pwc-archive/papers-with-abstracts huggingface dataset:
Polynomial-based rotation invariant features
Authors: Jarek Duda
Date: 2018-01-03 00:00:00
Tasks: BIG-bench Machine Learning

One of basic difficulties of machine learning is handling unknown rotations
of objects, for example in image recognition. A related problem is evaluation
of similarity of shapes, for example of two chemical molecules, for which
direct approach requires costly pairwise rotation alignment and comparison.
Rotation invariants are useful tools for such purposes, allowing to extract
features describing shape up to rotation, which can be used for example to
search for similar rotated patterns, or fast evaluation of similarity of shapes
e.g. for virtual screening, or machine learning including features directly
describing shape. A standard approach are rotationally invariant cylindrical or
spherical harmonics, which can be seen as based on polynomials on sphere,
however, they provide very few invariants - only one per degree of polynomial.
There will be discussed a general approach to construct arbitrarily large sets
of rotation invariants of polynomials, for degree $D$ in $\mathbb{R}^n$ up to
$O(n^D)$ independent invariants instead of $O(D)$ offered by standard
approaches, possibly also a complete set: providing not only necessary, but
also sufficient condition for differing only by rotation (and reflectional
symmetry).
Deep Learning Interior Tomography for Region-of-Interest Reconstruction
Authors: Yoseob Han, Jawook Gu, Jong Chul Ye
Date: 2017-12-29 00:00:00
Tasks: Deep Learning

Interior tomography for the region-of-interest (ROI) imaging has advantages
of using a small detector and reducing X-ray radiation dose. However, standard
analytic reconstruction suffers from severe cupping artifacts due to existence
of null space in the truncated Radon transform. Existing penalized
reconstruction methods may address this problem but they require extensive
computations due to the iterative reconstruction. Inspired by the recent deep
learning approaches to low-dose and sparse view CT, here we propose a deep
learning architecture that removes null space signals from the FBP
reconstruction. Experimental results have shown that the proposed method
provides near-perfect reconstruction with about 7-10 dB improvement in PSNR
over existing methods in spite of significantly reduced run-time complexity.
Clustering of Data with Missing Entries
Authors: Sunrita Poddar, Mathews Jacob
Date: 2018-01-03 00:00:00
Tasks: Clustering

The analysis of large datasets is often complicated by the presence of
missing entries, mainly because most of the current machine learning algorithms
are designed to work with full data. The main focus of this work is to
introduce a clustering algorithm, that will provide good clustering even in the
presence of missing data. The proposed technique solves an $\ell_0$ fusion
penalty based optimization problem to recover the clusters. We theoretically
analyze the conditions needed for the successful recovery of the clusters. We
also propose an algorithm to solve a relaxation of this problem using
saturating non-convex fusion penalties. The method is demonstrated on simulated
and real datasets, and is observed to perform well in the presence of large
fractions of missing entries.
Computational complexity lower bounds of certain discrete Radon transform approximations
Authors: Timur M. Khanipov
Date: 2018-01-03 00:00:00

For the computational model where only additions are allowed, the
$\Omega(n^2\log n)$ lower bound on operations count with respect to image size
$n\times n$ is obtained for two types of the discrete Radon transform
implementations: the fast Hough transform and a generic strip pattern class
which includes the classical Hough transform, implying the fast Hough transform
algorithm asymptotic optimality. The proofs are based on a specific result from
the boolean circuits complexity theory and are generalized for the case of
boolean $\vee$ binary operation.
Spot the Difference by Object Detection
Authors: Junhui Wu, Yun Ye, Yu Chen, Zhi Weng
Date: 2018-01-03 00:00:00
Tasks: Change Detection, Object, object-detection, Object Detection

