pwc-archive/papers-with-abstracts huggingface dataset:
A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes
Authors: Lichao Mou, Yuansheng Hua, Xiao Xiang Zhu
Date: 2019-04-11 00:00:00
Proceeding: CVPR 2019 6
Tasks: Relation, Relational Reasoning, Semantic Segmentation

Most current semantic segmentation approaches fall back on deep convolutional neural networks (CNNs). However, their use of convolution operations with local receptive fields causes failures in modeling contextual spatial relations. Prior works have sought to address this issue by using graphical models or spatial propagation modules in networks. But such models often fail to capture long-range spatial relationships between entities, which leads to spatially fragmented predictions. Moreover, recent works have demonstrated that channel-wise information also acts a pivotal part in CNNs. In this work, we introduce two simple yet effective network units, the spatial relation module and the channel relation module, to learn and reason about global relationships between any two spatial positions or feature maps, and then produce relation-augmented feature representations. The spatial and channel relation modules are general and extensible, and can be used in a plug-and-play fashion with the existing fully convolutional network (FCN) framework. We evaluate relation module-equipped networks on semantic segmentation tasks using two aerial image datasets, which fundamentally depend on long-range spatial relational reasoning. The networks achieve very competitive results, bringing significant improvements over baselines.
FTGAN: A Fully-trained Generative Adversarial Networks for Text to Face Generation
Authors: Xiang Chen, Lingbo Qing, Xiaohai He, Xiaodong Luo, Yining Xu
Date: 2019-04-11 00:00:00
Tasks: Decoder, Face Generation, Generative Adversarial Network, Image Generation, Text-to-Face Generation

As a sub-domain of text-to-image synthesis, text-to-face generation has huge
potentials in public safety domain. With lack of dataset, there are almost no
related research focusing on text-to-face synthesis. In this paper, we propose
a fully-trained Generative Adversarial Network (FTGAN) that trains the text
encoder and image decoder at the same time for fine-grained text-to-face
generation. With a novel fully-trained generative network, FTGAN can synthesize
higher-quality images and urge the outputs of the FTGAN are more relevant to
the input sentences. In addition, we build a dataset called SCU-Text2face for
text-to-face synthesis. Through extensive experiments, the FTGAN shows its
superiority in boosting both generated images' quality and similarity to the
input descriptions. The proposed FTGAN outperforms the previous state of the
art, boosting the best reported Inception Score to 4.63 on the CUB dataset. On
SCU-text2face, the face images generated by our proposed FTGAN just based on
the input descriptions is of average 59% similarity to the ground-truth, which
set a baseline for text-to-face synthesis.
Plug-and-Play: Improve Depth Estimation via Sparse Data Propagation
Authors: Tsun-Hsuan Wang, Fu-En Wang, Juan-Ting Lin, Yi-Hsuan Tsai, Wei-Chen Chiu, Min Sun
Date: 2018-12-20 00:00:00
Tasks: Depth Estimation, Depth Prediction

We propose a novel plug-and-play (PnP) module for improving depth prediction
with taking arbitrary patterns of sparse depths as input. Given any pre-trained
depth prediction model, our PnP module updates the intermediate feature map
such that the model outputs new depths consistent with the given sparse depths.
Our method requires no additional training and can be applied to practical
applications such as leveraging both RGB and sparse LiDAR points to robustly
estimate dense depth map. Our approach achieves consistent improvements on
various state-of-the-art methods on indoor (i.e., NYU-v2) and outdoor (i.e.,
KITTI) datasets. Various types of LiDARs are also synthesized in our
experiments to verify the general applicability of our PnP module in practice.
For project page, see https://zswang666.github.io/PnP-Depth-Project-Page/
Reconstructing Network Inputs with Additive Perturbation Signatures
Authors: Nick Moran, Chiraag Juvekar
Date: 2019-04-11 00:00:00

In this work, we present preliminary results demonstrating the ability to
recover a significant amount of information about secret model inputs given
only very limited access to model outputs and the ability evaluate the model on
additive perturbations to the input.
Elucidating image-to-set prediction: An analysis of models, losses and datasets
Authors: Luis Pineda, Amaia Salvador, Michal Drozdzal, Adriana Romero
Date: 2019-04-11 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Prediction

