Paper Group ANR 508
Committee Selection with Attribute Level Preferences. Self-Attentive Adversarial Stain Normalization. Modulating Surrogates for Bayesian Optimization. Universal Dependency Parsing from Scratch. Towards Learning Structure via Consensus for Face Segmentation and Parsing. DeepSpline: Data-Driven Reconstruction of Parametric Curves and Surfaces. Deep L …
Committee Selection with Attribute Level Preferences
| Title | Committee Selection with Attribute Level Preferences |
| Authors | Venkateswara Rao Kagita, Arun K Pujari, Vineet Padmanabhan, Vikas Kumar |
| Abstract | Approval ballot based committee formation is concerned with aggregating individual approvals of voters. Voters submit their approvals of candidates and these approvals are aggregated to arrive at the optimal committee of specified size. There are several aggregation techniques proposed in the literature and these techniques differ among themselves on the criterion function they optimize. Voters preferences for a candidate is based on his/her opinion on candidate suitability. We note that candidates have attributes that make him/her suitable or otherwise. Hence, it is relevant to approve attributes and select candidates who have the approved attributes. This paper addresses the committee selection problem when voters submit their approvals on attributes. Though attribute based preference is addressed in several contexts, committee selection problem with attribute approval has not been attempted earlier. We note that extending the theory of candidate approval to attribute approval in committee selection problem is not trivial. In this paper, we study different aspects of this problem and show that none of the existing aggregation rules satisfies Unanimity and Justified Representation when attribute based approvals are considered. We propose a new aggregation rule that satisfies both the above properties. We also present other analysis of committee selection problem with attribute approval. |
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| Published | 2019-01-29 |
| URL | http://arxiv.org/abs/1901.10064v1 |
| http://arxiv.org/pdf/1901.10064v1.pdf | |
| PWC | https://paperswithcode.com/paper/committee-selection-with-attribute-level |
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Self-Attentive Adversarial Stain Normalization
| Title | Self-Attentive Adversarial Stain Normalization |
| Authors | Aman Shrivastava, Will Adorno, Lubaina Ehsan, S. Asad Ali, Sean R. Moore, Beatrice C. Amadi, Paul Kelly, Sana Syed, Donald E. Brown |
| Abstract | Hematoxylin and Eosin (H&E) stained Whole Slide Images (WSIs) are utilized for biopsy visualization-based diagnostic and prognostic assessment of diseases. Variation in the H&E staining process across different lab sites can lead to significant variations in biopsy image appearance. These variations introduce an undesirable bias when the slides are examined by pathologists or used for training deep learning models. To reduce this bias, slides need to be translated to a common domain of stain appearance before analysis. We propose a Self-Attentive Adversarial Stain Normalization (SAASN) approach for the normalization of multiple stain appearances to a common domain. This unsupervised generative adversarial approach includes self-attention mechanism for synthesizing images with finer detail while preserving the structural consistency of the biopsy features during translation. SAASN demonstrates consistent and superior performance compared to other popular stain normalization techniques on H&E stained duodenal biopsy image data. |
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| Published | 2019-09-04 |
| URL | https://arxiv.org/abs/1909.01963v2 |
| https://arxiv.org/pdf/1909.01963v2.pdf | |
| PWC | https://paperswithcode.com/paper/self-attentive-adversarial-stain |
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Modulating Surrogates for Bayesian Optimization
| Title | Modulating Surrogates for Bayesian Optimization |
| Authors | Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill D. F. Campbell, Carl Henrik Ek |
| Abstract | Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if noise-free observations can be collected. Common approaches, which try to model the objective as precisely as possible, often fail to make progress by spending too many evaluations modeling irrelevant details. We address this issue by proposing surrogate models that focus on the well-behaved structure in the objective function, which is informative for search, while ignoring detrimental structure that is challenging to model from few observations. First, we demonstrate that surrogate models with appropriate noise distributions can absorb challenging structures in the objective function by treating them as irreducible uncertainty. Secondly, we show that a latent Gaussian process is an excellent surrogate for this purpose, comparing with Gaussian processes with standard noise distributions. We perform numerous experiments on a range of BO benchmarks and find that our approach improves reliability and performance when faced with challenging objective functions. |
