pwc-archive/papers-with-abstracts huggingface dataset:
OSTSC: Over Sampling for Time Series Classification in R
Authors: Matthew Dixon, Diego Klabjan, Lan Wei
Date: 2017-11-27 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis, Time Series Classification

The OSTSC package is a powerful oversampling approach for classifying
univariant, but multinomial time series data in R. This article provides a
brief overview of the oversampling methodology implemented by the package. A
tutorial of the OSTSC package is provided. We begin by providing three test
cases for the user to quickly validate the functionality in the package. To
demonstrate the performance impact of OSTSC, we then provide two medium size
imbalanced time series datasets. Each example applies a TensorFlow
implementation of a Long Short-Term Memory (LSTM) classifier - a type of a
Recurrent Neural Network (RNN) classifier - to imbalanced time series. The
classifier performance is compared with and without oversampling. Finally,
larger versions of these two datasets are evaluated to demonstrate the
scalability of the package. The examples demonstrate that the OSTSC package
improves the performance of RNN classifiers applied to highly imbalanced time
series data. In particular, OSTSC is observed to increase the AUC of LSTM from
0.543 to 0.784 on a high frequency trading dataset consisting of 30,000 time
series observations.
Receptive Field Block Net for Accurate and Fast Object Detection
Authors: Songtao Liu, Di Huang, Yunhong Wang
Date: 2017-11-21 00:00:00
Proceeding: ECCV 2018 9
Tasks: object-detection, Object Detection, Real-Time Object Detection

Current top-performing object detectors depend on deep CNN backbones, such as
ResNet-101 and Inception, benefiting from their powerful feature
representations but suffering from high computational costs. Conversely, some
lightweight model based detectors fulfil real time processing, while their
accuracies are often criticized. In this paper, we explore an alternative to
build a fast and accurate detector by strengthening lightweight features using
a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs)
in human visual systems, we propose a novel RF Block (RFB) module, which takes
the relationship between the size and eccentricity of RFs into account, to
enhance the feature discriminability and robustness. We further assemble RFB to
the top of SSD, constructing the RFB Net detector. To evaluate its
effectiveness, experiments are conducted on two major benchmarks and the
results show that RFB Net is able to reach the performance of advanced very
deep detectors while keeping the real-time speed. Code is available at
https://github.com/ruinmessi/RFBNet.
Hierarchical Spatial-aware Siamese Network for Thermal Infrared Object Tracking
Authors: Xin Li, Qiao Liu, Nana Fan, Zhenyu He, Hongzhi Wang
Date: 2017-11-27 00:00:00
Tasks: General Classification, Object Tracking, Thermal Infrared Object Tracking

Most thermal infrared (TIR) tracking methods are discriminative, treating the
tracking problem as a classification task. However, the objective of the
classifier (label prediction) is not coupled to the objective of the tracker
(location estimation). The classification task focuses on the between-class
difference of the arbitrary objects, while the tracking task mainly deals with
the within-class difference of the same objects. In this paper, we cast the TIR
tracking problem as a similarity verification task, which is coupled well to
the objective of the tracking task. We propose a TIR tracker via a Hierarchical
Spatial-aware Siamese Convolutional Neural Network (CNN), named HSSNet. To
obtain both spatial and semantic features of the TIR object, we design a
Siamese CNN that coalesces the multiple hierarchical convolutional layers.
Then, we propose a spatial-aware network to enhance the discriminative ability
of the coalesced hierarchical feature. Subsequently, we train this network end
to end on a large visible video detection dataset to learn the similarity
between paired objects before we transfer the network into the TIR domain.
Next, this pre-trained Siamese network is used to evaluate the similarity
between the target template and target candidates. Finally, we locate the
candidate that is most similar to the tracked target. Extensive experimental
results on the benchmarks VOT-TIR 2015 and VOT-TIR 2016 show that our proposed
method achieves favourable performance compared to the state-of-the-art
methods.
Pulsar Candidate Identification with Artificial Intelligence Techniques
Authors: Ping Guo, Fuqing Duan, Pei Wang, Yao Yao, Qian Yin, Xin Xin
Date: 2017-11-27 00:00:00
Tasks: Astronomy, Generative Adversarial Network