In this paper, we identify an important reproducibility challenge in the image-to-set prediction literature that impedes proper comparisons among published methods, namely, researchers use different evaluation protocols to assess their contributions. To alleviate this issue, we introduce an image-to-set prediction benchmark suite built on top of five public datasets of increasing task complexity that are suitable for multi-label classification (VOC, COCO, NUS-WIDE, ADE20k and Recipe1M). Using the benchmark, we provide an in-depth analysis where we study the key components of current models, namely the choice of the image representation backbone as well as the set predictor design. Our results show that (1) exploiting better image representation backbones leads to higher performance boosts than enhancing set predictors, and (2) modeling both the label co-occurrences and ordering has a slight positive impact in terms of performance, whereas explicit cardinality prediction only helps when training on complex datasets, such as Recipe1M. To facilitate future image-to-set prediction research, we make the code, best models and dataset splits publicly available at: https://github.com/facebookresearch/image-to-set.
Multiplicative Up-Drift
Authors: Benjamin Doerr, Timo Kötzing
Date: 2019-04-11 00:00:00
Tasks: Evolutionary Algorithms

Drift analysis aims at translating the expected progress of an evolutionary algorithm (or more generally, a random process) into a probabilistic guarantee on its run time (hitting time). So far, drift arguments have been successfully employed in the rigorous analysis of evolutionary algorithms, however, only for the situation that the progress is constant or becomes weaker when approaching the target. Motivated by questions like how fast fit individuals take over a population, we analyze random processes exhibiting a $(1+\delta)$-multiplicative growth in expectation. We prove a drift theorem translating this expected progress into a hitting time. This drift theorem gives a simple and insightful proof of the level-based theorem first proposed by Lehre (2011). Our version of this theorem has, for the first time, the best-possible near-linear dependence on $1/\delta$ (the previous results had an at least near-quadratic dependence), and it only requires a population size near-linear in $\delta$ (this was super-quadratic in previous results). These improvements immediately lead to stronger run time guarantees for a number of applications. We also discuss the case of large $\delta$ and show stronger results for this setting.
Cross-topic distributional semantic representations via unsupervised mappings
Authors: Eleftheria Briakou, Nikos Athanasiou, Alexandros Potamianos
Date: 2019-04-11 00:00:00
Proceeding: NAACL 2019 6
Tasks: Word Similarity

In traditional Distributional Semantic Models (DSMs) the multiple senses of a
polysemous word are conflated into a single vector space representation. In
this work, we propose a DSM that learns multiple distributional representations
of a word based on different topics. First, a separate DSM is trained for each
topic and then each of the topic-based DSMs is aligned to a common vector
space. Our unsupervised mapping approach is motivated by the hypothesis that
words preserving their relative distances in different topic semantic
sub-spaces constitute robust \textit{semantic anchors} that define the mappings
between them. Aligned cross-topic representations achieve state-of-the-art
results for the task of contextual word similarity. Furthermore, evaluation on
NLP downstream tasks shows that multiple topic-based embeddings outperform
single-prototype models.
YUVMultiNet: Real-time YUV multi-task CNN for autonomous driving
Authors: Thomas Boulay, Said El-Hachimi, Mani Kumar Surisetti, Pullarao Maddu, Saranya Kandan
Date: 2019-04-11 00:00:00
Tasks: Autonomous Driving, Quantization, Segmentation

In this paper, we propose a multi-task convolutional neural network (CNN)
architecture optimized for a low power automotive grade SoC. We introduce a
network based on a unified architecture where the encoder is shared among the
two tasks namely detection and segmentation. The pro-posed network runs at
25FPS for 1280x800 resolution. We briefly discuss the methods used to optimize
the network architecture such as using native YUV image directly, optimization
of layers & feature maps and applying quantization. We also focus on memory
bandwidth in our design as convolutions are data intensives and most SOCs are
bandwidth bottlenecked. We then demonstrate the efficiency of our proposed
network for a dedicated CNN accelerators presenting the key performance
indicators (KPI) for the detection and segmentation tasks obtained from the
hardware execution and the corresponding run-time.
A machine learning approach for underwater gas leakage detection
Authors: Paulo Hubert, Linilson Padovese
Date: 2019-04-11 00:00:00
Tasks: BIG-bench Machine Learning