| Tasks | Gaussian Processes |
| Published | 2019-06-26 |
| URL | https://arxiv.org/abs/1906.11152v3 |
| https://arxiv.org/pdf/1906.11152v3.pdf | |
| PWC | https://paperswithcode.com/paper/modulated-bayesian-optimization-using-latent |
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Universal Dependency Parsing from Scratch
| Title | Universal Dependency Parsing from Scratch |
| Authors | Peng Qi, Timothy Dozat, Yuhao Zhang, Christopher D. Manning |
| Abstract | This paper describes Stanford’s system at the CoNLL 2018 UD Shared Task. We introduce a complete neural pipeline system that takes raw text as input, and performs all tasks required by the shared task, ranging from tokenization and sentence segmentation, to POS tagging and dependency parsing. Our single system submission achieved very competitive performance on big treebanks. Moreover, after fixing an unfortunate bug, our corrected system would have placed the 2nd, 1st, and 3rd on the official evaluation metrics LAS,MLAS, and BLEX, and would have outperformed all submission systems on low-resource treebank categories on all metrics by a large margin. We further show the effectiveness of different model components through extensive ablation studies. |
| Tasks | Dependency Parsing, Tokenization |
| Published | 2019-01-29 |
| URL | http://arxiv.org/abs/1901.10457v1 |
| http://arxiv.org/pdf/1901.10457v1.pdf | |
| PWC | https://paperswithcode.com/paper/universal-dependency-parsing-from-scratch |
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Towards Learning Structure via Consensus for Face Segmentation and Parsing
| Title | Towards Learning Structure via Consensus for Face Segmentation and Parsing |
| Authors | Iacopo Masi, Joe Mathai, Wael AbdAlmageed |
| Abstract | Face segmentation is the task of densely labeling pixels on the face according to their semantics. While current methods place an emphasis on developing sophisticated architectures, use conditional random fields for smoothness, or rather employ adversarial training, we follow an alternative path towards robust face segmentation and parsing. Occlusions, along with other parts of the face, have a proper structure that needs to be propagated in the model during training. Unlike state-of-the-art methods that treat face segmentation as an independent pixel prediction problem, we argue instead that it should hold highly correlated outputs within the same object pixels. We thereby offer a novel learning mechanism to enforce structure in the prediction via consensus, guided by a robust loss function that forces pixel objects to be consistent with each other. Our face parser is trained by transferring knowledge from another model, yet it encourages spatial consistency while fitting the labels. Different than current practice, our method enjoys pixel-wise predictions, yet paves the way for fewer artifacts, less sparse masks, and spatially coherent outputs. |
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| Published | 2019-11-03 |
| URL | https://arxiv.org/abs/1911.00957v3 |
| https://arxiv.org/pdf/1911.00957v3.pdf | |
| PWC | https://paperswithcode.com/paper/learning-structure-via-consensus-for-face |
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DeepSpline: Data-Driven Reconstruction of Parametric Curves and Surfaces
| Title | DeepSpline: Data-Driven Reconstruction of Parametric Curves and Surfaces |
| Authors | Jun Gao, Chengcheng Tang, Vignesh Ganapathi-Subramanian, Jiahui Huang, Hao Su, Leonidas J. Guibas |
| Abstract | Reconstruction of geometry based on different input modes, such as images or point clouds, has been instrumental in the development of computer aided design and computer graphics. Optimal implementations of these applications have traditionally involved the use of spline-based representations at their core. Most such methods attempt to solve optimization problems that minimize an output-target mismatch. However, these optimization techniques require an initialization that is close enough, as they are local methods by nature. We propose a deep learning architecture that adapts to perform spline fitting tasks accordingly, providing complementary results to the aforementioned traditional methods. We showcase the performance of our approach, by reconstructing spline curves and surfaces based on input images or point clouds. |
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| Published | 2019-01-12 |
| URL | http://arxiv.org/abs/1901.03781v1 |
| http://arxiv.org/pdf/1901.03781v1.pdf | |
| PWC | https://paperswithcode.com/paper/deepspline-data-driven-reconstruction-of |
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Deep Learning for Hyperspectral Image Classification: An Overview
| Title | Deep Learning for Hyperspectral Image Classification: An Overview |
| Authors | Shutao Li, Weiwei Song, Leyuan Fang, Yushi Chen, Pedram Ghamisi, Jón Atli Benediktsson |