In this paper, we propose a simple yet effective solution to a change
detection task that detects the difference between two images, which we call
"spot the difference". Our approach uses CNN-based object detection by stacking
two aligned images as input and considering the differences between the two
images as objects to detect. An early-merging architecture is used as the
backbone network. Our method is accurate, fast and robust while using very
cheap annotation. We verify the proposed method on the task of change detection
between the digital design and its photographic image of a book. Compared to
verification based methods, our object detection based method outperforms other
methods by a large margin and gives extra information of location. We compress
the network and achieve 24 times acceleration while keeping the accuracy.
Besides, as we synthesize the training data for detection using weakly labeled
images, our method does not need expensive bounding box annotation.
Implementation of Deep Convolutional Neural Network in Multi-class Categorical Image Classification
Authors: Pushparaja Murugan
Date: 2018-01-03 00:00:00
Tasks: Bayesian Optimization, Classification, General Classification, image-classification, Image Classification

Convolutional Neural Networks has been implemented in many complex machine
learning takes such as image classification, object identification, autonomous
vehicle and robotic vision tasks. However, ConvNet architecture efficiency and
accuracy depend on a large number of fac- tors. Also, the complex architecture
requires a significant amount of data to train and involves with a large number
of hyperparameters that increases the computational expenses and difficul-
ties. Hence, it is necessary to address the limitations and techniques to
overcome the barriers to ensure that the architecture performs well in complex
visual tasks. This article is intended to develop an efficient ConvNet
architecture for multi-class image categorical classification applica- tion. In
the development of the architecture, large pool of grey scale images are taken
as input information images and split into training and test datasets. The
numerously available technique is implemented to reduce the overfitting and
poor generalization of the network. The hyperpa- rameters of determined by
Bayesian Optimization with Gaussian Process prior algorithm. ReLu non-linear
activation function is implemented after the convolutional layers. Max pooling
op- eration is carried out to downsampling the data points in pooling layers.
Cross-entropy loss function is used to measure the performance of the
architecture where the softmax is used in the classification layer. Mini-batch
gradient descent with Adam optimizer algorithm is used for backpropagation.
Developed architecture is validated with confusion matrix and classification
report.
Detecting Cross-Lingual Plagiarism Using Simulated Word Embeddings
Authors: Victor Thompson
Date: 2017-12-29 00:00:00
Tasks: Word Embeddings

Cross-lingual plagiarism (CLP) occurs when texts written in one language are
translated into a different language and used without acknowledging the
original sources. One of the most common methods for detecting CLP requires
online machine translators (such as Google or Microsoft translate) which are
not always available, and given that plagiarism detection typically involves
large document comparison, the amount of translations required would overwhelm
an online machine translator, especially when detecting plagiarism over the
web. In addition, when translated texts are replaced with their synonyms, using
online machine translators to detect CLP would result in poor performance. This
paper addresses the problem of cross-lingual plagiarism detection (CLPD) by
proposing a model that uses simulated word embeddings to reproduce the
predictions of an online machine translator (Google translate) when detecting
CLP. The simulated embeddings comprise of translated words in different
languages mapped in a common space, and replicated to increase the prediction
probability of retrieving the translations of a word (and their synonyms) from
the model. Unlike most existing models, the proposed model does not require
parallel corpora, and accommodates multiple languages (multi-lingual). We
demonstrated the effectiveness of the proposed model in detecting CLP in
standard datasets that contain CLP cases, and evaluated its performance against
a state-of-the-art baseline that relies on online machine translator (T+MA
model). Evaluation results revealed that the proposed model is not only
effective in detecting CLP, it outperformed the baseline. The results indicate
that CLP could be detected with state-of-the-art performances by leveraging the
prediction accuracy of an internet translator with word embeddings, without
relying on internet translators.
ViZDoom: DRQN with Prioritized Experience Replay, Double-Q Learning, & Snapshot Ensembling
Authors: Christopher Schulze, Marcus Schulze
Date: 2018-01-03 00:00:00
Tasks: Q-Learning, Reinforcement Learning

