pwc-archive/papers-with-abstracts huggingface dataset:
Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting
Authors: Yanhong Zeng, Jianlong Fu, Hongyang Chao, Baining Guo
Date: 2019-04-16 00:00:00
Proceeding: CVPR 2019 6
Tasks: Decoder, Image Inpainting, Vocal Bursts Intensity Prediction

High-quality image inpainting requires filling missing regions in a damaged image with plausible content. Existing works either fill the regions by copying image patches or generating semantically-coherent patches from region context, while neglect the fact that both visual and semantic plausibility are highly-demanded. In this paper, we propose a Pyramid-context ENcoder Network (PEN-Net) for image inpainting by deep generative models. The PEN-Net is built upon a U-Net structure, which can restore an image by encoding contextual semantics from full resolution input, and decoding the learned semantic features back into images. Specifically, we propose a pyramid-context encoder, which progressively learns region affinity by attention from a high-level semantic feature map and transfers the learned attention to the previous low-level feature map. As the missing content can be filled by attention transfer from deep to shallow in a pyramid fashion, both visual and semantic coherence for image inpainting can be ensured. We further propose a multi-scale decoder with deeply-supervised pyramid losses and an adversarial loss. Such a design not only results in fast convergence in training, but more realistic results in testing. Extensive experiments on various datasets show the superior performance of the proposed network
Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images
Authors: Ying Da Wang, Ryan Armstrong, Peyman Mostaghimi
Date: 2019-04-16 00:00:00
Tasks: Image Augmentation, Image Restoration, Image Segmentation, Image Super-Resolution, Semantic Segmentation, Super-Resolution

Single Image Super Resolution (SISR) techniques based on Super Resolution
Convolutional Neural Networks (SRCNN) are applied to micro-computed tomography
({\mu}CT) images of sandstone and carbonate rocks. Digital rock imaging is
limited by the capability of the scanning device resulting in trade-offs
between resolution and field of view, and super resolution methods tested in
this study aim to compensate for these limits. SRCNN models SR-Resnet, Enhanced
Deep SR (EDSR), and Wide-Activation Deep SR (WDSR) are used on the Digital Rock
Super Resolution 1 (DRSRD1) Dataset of 4x downsampled images, comprising of
2000 high resolution (800x800) raw micro-CT images of Bentheimer sandstone and
Estaillades carbonate. The trained models are applied to the validation and
test data within the dataset and show a 3-5 dB rise in image quality compared
to bicubic interpolation, with all tested models performing within a 0.1 dB
range. Difference maps indicate that edge sharpness is completely recovered in
images within the scope of the trained model, with only high frequency noise
related detail loss. We find that aside from generation of high-resolution
images, a beneficial side effect of super resolution methods applied to
synthetically downgraded images is the removal of image noise while recovering
edgewise sharpness which is beneficial for the segmentation process. The model
is also tested against real low-resolution images of Bentheimer rock with image
augmentation to account for natural noise and blur. The SRCNN method is shown
to act as a preconditioner for image segmentation under these circumstances
which naturally leads to further future development and training of models that
segment an image directly. Image restoration by SRCNN on the rock images is of
significantly higher quality than traditional methods and suggests SRCNN
methods are a viable processing step in a digital rock workflow.
An extended description logic system with knowledge element based on ALC
Authors: Bin Wen, Jianhou Gan, Juan L. G. Guirao, Wei Gao
Date: 2019-04-16 00:00:00
Tasks: Attribute, Management, Object

