pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Multi-Frequency Phase Unwrapping using Kernel Density Estimation
Authors: Felix Järemo Lawin, Per-Erik Forssén, Hannes Ovrén
Date: 2016-08-18 00:00:00
Tasks: Density Estimation, valid

In this paper we introduce an efficient method to unwrap multi-frequency
phase estimates for time-of-flight ranging. The algorithm generates multiple
depth hypotheses and uses a spatial kernel density estimate (KDE) to rank them.
The confidence produced by the KDE is also an effective means to detect
outliers. We also introduce a new closed-form expression for phase noise
prediction, that better fits real data. The method is applied to depth decoding
for the Kinect v2 sensor, and compared to the Microsoft Kinect SDK and to the
open source driver libfreenect2. The intended Kinect v2 use case is scenes with
less than 8m range, and for such cases we observe consistent improvements,
while maintaining real-time performance. When extending the depth range to the
maximal value of 8.75m, we get about 52% more valid measurements than
libfreenect2. The effect is that the sensor can now be used in large depth
scenes, where it was previously not a good choice. Code and supplementary
material are available at
http://www.cvl.isy.liu.se/research/datasets/kinect2-dataset.
Refining Geometry from Depth Sensors using IR Shading Images
Authors: Gyeongmin Choe, Jaesik Park, Yu-Wing Tai, In So Kweon
Date: 2016-08-18 00:00:00

We propose a method to refine geometry of 3D meshes from a consumer level
depth camera, e.g. Kinect, by exploiting shading cues captured from an infrared
(IR) camera. A major benefit to using an IR camera instead of an RGB camera is
that the IR images captured are narrow band images that filter out most
undesired ambient light, which makes our system robust against natural indoor
illumination. Moreover, for many natural objects with colorful textures in the
visible spectrum, the subjects appear to have a uniform albedo in the IR
spectrum. Based on our analyses on the IR projector light of the Kinect, we
define a near light source IR shading model that describes the captured
intensity as a function of surface normals, albedo, lighting direction, and
distance between light source and surface points. To resolve the ambiguity in
our model between the normals and distances, we utilize an initial 3D mesh from
the Kinect fusion and multi-view information to reliably estimate surface
details that were not captured and reconstructed by the Kinect fusion. Our
approach directly operates on the mesh model for geometry refinement. We ran
experiments on our algorithm for geometries captured by both the Kinect I and
Kinect II, as the depth acquisition in Kinect I is based on a structured-light
technique and that of the Kinect II is based on a time-of-flight (ToF)
technology. The effectiveness of our approach is demonstrated through several
challenging real-world examples. We have also performed a user study to
evaluate the quality of the mesh models before and after our refinements.
Seeing with Humans: Gaze-Assisted Neural Image Captioning
Authors: Yusuke Sugano, Andreas Bulling
Date: 2016-08-18 00:00:00
Tasks: Image Captioning, Object, Object Localization, Scene Recognition, Scene Understanding

Gaze reflects how humans process visual scenes and is therefore increasingly
used in computer vision systems. Previous works demonstrated the potential of
gaze for object-centric tasks, such as object localization and recognition, but
it remains unclear if gaze can also be beneficial for scene-centric tasks, such
as image captioning. We present a new perspective on gaze-assisted image
captioning by studying the interplay between human gaze and the attention
mechanism of deep neural networks. Using a public large-scale gaze dataset, we
first assess the relationship between state-of-the-art object and scene
recognition models, bottom-up visual saliency, and human gaze. We then propose
a novel split attention model for image captioning. Our model integrates human
gaze information into an attention-based long short-term memory architecture,
and allows the algorithm to allocate attention selectively to both fixated and
non-fixated image regions. Through evaluation on the COCO/SALICON datasets we
show that our method improves image captioning performance and that gaze can
complement machine attention for semantic scene understanding tasks.
Occlusion-Model Guided Anti-Occlusion Depth Estimation in Light Field
Authors: Hao Zhu, Qing Wang, Jingyi Yu
Date: 2016-08-15 00:00:00
Tasks: Depth Estimation