Discovering pulsars is a significant and meaningful research topic in the field of radio astronomy. With the advent of astronomical instruments such as he Five-hundred-meter Aperture Spherical Telescope (FAST) in China, data volumes and data rates are exponentially growing. This fact necessitates a focus on artificial intelligence (AI) technologies that can perform the automatic pulsar candidate identification to mine large astronomical data sets. Automatic pulsar candidate identification can be considered as a task of determining potential candidates for further investigation and eliminating noises of radio frequency interferences or other non-pulsar signals. It is very hard to raise the performance of DCNN-based pulsar identification because the limited training samples restrict network structure to be designed deep enough for learning good features as well as the crucial class imbalance problem due to very limited number of real pulsar samples. To address these problems, we proposed a framework which combines deep convolution generative adversarial network (DCGAN) with support vector machine (SVM) to deal with imbalance class problem and to improve pulsar identification accuracy. DCGAN is used as sample generation and feature learning model, and SVM is adopted as the classifier for predicting candidate's labels in the inference stage. The proposed framework is a novel technique which not only can solve imbalance class problem but also can learn discriminative feature representations of pulsar candidates instead of computing hand-crafted features in preprocessing steps too, which makes it more accurate for automatic pulsar candidate selection. Experiments on two pulsar datasets verify the effectiveness and efficiency of our proposed method.
Neural Text Generation: A Practical Guide
Authors: Ziang Xie
Date: 2017-11-27 00:00:00
Tasks: Decoder, Machine Translation, Response Generation, Text Generation, Translation

Deep learning methods have recently achieved great empirical success on
machine translation, dialogue response generation, summarization, and other
text generation tasks. At a high level, the technique has been to train
end-to-end neural network models consisting of an encoder model to produce a
hidden representation of the source text, followed by a decoder model to
generate the target. While such models have significantly fewer pieces than
earlier systems, significant tuning is still required to achieve good
performance. For text generation models in particular, the decoder can behave
in undesired ways, such as by generating truncated or repetitive outputs,
outputting bland and generic responses, or in some cases producing
ungrammatical gibberish. This paper is intended as a practical guide for
resolving such undesired behavior in text generation models, with the aim of
helping enable real-world applications.
Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams
Authors: Daesik Kim, Youngjoon Yoo, Jeesoo Kim, Sangkuk Lee, Nojun Kwak
Date: 2017-11-27 00:00:00
Proceeding: CVPR 2018 6
Tasks: Graph Generation, Question Answering

In this work, we introduce a new algorithm for analyzing a diagram, which
contains visual and textual information in an abstract and integrated way.
Whereas diagrams contain richer information compared with individual
image-based or language-based data, proper solutions for automatically
understanding them have not been proposed due to their innate characteristics
of multi-modality and arbitrariness of layouts. To tackle this problem, we
propose a unified diagram-parsing network for generating knowledge from
diagrams based on an object detector and a recurrent neural network designed
for a graphical structure. Specifically, we propose a dynamic graph-generation
network that is based on dynamic memory and graph theory. We explore the
dynamics of information in a diagram with activation of gates in gated
recurrent unit (GRU) cells. On publicly available diagram datasets, our model
demonstrates a state-of-the-art result that outperforms other baselines.
Moreover, further experiments on question answering shows potentials of the
proposed method for various applications.
Resilience: A Criterion for Learning in the Presence of Arbitrary Outliers
Authors: Jacob Steinhardt, Moses Charikar, Gregory Valiant
Date: 2017-03-15 00:00:00

