-phrasing-feature |
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Resource Creation Towards Automated Sentiment Analysis in Telugu (a low resource language) and Integrating Multiple Domain Sources to Enhance Sentiment Prediction
| Title | Resource Creation Towards Automated Sentiment Analysis in Telugu (a low resource language) and Integrating Multiple Domain Sources to Enhance Sentiment Prediction |
| Authors | Rama Rohit Reddy Gangula, Radhika Mamidi |
| Abstract | |
| Tasks | Sentiment Analysis |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1100/ |
| https://www.aclweb.org/anthology/L18-1100 | |
| PWC | https://paperswithcode.com/paper/resource-creation-towards-automated-sentiment |
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YNU-HPCC at SemEval-2018 Task 2: Multi-ensemble Bi-GRU Model with Attention Mechanism for Multilingual Emoji Prediction
| Title | YNU-HPCC at SemEval-2018 Task 2: Multi-ensemble Bi-GRU Model with Attention Mechanism for Multilingual Emoji Prediction |
| Authors | Nan Wang, Jin Wang, Xuejie Zhang |
| Abstract | This paper describes our approach to SemEval-2018 Task 2, which aims to predict the most likely associated emoji, given a tweet in English or Spanish. We normalized text-based tweets during pre-processing, following which we utilized a bi-directional gated recurrent unit with an attention mechanism to build our base model. Multi-models with or without class weights were trained for the ensemble methods. We boosted models without class weights, and only strong boost classifiers were identified. In our system, not only was a boosting method used, but we also took advantage of the voting ensemble method to enhance our final system result. Our method demonstrated an obvious improvement of approximately 3{%} of the macro F1 score in English and 2{%} in Spanish. |
| Tasks | Sentiment Analysis |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1073/ |
| https://www.aclweb.org/anthology/S18-1073 | |
| PWC | https://paperswithcode.com/paper/ynu-hpcc-at-semeval-2018-task-2-multi |
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A Tensor Analysis on Dense Connectivity via Convolutional Arithmetic Circuits
| Title | A Tensor Analysis on Dense Connectivity via Convolutional Arithmetic Circuits |
| Authors | Emilio Rafael Balda, Arash Behboodi, Rudolf Mathar |
| Abstract | Several state of the art convolutional networks rely on inter-connecting different layers to ease the flow of information and gradient between their input and output layers. These techniques have enabled practitioners to successfully train deep convolutional networks with hundreds of layers. Particularly, a novel way of interconnecting layers was introduced as the Dense Convolutional Network (DenseNet) and has achieved state of the art performance on relevant image recognition tasks. Despite their notable empirical success, their theoretical understanding is still limited. In this work, we address this problem by analyzing the effect of layer interconnection on the overall expressive power of a convolutional network. In particular, the connections used in DenseNet are compared with other types of inter-layer connectivity. We carry out a tensor analysis on the expressive power inter-connections on convolutional arithmetic circuits (ConvACs) and relate our results to standard convolutional networks. The analysis leads to performance bounds and practical guidelines for design of ConvACs. The generalization of these results are discussed for other kinds of convolutional networks via generalized tensor decompositions. |
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| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=Byj54-bAW |
| https://openreview.net/pdf?id=Byj54-bAW | |
| PWC | https://paperswithcode.com/paper/a-tensor-analysis-on-dense-connectivity-via |
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Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting With a Single Convolutional Net
| Title | Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting With a Single Convolutional Net |
| Authors | Wenjie Luo, Bin Yang, Raquel Urtasun |
| Abstract | In this paper we propose a novel deep neural network that is able to jointly reason about 3D detection, tracking and motion forecasting given data captured by a 3D sensor. By jointly reasoning about these tasks, our holistic approach is more robust to occlusion as well as sparse data at range. Our approach performs 3D convolutions across space and time over a bird’s eye view representation of the 3D world, which is very efficient in terms of both memory and computation. Our experiments on a new very large scale dataset captured in several north american cities, show that we can outperform the state-of-the-art by a large margin. Importantly, by sharing computation we can perform all tasks in as little as 30 ms. |
| Tasks | Motion Forecasting |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Luo_Fast_and_Furious_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Luo_Fast_and_Furious_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/fast-and-furious-real-time-end-to-end-3d |
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