With the rise of knowledge management and knowledge economy, the knowledge
elements that directly link and embody the knowledge system have become the
research focus and hotspot in certain areas. The existing knowledge element
representation methods are limited in functions to deal with the formality,
logic and reasoning. Based on description logic ALC and the common knowledge
element model, in order to describe the knowledge element, the description
logic ALC is expanded. The concept is extended to two diferent ones (that is,
the object knowledge element concept and the attribute knowledge element
concept). The relationship is extended to three (that is, relationship between
object knowledge element concept and attribute knowledge element concept,
relationship among object knowledge element concepts, relationship among
attribute knowledge element concepts), and the inverse relationship constructor
is added to propose a description logic KEDL system. By demonstrating, the
relevant properties, such as completeness, reliability,of the described logic
system KEDL are obtained. Finally, it is verified by the example that the
description logic KEDL system has strong knowledge element description ability.
c-trie++: A Dynamic Trie Tailored for Fast Prefix Searches
Authors: Kazuya Tsuruta, Dominik Köppl, Shunsuke Kanda, Yuto Nakashima, Shunsuke Inenaga, Hideo Bannai, Masayuki Takeda
Date: 2019-04-16 00:00:00

Given a dynamic set $K$ of $k$ strings of total length $n$ whose characters are drawn from an alphabet of size $\sigma$, a keyword dictionary is a data structure built on $K$ that provides locate, prefix search, and update operations on $K$. Under the assumption that $\alpha = w / \lg \sigma$ characters fit into a single machine word $w$, we propose a keyword dictionary that represents $K$ in $n \lg \sigma + \Theta(k \lg n)$ bits of space, supporting all operations in $O(m / \alpha + \lg \alpha)$ expected time on an input string of length $m$ in the word RAM model. This data structure is underlined with an exhaustive practical evaluation, highlighting the practical usefulness of the proposed data structure, especially for prefix searches - one of the most elementary keyword dictionary operations.
DSTP-RNN: a dual-stage two-phase attention-based recurrent neural networks for long-term and multivariate time series prediction
Authors: Yeqi Liu, Chuanyang Gong, Ling Yang, Yingyi Chen
Date: 2019-04-16 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis, Time Series Prediction

Long-term prediction of multivariate time series is still an important but
challenging problem. The key to solve this problem is to capture the spatial
correlations at the same time, the spatio-temporal relationships at different
times and the long-term dependence of the temporal relationships between
different series. Attention-based recurrent neural networks (RNN) can
effectively represent the dynamic spatio-temporal relationships between
exogenous series and target series, but it only performs well in one-step time
prediction and short-term time prediction. In this paper, inspired by human
attention mechanism including the dual-stage two-phase (DSTP) model and the
influence mechanism of target information and non-target information, we
propose DSTP-based RNN (DSTP-RNN) and DSTP-RNN-2 respectively for long-term
time series prediction. Specifically, we first propose the DSTP-based structure
to enhance the spatial correlations between exogenous series. The first phase
produces violent but decentralized response weight, while the second phase
leads to stationary and concentrated response weight. Secondly, we employ
multiple attentions on target series to boost the long-term dependence.
Finally, we study the performance of deep spatial attention mechanism and
provide experiment and interpretation. Our methods outperform nine baseline
methods on four datasets in the fields of energy, finance, environment and
medicine, respectively.
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms
Authors: Nhat Ho, Tianyi Lin, Michael. I. Jordan
Date: 2019-04-16 00:00:00
Tasks: Clustering

