Paper Group NANR 177
Natural Language Interface for Databases Using a Dual-Encoder Model. Depth and Transient Imaging With Compressive SPAD Array Cameras. Deblurring Natural Image Using Super-Gaussian Fields. Do Speakers Produce Discourse Connectives Rationally?. Multi-Input Attention for Unsupervised OCR Correction. Learning 3D Keypoint Descriptors for Non-Rigid Shape …
Natural Language Interface for Databases Using a Dual-Encoder Model
| Title | Natural Language Interface for Databases Using a Dual-Encoder Model |
| Authors | Ionel Alex Hosu, ru, Radu Cristian Alex Iacob, ru, Florin Brad, Stefan Ruseti, Traian Rebedea |
| Abstract | We propose a sketch-based two-step neural model for generating structured queries (SQL) based on a user{'}s request in natural language. The sketch is obtained by using placeholders for specific entities in the SQL query, such as column names, table names, aliases and variables, in a process similar to semantic parsing. The first step is to apply a sequence-to-sequence (SEQ2SEQ) model to determine the most probable SQL sketch based on the request in natural language. Then, a second network designed as a dual-encoder SEQ2SEQ model using both the text query and the previously obtained sketch is employed to generate the final SQL query. Our approach shows improvements over previous approaches on two recent large datasets (WikiSQL and SENLIDB) suitable for data-driven solutions for natural language interfaces for databases. |
| Tasks | Machine Translation, Semantic Parsing |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1043/ |
| https://www.aclweb.org/anthology/C18-1043 | |
| PWC | https://paperswithcode.com/paper/natural-language-interface-for-databases |
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Depth and Transient Imaging With Compressive SPAD Array Cameras
| Title | Depth and Transient Imaging With Compressive SPAD Array Cameras |
| Authors | Qilin Sun, Xiong Dun, Yifan Peng, Wolfgang Heidrich |
| Abstract | Time-of-flight depth imaging and transient imaging are two imaging modalities that have recently received a lot of interest. Despite much research, existing hardware systems are limited either in terms of temporal resolution or are prohibitively expensive. Arrays of Single Photon Avalanche Diodes (SPADs) promise to fill this gap by providing higher temporal resolution at an affordable cost. Unfortunately SPAD arrays are to date only available in relatively small resolutions. In this work we aim to overcome the spatial resolution limit of SPAD arrays by employing a compressive sensing camera design. Using a DMD and custom optics, we achieve an image resolution of up to 800400 on SPAD Arrays of resolution 6432. Using our new data fitting model for the time histograms, we suppress the noise while abstracting the phase and amplitude information, so as to realize a temporal resolution of a few tens of picoseconds. |
| Tasks | Compressive Sensing |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Sun_Depth_and_Transient_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Sun_Depth_and_Transient_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/depth-and-transient-imaging-with-compressive |
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Deblurring Natural Image Using Super-Gaussian Fields
| Title | Deblurring Natural Image Using Super-Gaussian Fields |
| Authors | Yuhang Liu, Wenyong Dong, Dong Gong, Lei Zhang, Qinfeng Shi |
| Abstract | Blind image deblurring is a challenging problem due to its ill-posed nature, of which the success is closely related to a proper image prior. Although a large number of sparsity-based priors, such as the sparse gradient prior, have been successfully applied for blind image deblurring, they inherently suffer from several drawbacks, limiting their applications. Existing sparsity-based priors are usually rooted in modeling the response of images to some specific filters (e.g., image gradients), which are insufficient to capture the complicated image structures. Moreover, the traditional sparse priors or regularizations model the filter response (e.g., image gradients) independently and thus fail to depict the long range correlation among them. To address the above issues, we present a novel image prior for image deblurring based on a Super-Gaussian field model with adaptive structures. Instead of modeling the response of the fixed short-term filters, the proposed Super-Gaussian fields capture the complicated structures in natural images by integrating potentials on all cliques (e.g., centring at each pixel) into a joint probabilistic distribution. Considering that the fixed filters in different scales are impractical for the coarse-to-fine framework of image deblurring, we define each potential function as a super-Gaussian distribution. Through this definition, the partition function, the curse for traditional MRFs, can be theoretically ignored, and all model parameters of the proposed Super-Gaussian fields can be data-adaptively learned and inferred from the blurred observation with a variational framework. Extensive experiments on both blind deblurring and non-blind deblurring demonstrate the effectiveness of the proposed method. |
| Tasks | Blind Image Deblurring, Deblurring |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Yuhang_Liu_Deblurring_Natural_Image_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Yuhang_Liu_Deblurring_Natural_Image_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/deblurring-natural-image-using-super-gaussian |
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Do Speakers Produce Discourse Connectives Rationally?