| Abstract | Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine learning methods. In addition, hyperspectral imaging often deals with an inherently nonlinear relation between the captured spectral information and the corresponding materials. In recent years, deep learning has been recognized as a powerful feature-extraction tool to effectively address nonlinear problems and widely used in a number of image processing tasks. Motivated by those successful applications, deep learning has also been introduced to classify HSIs and demonstrated good performance. This survey paper presents a systematic review of deep learning-based HSI classification literatures and compares several strategies for this topic. Specifically, we first summarize the main challenges of HSI classification which cannot be effectively overcome by traditional machine learning methods, and also introduce the advantages of deep learning to handle these problems. Then, we build a framework which divides the corresponding works into spectral-feature networks, spatial-feature networks, and spectral-spatial-feature networks to systematically review the recent achievements in deep learning-based HSI classification. In addition, considering the fact that available training samples in the remote sensing field are usually very limited and training deep networks require a large number of samples, we include some strategies to improve classification performance, which can provide some guidelines for future studies on this topic. Finally, several representative deep learning-based classification methods are conducted on real HSIs in our experiments. |
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| Published | 2019-10-26 |
| URL | https://arxiv.org/abs/1910.12861v1 |
| https://arxiv.org/pdf/1910.12861v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-learning-for-hyperspectral-image |
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The Privacy Blanket of the Shuffle Model
| Title | The Privacy Blanket of the Shuffle Model |
| Authors | Borja Balle, James Bell, Adria Gascon, Kobbi Nissim |
| Abstract | This work studies differential privacy in the context of the recently proposed shuffle model. Unlike in the local model, where the server collecting privatized data from users can track back an input to a specific user, in the shuffle model users submit their privatized inputs to a server anonymously. This setup yields a trust model which sits in between the classical curator and local models for differential privacy. The shuffle model is the core idea in the Encode, Shuffle, Analyze (ESA) model introduced by Bittau et al. (SOPS 2017). Recent work by Cheu et al. (EUROCRYPT 2019) analyzes the differential privacy properties of the shuffle model and shows that in some cases shuffled protocols provide strictly better accuracy than local protocols. Additionally, Erlingsson et al. (SODA 2019) provide a privacy amplification bound quantifying the level of curator differential privacy achieved by the shuffle model in terms of the local differential privacy of the randomizer used by each user. In this context, we make three contributions. First, we provide an optimal single message protocol for summation of real numbers in the shuffle model. Our protocol is very simple and has better accuracy and communication than the protocols for this same problem proposed by Cheu et al. Optimality of this protocol follows from our second contribution, a new lower bound for the accuracy of private protocols for summation of real numbers in the shuffle model. The third contribution is a new amplification bound for analyzing the privacy of protocols in the shuffle model in terms of the privacy provided by the corresponding local randomizer. Our amplification bound generalizes the results by Erlingsson et al. to a wider range of parameters, and provides a whole family of methods to analyze privacy amplification in the shuffle model. |
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| Published | 2019-03-07 |
| URL | https://arxiv.org/abs/1903.02837v2 |
| https://arxiv.org/pdf/1903.02837v2.pdf | |
| PWC | https://paperswithcode.com/paper/the-privacy-blanket-of-the-shuffle-model |
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GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing
| Title | GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing |
| Authors | Xiaohong Liu, Yongrui Ma, Zhihao Shi, Jun Chen |
| Abstract | We propose an end-to-end trainable Convolutional Neural Network (CNN), named GridDehazeNet, for single image dehazing. The GridDehazeNet consists of three modules: pre-processing, backbone, and post-processing. The trainable pre-processing module can generate learned inputs with better diversity and more pertinent features as compared to those derived inputs produced by hand-selected pre-processing methods. The backbone module implements a novel attention-based multi-scale estimation on a grid network, which can effectively alleviate the bottleneck issue often encountered in the conventional multi-scale approach. The post-processing module helps to reduce the artifacts in the final output. Experimental results indicate that the GridDehazeNet outperforms the state-of-the-arts on both synthetic and real-world images. The proposed hazing method does not rely on the atmosphere scattering model, and we provide an explanation as to why it is not necessarily beneficial to take advantage of the dimension reduction offered by the atmosphere scattering model for image dehazing, even if only the dehazing results on synthetic images are concerned. |