Underwater gas reservoirs are used in many situations. In particular, Carbon
Capture and Storage (CCS) facilities that are currently being developed intend
to store greenhouse gases inside geological formations in the deep sea. In
these formations, however, the gas might percolate, leaking back to the water
and eventually to the atmosphere. The early detection of such leaks is
therefore tantamount to any underwater CCS project. In this work, we propose to
use Passive Acoustic Monitoring (PAM) and a machine learning approach to design
efficient detectors that can signal the presence of a leakage. We use data
obtained from simulation experiments off the Brazilian shore, and show that the
detection based on classification algorithms achieve good performance. We also
propose a smoothing strategy based on Hidden Markov Models in order to
incorporate previous knowledge about the probabilities of leakage occurrences.
MxML: Mixture of Meta-Learners for Few-Shot Classification
Authors: Minseop Park, Jungtaek Kim, Saehoon Kim, Yanbin Liu, Seungjin Choi
Date: 2019-04-11 00:00:00
Tasks: Classification, General Classification, Meta-Learning

A meta-model is trained on a distribution of similar tasks such that it
learns an algorithm that can quickly adapt to a novel task with only a handful
of labeled examples. Most of current meta-learning methods assume that the
meta-training set consists of relevant tasks sampled from a single
distribution. In practice, however, a new task is often out of the task
distribution, yielding a performance degradation. One way to tackle this
problem is to construct an ensemble of meta-learners such that each
meta-learner is trained on different task distribution. In this paper we
present a method for constructing a mixture of meta-learners (MxML), where
mixing parameters are determined by the weight prediction network (WPN)
optimized to improve the few-shot classification performance. Experiments on
various datasets demonstrate that MxML significantly outperforms
state-of-the-art meta-learners, or their naive ensemble in the case of
out-of-distribution as well as in-distribution tasks.
Deep learning as optimal control problems: models and numerical methods
Authors: Martin Benning, Elena Celledoni, Matthias J. Ehrhardt, Brynjulf Owren, Carola-Bibiane Schönlieb
Date: 2019-04-11 00:00:00
Tasks: Deep Learning

We consider recent work of Haber and Ruthotto 2017 and Chang et al. 2018, where deep learning neural networks have been interpreted as discretisations of an optimal control problem subject to an ordinary differential equation constraint. We review the first order conditions for optimality, and the conditions ensuring optimality after discretisation. This leads to a class of algorithms for solving the discrete optimal control problem which guarantee that the corresponding discrete necessary conditions for optimality are fulfilled. The differential equation setting lends itself to learning additional parameters such as the time discretisation. We explore this extension alongside natural constraints (e.g. time steps lie in a simplex). We compare these deep learning algorithms numerically in terms of induced flow and generalisation ability.
Software Based Higher Order Structural Foot Abnormality Detection Using Image Processing
Authors: Arnesh Sen, Kaustav Sen, Jayoti Das
Date: 2019-04-11 00:00:00
Tasks: Anomaly Detection, General Classification

The entire movement of human body undergoes through a periodic process named
Gait Cycle. The structure of human foot is the key element to complete the
cycle successfully. Abnormality of this foot structure is an alarming form of
congenital disorder which results a classification based on the geometry of the
human foot print image. Image processing is one of the most efficient way to
determine a number of footprint parameter to detect the severeness of disorder.
This paper aims to detect the Flatfoot and High Arch foot abnormalities using
one of the footprint parameters named Modified Brucken Index by biomedical
image processing.
C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
Authors: Fang Wan, Chang Liu, Wei Ke, Xiangyang Ji, Jianbin Jiao, Qixiang Ye
Date: 2019-04-11 00:00:00
Proceeding: CVPR 2019 6
Tasks: Multiple Instance Learning, Object, object-detection, Object Detection, Object Localization, Weakly Supervised Object Detection, Weakly-Supervised Object Localization