| Title | Do Speakers Produce Discourse Connectives Rationally? |
| Authors | Frances Yung, Vera Demberg |
| Abstract | A number of different discourse connectives can be used to mark the same discourse relation, but it is unclear what factors affect connective choice. One recent account is the Rational Speech Acts theory, which predicts that speakers try to maximize the informativeness of an utterance such that the listener can interpret the intended meaning correctly. Existing prior work uses referential language games to test the rational account of speakers{'} production of concrete meanings, such as identification of objects within a picture. Building on the same paradigm, we design a novel Discourse Continuation Game to investigate speakers{'} production of abstract discourse relations. Experimental results reveal that speakers significantly prefer a more informative connective, in line with predictions of the RSA model. |
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| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-2802/ |
| https://www.aclweb.org/anthology/W18-2802 | |
| PWC | https://paperswithcode.com/paper/do-speakers-produce-discourse-connectives |
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Multi-Input Attention for Unsupervised OCR Correction
| Title | Multi-Input Attention for Unsupervised OCR Correction |
| Authors | Rui Dong, David Smith |
| Abstract | We propose a novel approach to OCR post-correction that exploits repeated texts in large corpora both as a source of noisy target outputs for unsupervised training and as a source of evidence when decoding. A sequence-to-sequence model with attention is applied for single-input correction, and a new decoder with multi-input attention averaging is developed to search for consensus among multiple sequences. We design two ways of training the correction model without human annotation, either training to match noisily observed textual variants or bootstrapping from a uniform error model. On two corpora of historical newspapers and books, we show that these unsupervised techniques cut the character and word error rates nearly in half on single inputs and, with the addition of multi-input decoding, can rival supervised methods. |
| Tasks | Optical Character Recognition |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-1220/ |
| https://www.aclweb.org/anthology/P18-1220 | |
| PWC | https://paperswithcode.com/paper/multi-input-attention-for-unsupervised-ocr |
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Learning 3D Keypoint Descriptors for Non-Rigid Shape Matching
| Title | Learning 3D Keypoint Descriptors for Non-Rigid Shape Matching |
| Authors | Hanyu Wang, Jianwei Guo, Dong-Ming Yan, Weize Quan, Xiaopeng Zhang |
| Abstract | In this paper, we present a novel deep learning framework that derives discriminative local descriptors for 3D surface shapes. In contrast to previous convolutional neural networks (CNNs) that rely on rendering multi-view images or extracting intrinsic shape properties, we parameterize the multi-scale localized neighborhoods of a keypoint into regular 2D grids, which are termed as `geometry images’. The benefits of such geometry images include retaining sufficient geometric information, as well as allowing the usage of standard CNNs. Specifically, we leverage a triplet network to perform deep metric learning, which takes a set of triplets as input, and a newly designed triplet loss function is minimized to distinguish between similar and dissimilar pairs of keypoints. At the testing stage, given a geometry image of a point of interest, our network outputs a discriminative local descriptor for it. Experimental results for non-rigid shape matching on several benchmarks demonstrate the superior performance of our learned descriptors over traditional descriptors and the state-of-the-art learning-based alternatives. | |
| Tasks | Metric Learning |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Hanyu_Wang_Learning_3D_Keypoint_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Hanyu_Wang_Learning_3D_Keypoint_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/learning-3d-keypoint-descriptors-for-non |
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智慧手機客語拼音輸入法之研發-以臺灣海陸腔為例 (Research and Implementation of Hakka Pinyin Input Method for Mobile Cell - An Example of Taiwan HioLiuk Accent) [In Chinese]
| Title | 智慧手機客語拼音輸入法之研發-以臺灣海陸腔為例 (Research and Implementation of Hakka Pinyin Input Method for Mobile Cell - An Example of Taiwan HioLiuk Accent) [In Chinese] |
| Authors | Feng-Long Huang, Ming-Chan Liu |
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| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/O18-1015/ |
| https://www.aclweb.org/anthology/O18-1015 | |