| Tasks | Dimensionality Reduction, Image Dehazing, Single Image Dehazing |
| Published | 2019-08-08 |
| URL | https://arxiv.org/abs/1908.03245v1 |
| https://arxiv.org/pdf/1908.03245v1.pdf | |
| PWC | https://paperswithcode.com/paper/griddehazenet-attention-based-multi-scale |
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Weakly supervised segmentation from extreme points
| Title | Weakly supervised segmentation from extreme points |
| Authors | Holger Roth, Ling Zhang, Dong Yang, Fausto Milletari, Ziyue Xu, Xiaosong Wang, Daguang Xu |
| Abstract | Annotation of medical images has been a major bottleneck for the development of accurate and robust machine learning models. Annotation is costly and time-consuming and typically requires expert knowledge, especially in the medical domain. Here, we propose to use minimal user interaction in the form of extreme point clicks in order to train a segmentation model that can, in turn, be used to speed up the annotation of medical images. We use extreme points in each dimension of a 3D medical image to constrain an initial segmentation based on the random walker algorithm. This segmentation is then used as a weak supervisory signal to train a fully convolutional network that can segment the organ of interest based on the provided user clicks. We show that the network’s predictions can be refined through several iterations of training and prediction using the same weakly annotated data. Ultimately, our method has the potential to speed up the generation process of new training datasets for the development of new machine learning and deep learning-based models for, but not exclusively, medical image analysis. |
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| Published | 2019-10-02 |
| URL | https://arxiv.org/abs/1910.01236v1 |
| https://arxiv.org/pdf/1910.01236v1.pdf | |
| PWC | https://paperswithcode.com/paper/weakly-supervised-segmentation-from-extreme |
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Fast Single Image Dehazing via Multilevel Wavelet Transform based Optimization
| Title | Fast Single Image Dehazing via Multilevel Wavelet Transform based Optimization |
| Authors | Jiaxi He, Frank Z. Xing, Ran Yang, Cishen Zhang |
| Abstract | The quality of images captured in outdoor environments can be affected by poor weather conditions such as fog, dust, and atmospheric scattering of other particles. This problem can bring extra challenges to high-level computer vision tasks like image segmentation and object detection. However, previous studies on image dehazing suffer from a huge computational workload and corruption of the original image, such as over-saturation and halos. In this paper, we present a novel image dehazing approach based on the optical model for haze images and regularized optimization. Specifically, we convert the non-convex, bilinear problem concerning the unknown haze-free image and light transmission distribution to a convex, linear optimization problem by estimating the atmosphere light constant. Our method is further accelerated by introducing a multilevel Haar wavelet transform. The optimization, instead, is applied to the low frequency sub-band decomposition of the original image. This dimension reduction significantly improves the processing speed of our method and exhibits the potential for real-time applications. Experimental results show that our approach outperforms state-of-the-art dehazing algorithms in terms of both image reconstruction quality and computational efficiency. For implementation details, source code can be publicly accessed via http://github.com/JiaxiHe/Image-and-Video-Dehazing. |
| Tasks | Dimensionality Reduction, Image Dehazing, Image Reconstruction, Object Detection, Semantic Segmentation, Single Image Dehazing |
| Published | 2019-04-18 |
| URL | http://arxiv.org/abs/1904.08573v1 |
| http://arxiv.org/pdf/1904.08573v1.pdf | |
| PWC | https://paperswithcode.com/paper/fast-single-image-dehazing-via-multilevel |
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3D Virtual Garment Modeling from RGB Images
| Title | 3D Virtual Garment Modeling from RGB Images |
| Authors | Yi Xu, Shanglin Yang, Wei Sun, Li Tan, Kefeng Li, Hui Zhou |
| Abstract | We present a novel approach that constructs 3D virtual garment models from photos. Unlike previous methods that require photos of a garment on a human model or a mannequin, our approach can work with various states of the garment: on a model, on a mannequin, or on a flat surface. To construct a complete 3D virtual model, our approach only requires two images as input, one front view and one back view. We first apply a multi-task learning network called JFNet that jointly predicts fashion landmarks and parses a garment image into semantic parts. The predicted landmarks are used for estimating sizing information of the garment. Then, a template garment mesh is deformed based on the sizing information to generate the final 3D model. The semantic parts are utilized for extracting color textures from input images. The results of our approach can be used in various Virtual Reality and Mixed Reality applications. |