We introduce a criterion, resilience, which allows properties of a dataset
(such as its mean or best low rank approximation) to be robustly computed, even
in the presence of a large fraction of arbitrary additional data. Resilience is
a weaker condition than most other properties considered so far in the
literature, and yet enables robust estimation in a broader variety of settings.
We provide new information-theoretic results on robust distribution learning,
robust estimation of stochastic block models, and robust mean estimation under
bounded $k$th moments. We also provide new algorithmic results on robust
distribution learning, as well as robust mean estimation in $\ell_p$-norms.
Among our proof techniques is a method for pruning a high-dimensional
distribution with bounded $1$st moments to a stable "core" with bounded $2$nd
moments, which may be of independent interest.
Pixel Deconvolutional Networks
Authors: Hongyang Gao, Hao Yuan, Zhengyang Wang, Shuiwang Ji
Date: 2017-05-18 00:00:00
Proceeding: ICLR 2018 1
Tasks: Decoder, Image Generation, Segmentation, Semantic Segmentation

Deconvolutional layers have been widely used in a variety of deep models for
up-sampling, including encoder-decoder networks for semantic segmentation and
deep generative models for unsupervised learning. One of the key limitations of
deconvolutional operations is that they result in the so-called checkerboard
problem. This is caused by the fact that no direct relationship exists among
adjacent pixels on the output feature map. To address this problem, we propose
the pixel deconvolutional layer (PixelDCL) to establish direct relationships
among adjacent pixels on the up-sampled feature map. Our method is based on a
fresh interpretation of the regular deconvolution operation. The resulting
PixelDCL can be used to replace any deconvolutional layer in a plug-and-play
manner without compromising the fully trainable capabilities of original
models. The proposed PixelDCL may result in slight decrease in efficiency, but
this can be overcome by an implementation trick. Experimental results on
semantic segmentation demonstrate that PixelDCL can consider spatial features
such as edges and shapes and yields more accurate segmentation outputs than
deconvolutional layers. When used in image generation tasks, our PixelDCL can
largely overcome the checkerboard problem suffered by regular deconvolution
operations.
DeepDeblur: Fast one-step blurry face images restoration
Authors: Lingxiao Wang, Ya-Li Li, Shengjin Wang
Date: 2017-11-27 00:00:00
Tasks: Deblurring, Face Recognition

We propose a very fast and effective one-step restoring method for blurry
face images. In the last decades, many blind deblurring algorithms have been
proposed to restore latent sharp images. However, these algorithms run slowly
because of involving two steps: kernel estimation and following non-blind
deconvolution or latent image estimation. Also they cannot handle face images
in small size. Our proposed method restores sharp face images directly in one
step using Convolutional Neural Network. Unlike previous deep learning involved
methods that can only handle a single blur kernel at one time, our network is
trained on totally random and numerous training sample pairs to deal with the
variances due to different blur kernels in practice. A smoothness
regularization as well as a facial regularization are added to keep facial
identity information which is the key to face image applications. Comprehensive
experiments demonstrate that our proposed method can handle various blur kenels
and achieve state-of-the-art results for small size blurry face images
restoration. Moreover, the proposed method shows significant improvement in
face recognition accuracy along with increasing running speed by more than 100
times.
Structure propagation for zero-shot learning
Authors: Guangfeng Lin, Yajun Chen, Fan Zhao
Date: 2017-11-27 00:00:00
Tasks: Zero-Shot Learning

The key of zero-shot learning (ZSL) is how to find the information transfer
model for bridging the gap between images and semantic information (texts or
attributes). Existing ZSL methods usually construct the compatibility function
between images and class labels with the consideration of the relevance on the
semantic classes (the manifold structure of semantic classes). However, the
relationship of image classes (the manifold structure of image classes) is also
very important for the compatibility model construction. It is difficult to
capture the relationship among image classes due to unseen classes, so that the
manifold structure of image classes often is ignored in ZSL. To complement each
other between the manifold structure of image classes and that of semantic
classes information, we propose structure propagation (SP) for improving the
performance of ZSL for classification. SP can jointly consider the manifold
structure of image classes and that of semantic classes for approximating to
the intrinsic structure of object classes. Moreover, the SP can describe the
constrain condition between the compatibility function and these manifold
structures for balancing the influence of the structure propagation iteration.
The SP solution provides not only unseen class labels but also the relationship
of two manifold structures that encode the positive transfer in structure
propagation. Experimental results demonstrate that SP can attain the promising
results on the AwA, CUB, Dogs and SUN databases.
Discriminative Learning of Open-Vocabulary Object Retrieval and Localization by Negative Phrase Augmentation
Authors: Ryota Hinami, Shin'ichi Satoh
Date: 2017-11-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Object, object-detection, Object Detection, Retrieval