The filtering-clustering models, including trend filtering and convex clustering, have become an important source of ideas and modeling tools in machine learning and related fields. The statistical guarantee of optimal solutions in these models has been extensively studied yet the investigations on the computational aspect have remained limited. In particular, practitioners often employ the first-order algorithms in real-world applications and are impressed by their superior performance regardless of ill-conditioned structures of difference operator matrices, thus leaving open the problem of understanding the convergence property of first-order algorithms. This paper settles this open problem and contributes to the broad interplay between statistics and optimization by identifying a \textit{global error bound} condition, which is satisfied by a large class of dual filtering-clustering problems, and designing a class of \textit{generalized dual gradient ascent} algorithm, which is \textit{optimal} first-order algorithms in deterministic, finite-sum and online settings. Our results are new and help explain why the filtering-clustering models can be efficiently solved by first-order algorithms. We also provide the detailed convergence rate analysis for the proposed algorithms in different settings, shedding light on their potential to solve the filtering-clustering models efficiently. We also conduct experiments on real datasets and the numerical results demonstrate the effectiveness of our algorithms.
Deep Neural Network Based Hyperspectral Pixel Classification With Factorized Spectral-Spatial Feature Representation
Authors: Jingzhou Chen, Siyu Chen, Peilin Zhou, Yuntao Qian
Date: 2019-04-16 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Deep learning has been widely used for hyperspectral pixel classification due
to its ability of generating deep feature representation. However, how to
construct an efficient and powerful network suitable for hyperspectral data is
still under exploration. In this paper, a novel neural network model is
designed for taking full advantage of the spectral-spatial structure of
hyperspectral data. Firstly, we extract pixel-based intrinsic features from
rich yet redundant spectral bands by a subnetwork with supervised pre-training
scheme. Secondly, in order to utilize the local spatial correlation among
pixels, we share the previous subnetwork as a spectral feature extractor for
each pixel in a patch of image, after which the spectral features of all pixels
in a patch are combined and feeded into the subsequent classification
subnetwork. Finally, the whole network is further fine-tuned to improve its
classification performance. Specially, the spectral-spatial factorization
scheme is applied in our model architecture, making the network size and the
number of parameters great less than the existing spectral-spatial deep
networks for hyperspectral image classification. Experiments on the
hyperspectral data sets show that, compared with some state-of-art deep
learning methods, our method achieves better classification results while
having smaller network size and less parameters.
Fashion-AttGAN: Attribute-Aware Fashion Editing with Multi-Objective GAN
Authors: Qing Ping, Bing Wu, Wanying Ding, Jiangbo Yuan
Date: 2019-04-16 00:00:00
Tasks: Attribute

In this paper, we introduce attribute-aware fashion-editing, a novel task, to
the fashion domain. We re-define the overall objectives in AttGAN and propose
the Fashion-AttGAN model for this new task. A dataset is constructed for this
task with 14,221 and 22 attributes, which has been made publically available.
Experimental results show the effectiveness of our Fashion-AttGAN on fashion
editing over the original AttGAN.
A Bayesian Perspective on the Deep Image Prior
Authors: Zezhou Cheng, Matheus Gadelha, Subhransu Maji, Daniel Sheldon
Date: 2019-04-16 00:00:00
Proceeding: CVPR 2019 6
Tasks: Bayesian Inference, Denoising, Image Reconstruction

The deep image prior was recently introduced as a prior for natural images.
It represents images as the output of a convolutional network with random
inputs. For "inference", gradient descent is performed to adjust network
parameters to make the output match observations. This approach yields good
performance on a range of image reconstruction tasks. We show that the deep
image prior is asymptotically equivalent to a stationary Gaussian process prior
in the limit as the number of channels in each layer of the network goes to
infinity, and derive the corresponding kernel. This informs a Bayesian approach
to inference. We show that by conducting posterior inference using stochastic
gradient Langevin we avoid the need for early stopping, which is a drawback of
the current approach, and improve results for denoising and impainting tasks.
We illustrate these intuitions on a number of 1D and 2D signal reconstruction
tasks.
Point cloud registration: matching a maximal common subset on pointclouds with noise (with 2D implementation)
Authors: Jorge Arce Garro, David Jiménez López
Date: 2019-04-16 00:00:00
Tasks: Point Cloud Registration

We analyze the problem of determining whether 2 given point clouds in 2D,
with any distinct cardinality and any number of outliers, have subsets of the
same size that can be matched via a rigid motion. This problem is important,
for example, in the application of fingerprint matching with incomplete data.
We propose an algorithm that, under assumptions on the noise tolerance, allows
to find corresponding subclouds of the maximum possible size. Our procedure
optimizes a potential energy function to do so, which was first inspired in the
potential energy interaction that occurs between point charges in
electrostatics.
Spoof detection using time-delay shallow neural network and feature switching
Authors: Mari Ganesh Kumar, Suvidha Rupesh Kumar, Saranya M, B. Bharathi, Hema A. Murthy
Date: 2019-04-16 00:00:00
Tasks: Speaker Verification, Speech Synthesis, Voice Anti-spoofing, Voice Conversion