| PWC | https://paperswithcode.com/paper/oaea14e3e14-a3a1c-c14-aeoce-ecoa34-research |
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A Corpus of eRulemaking User Comments for Measuring Evaluability of Arguments
| Title | A Corpus of eRulemaking User Comments for Measuring Evaluability of Arguments |
| Authors | Joonsuk Park, Claire Cardie |
| Abstract | |
| Tasks | Argument Mining |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1257/ |
| https://www.aclweb.org/anthology/L18-1257 | |
| PWC | https://paperswithcode.com/paper/a-corpus-of-erulemaking-user-comments-for |
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Weakly Supervised Facial Action Unit Recognition Through Adversarial Training
| Title | Weakly Supervised Facial Action Unit Recognition Through Adversarial Training |
| Authors | Guozhu Peng, Shangfei Wang |
| Abstract | Current works on facial action unit (AU) recognition typically require fully AU-annotated facial images for supervised AU classifier training. AU annotation is a time-consuming, expensive, and error-prone process. While AUs are hard to annotate, facial expression is relatively easy to label. Furthermore, there exist strong probabilistic dependencies between expressions and AUs as well as dependencies among AUs. Such dependencies are referred to as domain knowledge. In this paper, we propose a novel AU recognition method that learns AU classifiers from domain knowledge and expression-annotated facial images through adversarial training. Specifically, we first generate pseudo AU labels according to the probabilistic dependencies between expressions and AUs as well as correlations among AUs summarized from domain knowledge. Then we propose a weakly supervised AU recognition method via an adversarial process, in which we simultaneously train two models: a recognition model R, which learns AU classifiers, and a discrimination model D, which estimates the probability that AU labels generated from domain knowledge rather than the recognized AU labels from R. The training procedure for R maximizes the probability of D making a mistake. By leveraging the adversarial mechanism, the distribution of recognized AUs is closed to AU prior distribution from domain knowledge. Furthermore, the proposed weakly supervised AU recognition can be extended to semi-supervised learning scenarios with partially AU-annotated images. Experimental results on three benchmark databases demonstrate that the proposed method successfully leverages the summarized domain knowledge to weakly supervised AU classifier learning through an adversarial process, and thus achieves state-of-the-art performance. |
| Tasks | Facial Action Unit Detection |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Peng_Weakly_Supervised_Facial_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Peng_Weakly_Supervised_Facial_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/weakly-supervised-facial-action-unit |
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Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations
| Title | Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations |
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| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-5000/ |
| https://www.aclweb.org/anthology/N18-5000 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-2018-conference-of-the-3 |
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Classifier Learning With Prior Probabilities for Facial Action Unit Recognition
| Title | Classifier Learning With Prior Probabilities for Facial Action Unit Recognition |
| Authors | Yong Zhang, Weiming Dong, Bao-Gang Hu, Qiang Ji |
| Abstract | Facial action units (AUs) play an important role in human emotion understanding. One big challenge for data-driven AU recognition approaches is the lack of enough AU annotations, since AU annotation requires strong domain expertise. To alleviate this issue, we propose a knowledge-driven method for jointly learning multiple AU classifiers without any AU annotation by leveraging prior probabilities on AUs, including expression-independent and expression-dependent AU probabilities. These prior probabilities are drawn from facial anatomy and emotion studies, and are independent of datasets. We incorporate the prior probabilities on AUs as the constraints into the objective function of multiple AU classifiers, and develop an efficient learning algorithm to solve the formulated problem. Experimental results on five benchmark expression databases demonstrate the effectiveness of the proposed method, especially its generalization ability, and the power of the prior probabilities. |
| Tasks | Facial Action Unit Detection |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Classifier_Learning_With_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Classifier_Learning_With_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/classifier-learning-with-prior-probabilities |