ViZDoom is a robust, first-person shooter reinforcement learning environment,
characterized by a significant degree of latent state information. In this
paper, double-Q learning and prioritized experience replay methods are tested
under a certain ViZDoom combat scenario using a competitive deep recurrent
Q-network (DRQN) architecture. In addition, an ensembling technique known as
snapshot ensembling is employed using a specific annealed learning rate to
observe differences in ensembling efficacy under these two methods. Annealed
learning rates are important in general to the training of deep neural network
models, as they shake up the status-quo and counter a model's tending towards
local optima. While both variants show performance exceeding those of built-in
AI agents of the game, the known stabilizing effects of double-Q learning are
illustrated, and priority experience replay is again validated in its
usefulness by showing immediate results early on in agent development, with the
caveat that value overestimation is accelerated in this case. In addition, some
unique behaviors are observed to develop for priority experience replay (PER)
and double-Q (DDQ) variants, and snapshot ensembling of both PER and DDQ proves
a valuable method for improving performance of the ViZDoom Marine.
Hybrid Jacobian and Gauss-Seidel proximal block coordinate update methods for linearly constrained convex programming
Authors: Yangyang Xu
Date: 2016-08-13 00:00:00

Recent years have witnessed the rapid development of block coordinate update
(BCU) methods, which are particularly suitable for problems involving
large-sized data and/or variables. In optimization, BCU first appears as the
coordinate descent method that works well for smooth problems or those with
separable nonsmooth terms and/or separable constraints. As nonseparable
constraints exist, BCU can be applied under primal-dual settings.
  In the literature, it has been shown that for weakly convex problems with
nonseparable linear constraint, BCU with fully Gauss-Seidel updating rule may
fail to converge and that with fully Jacobian rule can converge sublinearly.
However, empirically the method with Jacobian update is usually slower than
that with Gauss-Seidel rule. To maintain their advantages, we propose a hybrid
Jacobian and Gauss-Seidel BCU method for solving linearly constrained
multi-block structured convex programming, where the objective may have a
nonseparable quadratic term and separable nonsmooth terms. At each primal block
variable update, the method approximates the augmented Lagrangian function at
an affine combination of the previous two iterates, and the affinely mixing
matrix with desired nice properties can be chosen through solving a
semidefinite programming. We show that the hybrid method enjoys the theoretical
convergence guarantee as Jacobian BCU. In addition, we numerically demonstrate
that the method can perform as well as Gauss-Seidel method and better than a
recently proposed randomized primal-dual BCU method.
Single-Shot Refinement Neural Network for Object Detection
Authors: Shifeng Zhang, Longyin Wen, Xiao Bian, Zhen Lei, Stan Z. Li
Date: 2017-11-18 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object, object-detection, Object Detection

For object detection, the two-stage approach (e.g., Faster R-CNN) has been
achieving the highest accuracy, whereas the one-stage approach (e.g., SSD) has
the advantage of high efficiency. To inherit the merits of both while
overcoming their disadvantages, in this paper, we propose a novel single-shot
based detector, called RefineDet, that achieves better accuracy than two-stage
methods and maintains comparable efficiency of one-stage methods. RefineDet
consists of two inter-connected modules, namely, the anchor refinement module
and the object detection module. Specifically, the former aims to (1) filter
out negative anchors to reduce search space for the classifier, and (2)
coarsely adjust the locations and sizes of anchors to provide better
initialization for the subsequent regressor. The latter module takes the
refined anchors as the input from the former to further improve the regression
and predict multi-class label. Meanwhile, we design a transfer connection block
to transfer the features in the anchor refinement module to predict locations,
sizes and class labels of objects in the object detection module. The
multi-task loss function enables us to train the whole network in an end-to-end
way. Extensive experiments on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO
demonstrate that RefineDet achieves state-of-the-art detection accuracy with
high efficiency. Code is available at https://github.com/sfzhang15/RefineDet
FaceBoxes: A CPU Real-time Face Detector with High Accuracy
Authors: Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, Stan Z. Li
Date: 2017-08-17 00:00:00
Tasks: CPU, Face Detection, GPU, Vocal Bursts Intensity Prediction