Thanks to the success of object detection technology, we can retrieve objects
of the specified classes even from huge image collections. However, the current
state-of-the-art object detectors (such as Faster R-CNN) can only handle
pre-specified classes. In addition, large amounts of positive and negative
visual samples are required for training. In this paper, we address the problem
of open-vocabulary object retrieval and localization, where the target object
is specified by a textual query (e.g., a word or phrase). We first propose
Query-Adaptive R-CNN, a simple extension of Faster R-CNN adapted to
open-vocabulary queries, by transforming the text embedding vector into an
object classifier and localization regressor. Then, for discriminative
training, we then propose negative phrase augmentation (NPA) to mine hard
negative samples which are visually similar to the query and at the same time
semantically mutually exclusive of the query. The proposed method can retrieve
and localize objects specified by a textual query from one million images in
only 0.5 seconds with high precision.
Depth Map Completion by Jointly Exploiting Blurry Color Images and Sparse Depth Maps
Authors: Liyuan Pan, Yuchao Dai, Miaomiao Liu, Fatih Porikli
Date: 2017-11-27 00:00:00

We aim at predicting a complete and high-resolution depth map from
incomplete, sparse and noisy depth measurements. Existing methods handle this
problem either by exploiting various regularizations on the depth maps directly
or resorting to learning based methods. When the corresponding color images are
available, the correlation between the depth maps and the color images are used
to improve the completion performance, assuming the color images are clean and
sharp. However, in real world dynamic scenes, color images are often blurry due
to the camera motion and the moving objects in the scene. In this paper, we
propose to tackle the problem of depth map completion by jointly exploiting the
blurry color image sequences and the sparse depth map measurements, and present
an energy minimization based formulation to simultaneously complete the depth
maps, estimate the scene flow and deblur the color images. Our experimental
evaluations on both outdoor and indoor scenarios demonstrate the
state-of-the-art performance of our approach.
Illuminant Spectra-based Source Separation Using Flash Photography
Authors: Zhuo Hui, Kalyan Sunkavalli, Sunil Hadap, Aswin C. Sankaranarayanan
Date: 2017-04-19 00:00:00
Proceeding: CVPR 2018 6

Real-world lighting often consists of multiple illuminants with different
spectra. Separating and manipulating these illuminants in post-process is a
challenging problem that requires either significant manual input or calibrated
scene geometry and lighting. In this work, we leverage a flash/no-flash image
pair to analyze and edit scene illuminants based on their spectral differences.
We derive a novel physics-based relationship between color variations in the
observed flash/no-flash intensities and the spectra and surface shading
corresponding to individual scene illuminants. Our technique uses this
constraint to automatically separate an image into constituent images lit by
each illuminant. This separation can be used to support applications like white
balancing, lighting editing, and RGB photometric stereo, where we demonstrate
results that outperform state-of-the-art techniques on a wide range of images.
SkipNet: Learning Dynamic Routing in Convolutional Networks
Authors: Xin Wang, Fisher Yu, Zi-Yi Dou, Trevor Darrell, Joseph E. Gonzalez
Date: 2017-11-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Decision Making, Reinforcement Learning, Sequential Decision Making