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Not Just Depressed: Bipolar Disorder Prediction on Reddit
| Title | Not Just Depressed: Bipolar Disorder Prediction on Reddit |
| Authors | Ivan Sekulic, Matej Gjurkovi{'c}, Jan {\v{S}}najder |
| Abstract | Bipolar disorder, an illness characterized by manic and depressive episodes, affects more than 60 million people worldwide. We present a preliminary study on bipolar disorder prediction from user-generated text on Reddit, which relies on users{'} self-reported labels. Our benchmark classifiers for bipolar disorder prediction outperform the baselines and reach accuracy and F1-scores of above 86{%}. Feature analysis shows interesting differences in language use between users with bipolar disorders and the control group, including differences in the use of emotion-expressive words. |
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| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/W18-6211/ |
| https://www.aclweb.org/anthology/W18-6211 | |
| PWC | https://paperswithcode.com/paper/not-just-depressed-bipolar-disorder-1 |
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Saying no but meaning yes: negation and sentiment analysis in Basque
| Title | Saying no but meaning yes: negation and sentiment analysis in Basque |
| Authors | Jon Alkorta, Koldo Gojenola, Mikel Iruskieta |
| Abstract | In this work, we have analyzed the effects of negation on the semantic orientation in Basque. The analysis shows that negation markers can strengthen, weaken or have no effect on sentiment orientation of a word or a group of words. Using the Constraint Grammar formalism, we have designed and evaluated a set of linguistic rules to formalize these three phenomena. The results show that two phenomena, strengthening and no change, have been identified accurately and the third one, weakening, with acceptable results. |
| Tasks | Sentiment Analysis |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/W18-6213/ |
| https://www.aclweb.org/anthology/W18-6213 | |
| PWC | https://paperswithcode.com/paper/saying-no-but-meaning-yes-negation-and |
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Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information
| Title | Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information |
| Authors | Sonit Singh |
| Abstract | Recently, there has been increasing interest in the intersection of computer vision and natural language processing. Researchers have studied several interesting tasks, including generating text descriptions from images and videos and language embedding of images. More recent work has further extended the scope of this area to combine videos and language, learning to solve non-visual tasks using visual cues, visual question answering, and visual dialog. Despite a large body of research on the intersection of vision-language technology, its adaption to the medical domain is not fully explored. To address this research gap, we aim to develop machine learning models that can reason jointly on medical images and clinical text for advanced search, retrieval, annotation and description of medical images. |
| Tasks | Image Classification, Machine Translation, Object Detection, Question Answering, Semantic Segmentation, Visual Dialog, Visual Question Answering |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-3005/ |
| https://www.aclweb.org/anthology/P18-3005 | |
| PWC | https://paperswithcode.com/paper/pushing-the-limits-of-radiology-with-joint |
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(Probably) Concave Graph Matching
| Title | (Probably) Concave Graph Matching |
| Authors | Haggai Maron, Yaron Lipman |
| Abstract | In this paper we address the graph matching problem. Following the recent works of \cite{zaslavskiy2009path,Vestner2017} we analyze and generalize the idea of concave relaxations. We introduce the concepts of \emph{conditionally concave} and \emph{probably conditionally concave} energies on polytopes and show that they encapsulate many instances of the graph matching problem, including matching Euclidean graphs and graphs on surfaces. We further prove that local minima of probably conditionally concave energies on general matching polytopes (\eg, doubly stochastic) are with high probability extreme points of the matching polytope (\eg, permutations). |
| Tasks | Graph Matching |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7323-probably-concave-graph-matching |
| http://papers.nips.cc/paper/7323-probably-concave-graph-matching.pdf | |
| PWC | https://paperswithcode.com/paper/probably-concave-graph-matching |
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