Although tremendous strides have been made in face detection, one of the
remaining open challenges is to achieve real-time speed on the CPU as well as
maintain high performance, since effective models for face detection tend to be
computationally prohibitive. To address this challenge, we propose a novel face
detector, named FaceBoxes, with superior performance on both speed and
accuracy. Specifically, our method has a lightweight yet powerful network
structure that consists of the Rapidly Digested Convolutional Layers (RDCL) and
the Multiple Scale Convolutional Layers (MSCL). The RDCL is designed to enable
FaceBoxes to achieve real-time speed on the CPU. The MSCL aims at enriching the
receptive fields and discretizing anchors over different layers to handle faces
of various scales. Besides, we propose a new anchor densification strategy to
make different types of anchors have the same density on the image, which
significantly improves the recall rate of small faces. As a consequence, the
proposed detector runs at 20 FPS on a single CPU core and 125 FPS using a GPU
for VGA-resolution images. Moreover, the speed of FaceBoxes is invariant to the
number of faces. We comprehensively evaluate this method and present
state-of-the-art detection performance on several face detection benchmark
datasets, including the AFW, PASCAL face, and FDDB. Code is available at
https://github.com/sfzhang15/FaceBoxes
RADNET: Radiologist Level Accuracy using Deep Learning for HEMORRHAGE detection in CT Scans
Authors: Monika Grewal, Muktabh Mayank Srivastava, Pulkit Kumar, Srikrishna Varadarajan
Date: 2017-10-13 00:00:00
Tasks: Computed Tomography (CT)

We describe a deep learning approach for automated brain hemorrhage detection
from computed tomography (CT) scans. Our model emulates the procedure followed
by radiologists to analyse a 3D CT scan in real-world. Similar to radiologists,
the model sifts through 2D cross-sectional slices while paying close attention
to potential hemorrhagic regions. Further, the model utilizes 3D context from
neighboring slices to improve predictions at each slice and subsequently,
aggregates the slice-level predictions to provide diagnosis at CT level. We
refer to our proposed approach as Recurrent Attention DenseNet (RADnet) as it
employs original DenseNet architecture along with adding the components of
attention for slice level predictions and recurrent neural network layer for
incorporating 3D context. The real-world performance of RADnet has been
benchmarked against independent analysis performed by three senior radiologists
for 77 brain CTs. RADnet demonstrates 81.82% hemorrhage prediction accuracy at
CT level that is comparable to radiologists. Further, RADnet achieves higher
recall than two of the three radiologists, which is remarkable.
Joint convolutional neural pyramid for depth map super-resolution
Authors: Yi Xiao, Xiang Cao, Xianyi Zhu, Renzhi Yang, Yan Zheng
Date: 2018-01-03 00:00:00
Tasks: Depth Map Super-Resolution, Super-Resolution

High-resolution depth map can be inferred from a low-resolution one with the
guidance of an additional high-resolution texture map of the same scene.
Recently, deep neural networks with large receptive fields are shown to benefit
applications such as image completion. Our insight is that super resolution is
similar to image completion, where only parts of the depth values are precisely
known. In this paper, we present a joint convolutional neural pyramid model
with large receptive fields for joint depth map super-resolution. Our model
consists of three sub-networks, two convolutional neural pyramids concatenated
by a normal convolutional neural network. The convolutional neural pyramids
extract information from large receptive fields of the depth map and guidance
map, while the convolutional neural network effectively transfers useful
structures of the guidance image to the depth image. Experimental results show
that our model outperforms existing state-of-the-art algorithms not only on
data pairs of RGB/depth images, but also on other data pairs like
color/saliency and color-scribbles/colorized images.
Local communities obstruct global consensus: Naming game on multi-local-world networks
Authors: Yang Lou, Guanrong Chen, Zhengping Fan, Luna Xiang
Date: 2016-05-20 00:00:00
Tasks: Clustering