Weakly supervised object detection (WSOD) is a challenging task when provided
with image category supervision but required to simultaneously learn object
locations and object detectors. Many WSOD approaches adopt multiple instance
learning (MIL) and have non-convex loss functions which are prone to get stuck
into local minima (falsely localize object parts) while missing full object
extent during training. In this paper, we introduce a continuation optimization
method into MIL and thereby creating continuation multiple instance learning
(C-MIL), with the intention of alleviating the non-convexity problem in a
systematic way. We partition instances into spatially related and class related
subsets, and approximate the original loss function with a series of smoothed
loss functions defined within the subsets. Optimizing smoothed loss functions
prevents the training procedure falling prematurely into local minima and
facilitates the discovery of Stable Semantic Extremal Regions (SSERs) which
indicate full object extent. On the PASCAL VOC 2007 and 2012 datasets, C-MIL
improves the state-of-the-art of weakly supervised object detection and weakly
supervised object localization with large margins.
Retinal Vessels Segmentation Based on Dilated Multi-Scale Convolutional Neural Network
Authors: Yun Jiang, Ning Tan, Tingting Peng, Hai Zhang
Date: 2019-04-11 00:00:00
Tasks: Specificity

Accurate segmentation of retinal vessels is a basic step in Diabetic
retinopathy(DR) detection. Most methods based on deep convolutional neural
network (DCNN) have small receptive fields, and hence they are unable to
capture global context information of larger regions, with difficult to
identify lesions. The final segmented retina vessels contain more noise with
low classification accuracy. Therefore, in this paper, we propose a DCNN
structure named as D-Net. In the proposed D-Net, the dilation convolution is
used in the backbone network to obtain a larger receptive field without losing
spatial resolution, so as to reduce the loss of feature information and to
reduce the difficulty of tiny thin vessels segmentation. The large receptive
field can better distinguished between the lesion area and the blood vessel
area. In the proposed Multi-Scale Information Fusion module (MSIF), parallel
convolution layers with different dilation rates are used, so that the model
can obtain more dense feature information and better capture retinal vessel
information of different sizes. In the decoding module, the skip layer
connection is used to propagate context information to higher resolution
layers, so as to prevent low-level information from passing the entire network
structure. Finally, our method was verified on DRIVE, STARE and CHASE dataset.
The experimental results show that our network structure outperforms some
state-of-art method, such as N4-fields, U-Net, and DRIU in terms of accuracy,
sensitivity, specificity, and AUCROC. Particularly, D-Net outperforms U-Net by
1.04%, 1.23% and 2.79% in DRIVE, STARE, and CHASE three dataset, respectively.
Political Discussions in Homogeneous and Cross-Cutting Communication Spaces
Authors: Jisun An, Haewoon Kwak, Oliver Posegga, Andreas Jungherr
Date: 2019-04-11 00:00:00

Online platforms, such as Facebook, Twitter, and Reddit, provide users with a
rich set of features for sharing and consuming political information,
expressing political opinions, and exchanging potentially contrary political
views. In such activities, two types of communication spaces naturally emerge:
those dominated by exchanges between politically homogeneous users and those
that allow and encourage cross-cutting exchanges in politically heterogeneous
groups. While research on political talk in online environments abounds, we
know surprisingly little about the potentially varying nature of discussions in
politically homogeneous spaces as compared to cross-cutting communication
spaces. To fill this gap, we use Reddit to explore the nature of political
discussions in homogeneous and cross-cutting communication spaces. In
particular, we develop an analytical template to study interaction and
linguistic patterns within and between politically homogeneous and
heterogeneous communication spaces. Our analyses reveal different behavioral
patterns in homogeneous and cross-cutting communications spaces. We discuss
theoretical and practical implications in the context of research on political
talk online.
Modified online Newton step based on element wise multiplication
Authors: Charanjeet, Anuj Sharma
Date: 2019-04-11 00:00:00