While deeper convolutional networks are needed to achieve maximum accuracy in
visual perception tasks, for many inputs shallower networks are sufficient. We
exploit this observation by learning to skip convolutional layers on a
per-input basis. We introduce SkipNet, a modified residual network, that uses a
gating network to selectively skip convolutional blocks based on the
activations of the previous layer. We formulate the dynamic skipping problem in
the context of sequential decision making and propose a hybrid learning
algorithm that combines supervised learning and reinforcement learning to
address the challenges of non-differentiable skipping decisions. We show
SkipNet reduces computation by 30-90% while preserving the accuracy of the
original model on four benchmark datasets and outperforms the state-of-the-art
dynamic networks and static compression methods. We also qualitatively evaluate
the gating policy to reveal a relationship between image scale and saliency and
the number of layers skipped.
STAR-RT: Visual attention for real-time video game playing
Authors: Iuliia Kotseruba, John K. Tsotsos
Date: 2017-11-26 00:00:00

In this paper we present STAR-RT - the first working prototype of Selective
Tuning Attention Reference (STAR) model and Cognitive Programs (CPs). The
Selective Tuning (ST) model received substantial support through psychological
and neurophysiological experiments. The STAR framework expands ST and applies
it to practical visual tasks. In order to do so, similarly to many cognitive
architectures, STAR combines the visual hierarchy (based on ST) with the
executive controller, working and short-term memory components and fixation
controller. CPs in turn enable the communication among all these elements for
visual task execution. To test the relevance of the system in a realistic
context, we implemented the necessary components of STAR and designed CPs for
playing two closed-source video games - Canabaltand Robot Unicorn Attack. Since
both games run in a browser window, our algorithm has the same amount of
information and the same amount of time to react to the events on the screen as
a human player would. STAR-RT plays both games in real time using only visual
input and achieves scores comparable to human expert players. It thus provides
an existence proof for the utility of the particular CP structure and
primitives used and the potential for continued experimentation and
verification of their utility in broader scenarios.
Sample-Efficient Algorithms for Recovering Structured Signals from Magnitude-Only Measurements
Authors: Gauri Jagatap, Chinmay Hegde
Date: 2017-05-18 00:00:00
Tasks: Retrieval

We consider the problem of recovering a signal $\mathbf{x}^* \in
\mathbf{R}^n$, from magnitude-only measurements $y_i =
|\left\langle\mathbf{a}_i,\mathbf{x}^*\right\rangle|$ for $i=[m]$. Also called
the phase retrieval, this is a fundamental challenge in bio-,astronomical
imaging and speech processing. The problem above is ill-posed; additional
assumptions on the signal and/or the measurements are necessary. In this paper
we first study the case where the signal $\mathbf{x}^*$ is $s$-sparse. We
develop a novel algorithm that we call Compressive Phase Retrieval with
Alternating Minimization, or CoPRAM. Our algorithm is simple; it combines the
classical alternating minimization approach for phase retrieval with the CoSaMP
algorithm for sparse recovery. Despite its simplicity, we prove that CoPRAM
achieves a sample complexity of $O(s^2\log n)$ with Gaussian measurements
$\mathbf{a}_i$, matching the best known existing results; moreover, it
demonstrates linear convergence in theory and practice. Additionally, it
requires no extra tuning parameters other than signal sparsity $s$ and is
robust to noise. When the sorted coefficients of the sparse signal exhibit a
power law decay, we show that CoPRAM achieves a sample complexity of $O(s\log
n)$, which is close to the information-theoretic limit. We also consider the
case where the signal $\mathbf{x}^*$ arises from structured sparsity models. We
specifically examine the case of block-sparse signals with uniform block size
of $b$ and block sparsity $k=s/b$. For this problem, we design a recovery
algorithm Block CoPRAM that further reduces the sample complexity to $O(ks\log
n)$. For sufficiently large block lengths of $b=\Theta(s)$, this bound equates
to $O(s\log n)$. To our knowledge, this constitutes the first end-to-end
algorithm for phase retrieval where the Gaussian sample complexity has a
sub-quadratic dependence on the signal sparsity level.