Occlusion is one of the most challenging problems in depth estimation.
Previous work has modeled the single-occluder occlusion in light field and get
good results, however it is still difficult to obtain accurate depth for
multi-occluder occlusion. In this paper, we explore the multi-occluder
occlusion model in light field, and derive the occluder-consistency between the
spatial and angular space which is used as a guidance to select the un-occluded
views for each candidate occlusion point. Then an anti-occlusion energy
function is built to regularize depth map. The experimental results on public
light field datasets have demonstrated the advantages of the proposed algorithm
compared with other state-of-the-art light field depth estimation algorithms,
especially in multi-occluder areas.
Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs
Authors: Youbao Tang, Xiangqian Wu
Date: 2016-08-18 00:00:00
Tasks: Prediction, Saliency Detection, Saliency Prediction

This paper proposes a novel saliency detection method by combining
region-level saliency estimation and pixel-level saliency prediction with CNNs
(denoted as CRPSD). For pixel-level saliency prediction, a fully convolutional
neural network (called pixel-level CNN) is constructed by modifying the VGGNet
architecture to perform multi-scale feature learning, based on which an
image-to-image prediction is conducted to accomplish the pixel-level saliency
detection. For region-level saliency estimation, an adaptive superpixel based
region generation technique is first designed to partition an image into
regions, based on which the region-level saliency is estimated by using a CNN
model (called region-level CNN). The pixel-level and region-level saliencies
are fused to form the final salient map by using another CNN (called fusion
CNN). And the pixel-level CNN and fusion CNN are jointly learned. Extensive
quantitative and qualitative experiments on four public benchmark datasets
demonstrate that the proposed method greatly outperforms the state-of-the-art
saliency detection approaches.
Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection
Authors: Youbao Tang, Xiangqian Wu, Wei Bu
Date: 2016-08-18 00:00:00
Tasks: Saliency Detection, Saliency Prediction

This paper proposes a novel saliency detection method by developing a
deeply-supervised recurrent convolutional neural network (DSRCNN), which
performs a full image-to-image saliency prediction. For saliency detection, the
local, global, and contextual information of salient objects is important to
obtain a high quality salient map. To achieve this goal, the DSRCNN is designed
based on VGGNet-16. Firstly, the recurrent connections are incorporated into
each convolutional layer, which can make the model more powerful for learning
the contextual information. Secondly, side-output layers are added to conduct
the deeply-supervised operation, which can make the model learn more
discriminative and robust features by effecting the intermediate layers.
Finally, all of the side-outputs are fused to integrate the local and global
information to get the final saliency detection results. Therefore, the DSRCNN
combines the advantages of recurrent convolutional neural networks and
deeply-supervised nets. The DSRCNN model is tested on five benchmark datasets,
and experimental results demonstrate that the proposed method significantly
outperforms the state-of-the-art saliency detection approaches on all test
datasets.
AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification
Authors: Gui-Song Xia, Jingwen Hu, Fan Hu, Baoguang Shi, Xiang Bai, Yanfei Zhong, Liangpei Zhang
Date: 2016-08-18 00:00:00
Tasks: Aerial Scene Classification, Classification, General Classification, Scene Classification