The second order method as Newton Step is a suitable technique in Online
Learning to guarantee regret bound. The large data is a challenge in Newton
method to store second order matrices as hessian. In this paper, we have
proposed an modified online Newton step that store first and second order
matrices of dimension m (classes) by d (features). we have used element wise
arithmetic operation to retain matrices size same. The modified second order
matrix size results in faster computations. Also, the mistake rate is at par
with respect to popular methods in literature. The experiments outcome indicate
that proposed method could be helpful to handle large multi class datasets in
common desktop machines using second order method as Newton step.
Detecting Repeating Objects using Patch Correlation Analysis
Authors: Inbar Huberman, Raanan Fattal
Date: 2019-04-11 00:00:00
Proceeding: CVPR 2016 6
Tasks: Active Learning, Object, Specificity

In this paper we describe a new method for detecting and counting a repeating
object in an image. While the method relies on a fairly sophisticated
deformable part model, unlike existing techniques it estimates the model
parameters in an unsupervised fashion thus alleviating the need for a
user-annotated training data and avoiding the associated specificity. This
automatic fitting process is carried out by exploiting the recurrence of small
image patches associated with the repeating object and analyzing their spatial
correlation. The analysis allows us to reject outlier patches, recover the
visual and shape parameters of the part model, and detect the object instances
efficiently. In order to achieve a practical system which is able to cope with
diverse images, we describe a simple and intuitive active-learning procedure
that updates the object classification by querying the user on very few
carefully chosen marginal classifications. Evaluation of the new method against
the state-of-the-art techniques demonstrates its ability to achieve higher
accuracy through a better user experience.
Autoregressive Energy Machines
Authors: Charlie Nash, Conor Durkan
Date: 2019-04-11 00:00:00
Tasks: Density Estimation, valid

Neural density estimators are flexible families of parametric models which
have seen widespread use in unsupervised machine learning in recent years.
Maximum-likelihood training typically dictates that these models be constrained
to specify an explicit density. However, this limitation can be overcome by
instead using a neural network to specify an energy function, or unnormalized
density, which can subsequently be normalized to obtain a valid distribution.
The challenge with this approach lies in accurately estimating the normalizing
constant of the high-dimensional energy function. We propose the Autoregressive
Energy Machine, an energy-based model which simultaneously learns an
unnormalized density and computes an importance-sampling estimate of the
normalizing constant for each conditional in an autoregressive decomposition.
The Autoregressive Energy Machine achieves state-of-the-art performance on a
suite of density-estimation tasks.
A Stochastic LBFGS Algorithm for Radio Interferometric Calibration
Authors: Sarod Yatawatta, Lukas De Clercq, Hanno Spreeuw, Faruk Diblen
Date: 2019-04-11 00:00:00

We present a stochastic, limited-memory Broyden Fletcher Goldfarb Shanno
(LBFGS) algorithm that is suitable for handling very large amounts of data. A
direct application of this algorithm is radio interferometric calibration of
raw data at fine time and frequency resolution. Almost all existing radio
interferometric calibration algorithms assume that it is possible to fit the
dataset being calibrated into memory. Therefore, the raw data is averaged in
time and frequency to reduce its size by many orders of magnitude before
calibration is performed. However, this averaging is detrimental for the
detection of some signals of interest that have narrow bandwidth and time
duration such as fast radio bursts (FRBs). Using the proposed algorithm, it is
possible to calibrate data at such a fine resolution that they cannot be
entirely loaded into memory, thus preserving such signals. As an additional
demonstration, we use the proposed algorithm for training deep neural networks
and compare the performance against the mainstream first order optimization
algorithms that are used in deep learning.
Reducing Lateral Visual Biases in Displays
Authors: Inbar Huberman, Raanan Fattal
Date: 2019-04-11 00:00:00

The human visual system is composed of multiple physiological components that
apply multiple mechanisms in order to cope with the rich visual content it
encounters. The complexity of this system leads to non-trivial relations
between what we see and what we perceive, and in particular, between the raw
intensities of an image that we display and the ones we perceive where various
visual biases and illusions are introduced. In this paper we describe a method
for reducing a large class of biases related to the lateral inhibition
mechanism in the human retina where neurons suppress the activity of
neighboring receptors. Among these biases are the well-known Mach bands and
halos that appear around smooth and sharp image gradients as well as the
appearance of false contrasts between identical regions. The new method removes
these visual biases by computing an image that contains counter biases such
that when this laterally-compensated image is viewed on a display, the inserted
biases cancel the ones created in the retina.