Detecting spoofed utterances is a fundamental problem in voice-based biometrics. Spoofing can be performed either by logical accesses like speech synthesis, voice conversion or by physical accesses such as replaying the pre-recorded utterance. Inspired by the state-of-the-art \emph{x}-vector based speaker verification approach, this paper proposes a time-delay shallow neural network (TD-SNN) for spoof detection for both logical and physical access. The novelty of the proposed TD-SNN system vis-a-vis conventional DNN systems is that it can handle variable length utterances during testing. Performance of the proposed TD-SNN systems and the baseline Gaussian mixture models (GMMs) is analyzed on the ASV-spoof-2019 dataset. The performance of the systems is measured in terms of the minimum normalized tandem detection cost function (min-t-DCF). When studied with individual features, the TD-SNN system consistently outperforms the GMM system for physical access. For logical access, GMM surpasses TD-SNN systems for certain individual features. When combined with the decision-level feature switching (DLFS) paradigm, the best TD-SNN system outperforms the best baseline GMM system on evaluation data with a relative improvement of 48.03\% and 49.47\% for both logical and physical access, respectively.
Counterfactual Visual Explanations
Authors: Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, Stefan Lee
Date: 2019-04-16 00:00:00
Tasks: counterfactual, General Classification, image-classification, Image Classification

In this work, we develop a technique to produce counterfactual visual explanations. Given a 'query' image $I$ for which a vision system predicts class $c$, a counterfactual visual explanation identifies how $I$ could change such that the system would output a different specified class $c'$. To do this, we select a 'distractor' image $I'$ that the system predicts as class $c'$ and identify spatial regions in $I$ and $I'$ such that replacing the identified region in $I$ with the identified region in $I'$ would push the system towards classifying $I$ as $c'$. We apply our approach to multiple image classification datasets generating qualitative results showcasing the interpretability and discriminativeness of our counterfactual explanations. To explore the effectiveness of our explanations in teaching humans, we present machine teaching experiments for the task of fine-grained bird classification. We find that users trained to distinguish bird species fare better when given access to counterfactual explanations in addition to training examples.
Decoupling Localization and Classification in Single Shot Temporal Action Detection
Authors: Yupan Huang, Qi Dai, Yutong Lu
Date: 2019-04-16 00:00:00
Tasks: Action Detection, Classification, General Classification, Temporal Action Localization

Video temporal action detection aims to temporally localize and recognize the
action in untrimmed videos. Existing one-stage approaches mostly focus on
unifying two subtasks, i.e., localization of action proposals and
classification of each proposal through a fully shared backbone. However, such
design of encapsulating all components of two subtasks in one single network
might restrict the training by ignoring the specialized characteristic of each
subtask. In this paper, we propose a novel Decoupled Single Shot temporal
Action Detection (Decouple-SSAD) method to mitigate such problem by decoupling
the localization and classification in a one-stage scheme. Particularly, two
separate branches are designed in parallel to enable each component to own
representations privately for accurate localization or classification. Each
branch produces a set of action anchor layers by applying deconvolution to the
feature maps of the main stream. Each branch produces a set of feature maps by
applying deconvolution to the feature maps of the main stream. High-level
semantic information from deeper layers is thus incorporated to enhance the
feature representations. We conduct extensive experiments on THUMOS14 dataset
and demonstrate superior performance over state-of-the-art methods. Our code is
available online.
Photofeeler-D3: A Neural Network with Voter Modeling for Dating Photo Impression Prediction
Authors: Agastya Kalra, Ben Peterson
Date: 2019-04-16 00:00:00
Tasks: Facial Beauty Prediction