Community structure is essential for social communications, where individuals
belonging to the same community are much more actively interacting and
communicating with each other than those in different communities within the
human society. Naming game, on the other hand, is a social communication model
that simulates the process of learning a name of an object within a community
of humans, where the individuals can generally reach global consensus
asymptotically through iterative pair-wise conversations. The underlying
network indicates the relationships among the individuals. In this paper, three
typical topologies, namely random-graph, small-world and scale-free networks,
are employed, which are embedded with the multi-local-world community
structure, to study the naming game. Simulations show that 1) the convergence
process to global consensus is getting slower as the community structure
becomes more prominent, and eventually might fail; 2) if the inter-community
connections are sufficiently dense, neither the number nor the size of the
communities affects the convergence process; and 3) for different topologies
with the same average node-degree, local clustering of individuals obstruct or
prohibit global consensus to take place. The results reveal the role of local
communities in a global naming game in social network studies.
Emerging from Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer
Authors: Chongyi Li, Jichang Guo, Chunle Guo
Date: 2017-10-19 00:00:00
Tasks: SSIM

Underwater vision suffers from severe effects due to selective attenuation
and scattering when light propagates through water. Such degradation not only
affects the quality of underwater images but limits the ability of vision
tasks. Different from existing methods which either ignore the wavelength
dependency of the attenuation or assume a specific spectral profile, we tackle
color distortion problem of underwater image from a new view. In this letter,
we propose a weakly supervised color transfer method to correct color
distortion, which relaxes the need of paired underwater images for training and
allows for the underwater images unknown where were taken. Inspired by
Cycle-Consistent Adversarial Networks, we design a multi-term loss function
including adversarial loss, cycle consistency loss, and SSIM (Structural
Similarity Index Measure) loss, which allows the content and structure of the
corrected result the same as the input, but the color as if the image was taken
without the water. Experiments on underwater images captured under diverse
scenes show that our method produces visually pleasing results, even
outperforms the art-of-the-state methods. Besides, our method can improve the
performance of vision tasks.
Restricted Deformable Convolution based Road Scene Semantic Segmentation Using Surround View Cameras
Authors: Liuyuan Deng, Ming Yang, Hao Li, Tianyi Li, Bing Hu, Chunxiang Wang
Date: 2018-01-02 00:00:00
Tasks: Autonomous Driving, Multi-Task Learning, Segmentation, Semantic Segmentation

Understanding the surrounding environment of the vehicle is still one of the challenges for autonomous driving. This paper addresses 360-degree road scene semantic segmentation using surround view cameras, which are widely equipped in existing production cars. First, in order to address large distortion problem in the fisheye images, Restricted Deformable Convolution (RDC) is proposed for semantic segmentation, which can effectively model geometric transformations by learning the shapes of convolutional filters conditioned on the input feature map. Second, in order to obtain a large-scale training set of surround view images, a novel method called zoom augmentation is proposed to transform conventional images to fisheye images. Finally, an RDC based semantic segmentation model is built; the model is trained for real-world surround view images through a multi-task learning architecture by combining real-world images with transformed images. Experiments demonstrate the effectiveness of the RDC to handle images with large distortions, and that the proposed approach shows a good performance using surround view cameras with the help of the transformed images.
Topological Tracking of Connected Components in Image Sequences
Authors: Rocio Gonzalez-Diaz, Maria-Jose Jimenez, Belen Medrano
Date: 2018-01-03 00:00:00
Tasks: valid

Persistent homology provides information about the lifetime of homology
classes along a filtration of cell complexes. Persistence barcode is a
graphical representation of such information. A filtration might be determined
by time in a set of spatiotemporal data, but classical methods for computing
persistent homology do not respect the fact that we can not move backwards in
time. In this paper, taking as input a time-varying sequence of two-dimensional
(2D) binary digital images, we develop an algorithm for encoding, in the
so-called {\it spatiotemporal barcode}, lifetime of connected components (of
either the foreground or background) that are moving in the image sequence over
time (this information may not coincide with the one provided by the
persistence barcode). This way, given a connected component at a specific time
in the sequence, we can track the component backwards in time until the moment
it was born, by what we call a {\it spatiotemporal path}. The main contribution
of this paper with respect to our previous works lies in a new algorithm that
computes spatiotemporal paths directly, valid for both foreground and
background and developed in a general context, setting the ground for a future
extension for tracking higher dimensional topological features in $nD$ binary
digital image sequences.