| Tasks | Multi-Task Learning |
| Published | 2019-07-31 |
| URL | https://arxiv.org/abs/1908.00114v1 |
| https://arxiv.org/pdf/1908.00114v1.pdf | |
| PWC | https://paperswithcode.com/paper/3d-virtual-garment-modeling-from-rgb-images |
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Learned Conjugate Gradient Descent Network for Massive MIMO Detection
| Title | Learned Conjugate Gradient Descent Network for Massive MIMO Detection |
| Authors | Yi Wei, Ming-Min Zhao, Min-jian Zhao, Ming Lei |
| Abstract | In this work, we consider the use of model-driven deep learning techniques for massive multiple-input multiple-output (MIMO) detection. Compared with conventional MIMO systems, massive MIMO promises improved spectral efficiency, coverage and range. Unfortunately, these benefits are coming at the cost of significantly increased computational complexity. To reduce the complexity of signal detection and guarantee the performance, we present a learned conjugate gradient descent network (LcgNet), which is constructed by unfolding the iterative conjugate gradient descent (CG) detector. In the proposed network, instead of calculating the exact values of the scalar step-sizes, we explicitly learn their universal values. Also, we can enhance the proposed network by augmenting the dimensions of these step-sizes. Furthermore, in order to reduce the memory costs, a novel quantized LcgNet is proposed, where a low-resolution nonuniform quantizer is integrated into the LcgNet to smartly quantize the aforementioned step-sizes. The quantizer is based on a specially designed soft staircase function with learnable parameters to adjust its shape. Meanwhile, due to fact that the number of learnable parameters is limited, the proposed networks are easy and fast to train. Numerical results demonstrate that the proposed network can achieve promising performance with much lower complexity. |
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| Published | 2019-06-10 |
| URL | https://arxiv.org/abs/1906.03814v3 |
| https://arxiv.org/pdf/1906.03814v3.pdf | |
| PWC | https://paperswithcode.com/paper/learned-conjugate-gradient-descent-network |
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MORPHOLO C++ Library for glasses-free multi-view stereo vision and streaming of live 3D video
| Title | MORPHOLO C++ Library for glasses-free multi-view stereo vision and streaming of live 3D video |
| Authors | Enrique Canessa, Livio Tenze |
| Abstract | The MORPHOLO C++ extended Library allows to convert a specific stereoscopic snapshot into a Native multi-view image through morphing algorithms taking into account display calibration data for specific slanted lenticular 3D monitors. MORPHOLO can also be implemented for glasses-free live applicatons of 3D video streaming, and for diverse innovative scientific, engineering and 3D video game applications -see http://www.morpholo.it |
| Tasks | Calibration |
| Published | 2019-12-04 |
| URL | https://arxiv.org/abs/1912.02202v1 |
| https://arxiv.org/pdf/1912.02202v1.pdf | |
| PWC | https://paperswithcode.com/paper/morpholo-c-library-for-glasses-free-multi |
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Feature Aggregation Network for Video Face Recognition
| Title | Feature Aggregation Network for Video Face Recognition |
| Authors | Zhaoxiang Liu, Huan Hu, Jinqiang Bai, Shaohua Li, Shiguo Lian |
| Abstract | This paper aims to learn a compact representation of a video for video face recognition task. We make the following contributions: first, we propose a meta attention-based aggregation scheme which adaptively and fine-grained weighs the feature along each feature dimension among all frames to form a compact and discriminative representation. It makes the best to exploit the valuable or discriminative part of each frame to promote the performance of face recognition, without discarding or despising low quality frames as usual methods do. Second, we build a feature aggregation network comprised of a feature embedding module and a feature aggregation module. The embedding module is a convolutional neural network used to extract a feature vector from a face image, while the aggregation module consists of cascaded two meta attention blocks which adaptively aggregate the feature vectors into a single fixed-length representation. The network can deal with arbitrary number of frames, and is insensitive to frame order. Third, we validate the performance of proposed aggregation scheme. Experiments on publicly available datasets, such as YouTube face dataset and IJB-A dataset, show the effectiveness of our method, and it achieves competitive performances on both the verification and identification protocols. |
| Tasks | Face Recognition |
| Published | 2019-05-06 |
| URL | https://arxiv.org/abs/1905.01796v2 |
| https://arxiv.org/pdf/1905.01796v2.pdf | |
| PWC | https://paperswithcode.com/paper/fine-grained-attention-based-video-face |
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