In just a few years, online dating has become the dominant way that young people meet to date, making the deceptively error-prone task of picking good dating profile photos vital to a generation's ability to form romantic connections. Until now, artificial intelligence approaches to Dating Photo Impression Prediction (DPIP) have been very inaccurate, unadaptable to real-world application, and have only taken into account a subject's physical attractiveness. To that effect, we propose Photofeeler-D3 - the first convolutional neural network as accurate as 10 human votes for how smart, trustworthy, and attractive the subject appears in highly variable dating photos. Our "attractive" output is also applicable to Facial Beauty Prediction (FBP), making Photofeeler-D3 state-of-the-art for both DPIP and FBP. We achieve this by leveraging Photofeeler's Dating Dataset (PDD) with over 1 million images and tens of millions of votes, our novel technique of voter modeling, and cutting-edge computer vision techniques.
Shortest Paths in HSI Space for Color Texture Classification
Authors: Mingxin Jin, Yongsheng Dong, Lintao Zheng, Lingfei Liang, Tianyu Wang, Hongyan zhang
Date: 2019-04-16 00:00:00
Tasks: Classification, General Classification, Texture Classification

Color texture representation is an important step in the task of texture
classification. Shortest paths was used to extract color texture features from
RGB and HSV color spaces. In this paper, we propose to use shortest paths in
the HSI space to build a texture representation for classification. In
particular, two undirected graphs are used to model the H channel and the S and
I channels respectively in order to represent a color texture image. Moreover,
the shortest paths is constructed by using four pairs of pixels according to
different scales and directions of the texture image. Experimental results on
colored Brodatz and USPTex databases reveal that our proposed method is
effective, and the highest classification accuracy rate is 96.93% in the
Brodatz database.
A Medical Literature Search System for Identifying Effective Treatments in Precision Medicine
Authors: Jiaming Qu, Yue Wang
Date: 2019-04-16 00:00:00
Tasks: Retrieval, Text Retrieval

The Precision Medicine Initiative states that treatments for a patient should
take into account not only the patient's disease, but his/her specific genetic
variation as well. The vast biomedical literature holds the potential for
physicians to identify effective treatment options for a cancer patient.
However, the complexity and ambiguity of medical terms can result in vocabulary
mismatch between the physician's query and the literature. The physician's
search intent (finding treatments instead of other types of studies) is
difficult to explicitly formulate in a query. Therefore, simple ad hot
retrieval approach will suffer from low recall and precision. In this paper, we
propose a new retrieval system that helps physicians identify effective
treatments in precision medicine. Given a cancer patient with a specific
disease, genetic variation, and demographic information, the system aims to
identify biomedical publications that report effective treatments. We approach
this goal from two directions. First, we expand the original disease and gene
terms using biomedical knowledge bases to improve recall of the initial
retrieval. We then improve precision by promoting treatment-related
publications to the top using a machine learning reranker trained on 2017 Text
Retrieval Conference Precision Medicine (PM) track corpus. Batch evaluation
results on 2018 PM track corpus show that the proposed approach effectively
improves both recall and precision, achieving performance comparable to the top
entries on the leaderboard of 2018 PM track.
Single Pixel Reconstruction for One-stage Instance Segmentation
Authors: Jun Yu, Jinghan Yao, Jian Zhang, Zhou Yu, DaCheng Tao
Date: 2019-04-16 00:00:00
Tasks: Instance Segmentation, Region Proposal, Segmentation, Semantic Segmentation

Object instance segmentation is one of the most fundamental but challenging tasks in computer vision, and it requires the pixel-level image understanding. Most existing approaches address this problem by adding a mask prediction branch to a two-stage object detector with the Region Proposal Network (RPN). Although producing good segmentation results, the efficiency of these two-stage approaches is far from satisfactory, restricting their applicability in practice. In this paper, we propose a one-stage framework, SPRNet, which performs efficient instance segmentation by introducing a single pixel reconstruction (SPR) branch to off-the-shelf one-stage detectors. The added SPR branch reconstructs the pixel-level mask from every single pixel in the convolution feature map directly. Using the same ResNet-50 backbone, SPRNet achieves comparable mask AP to Mask R-CNN at a higher inference speed, and gains all-round improvements on box AP at every scale comparing with RetinaNet.