Aerial scene classification, which aims to automatically label an aerial
image with a specific semantic category, is a fundamental problem for
understanding high-resolution remote sensing imagery. In recent years, it has
become an active task in remote sensing area and numerous algorithms have been
proposed for this task, including many machine learning and data-driven
approaches. However, the existing datasets for aerial scene classification like
UC-Merced dataset and WHU-RS19 are with relatively small sizes, and the results
on them are already saturated. This largely limits the development of scene
classification algorithms. This paper describes the Aerial Image Dataset (AID):
a large-scale dataset for aerial scene classification. The goal of AID is to
advance the state-of-the-arts in scene classification of remote sensing images.
For creating AID, we collect and annotate more than ten thousands aerial scene
images. In addition, a comprehensive review of the existing aerial scene
classification techniques as well as recent widely-used deep learning methods
is given. Finally, we provide a performance analysis of typical aerial scene
classification and deep learning approaches on AID, which can be served as the
baseline results on this benchmark.
Every Filter Extracts A Specific Texture In Convolutional Neural Networks
Authors: Zhiqiang Xia, Ce Zhu, Zhengtao Wang, Qi Guo, Yipeng Liu
Date: 2016-08-15 00:00:00

Many works have concentrated on visualizing and understanding the inner
mechanism of convolutional neural networks (CNNs) by generating images that
activate some specific neurons, which is called deep visualization. However, it
is still unclear what the filters extract from images intuitively. In this
paper, we propose a modified code inversion algorithm, called feature map
inversion, to understand the function of filter of interest in CNNs. We reveal
that every filter extracts a specific texture. The texture from higher layer
contains more colours and more intricate structures. We also demonstrate that
style of images could be a combination of these texture primitives. Two methods
are proposed to reallocate energy distribution of feature maps randomly and
purposefully. Then, we inverse the modified code and generate images of diverse
styles. With these results, we provide an explanation about why Gram matrix of
feature maps \cite{Gatys_2016_CVPR} could represent image style.
Multi-stage Object Detection with Group Recursive Learning
Authors: Jianan Li, Xiaodan Liang, Jianshu Li, Tingfa Xu, Jiashi Feng, Shuicheng Yan
Date: 2016-08-18 00:00:00
Tasks: Object, object-detection, Object Detection, Object Proposal Generation, Segmentation, Semantic Segmentation, Weakly-Supervised Object Segmentation

Most of existing detection pipelines treat object proposals independently and
predict bounding box locations and classification scores over them separately.
However, the important semantic and spatial layout correlations among proposals
are often ignored, which are actually useful for more accurate object
detection. In this work, we propose a new EM-like group recursive learning
approach to iteratively refine object proposals by incorporating such context
of surrounding proposals and provide an optimal spatial configuration of object
detections. In addition, we propose to incorporate the weakly-supervised object
segmentation cues and region-based object detection into a multi-stage
architecture in order to fully exploit the learned segmentation features for
better object detection in an end-to-end way. The proposed architecture
consists of three cascaded networks which respectively learn to perform
weakly-supervised object segmentation, object proposal generation and recursive
detection refinement. Combining the group recursive learning and the
multi-stage architecture provides competitive mAPs of 78.6% and 74.9% on the
PASCAL VOC2007 and VOC2012 datasets respectively, which outperforms many
well-established baselines [10] [20] significantly.
Enabling Factor Analysis on Thousand-Subject Neuroimaging Datasets
Authors: Michael J. Anderson, Mihai Capotă, Javier S. Turek, Xia Zhu, Theodore L. Willke, Yida Wang, Po-Hsuan Chen, Jeremy R. Manning, Peter J. Ramadge, Kenneth A. Norman
Date: 2016-08-16 00:00:00

The scale of functional magnetic resonance image data is rapidly increasing
as large multi-subject datasets are becoming widely available and
high-resolution scanners are adopted. The inherent low-dimensionality of the
information in this data has led neuroscientists to consider factor analysis
methods to extract and analyze the underlying brain activity. In this work, we
consider two recent multi-subject factor analysis methods: the Shared Response
Model and Hierarchical Topographic Factor Analysis. We perform analytical,
algorithmic, and code optimization to enable multi-node parallel
implementations to scale. Single-node improvements result in 99x and 1812x
speedups on these two methods, and enables the processing of larger datasets.
Our distributed implementations show strong scaling of 3.3x and 5.5x
respectively with 20 nodes on real datasets. We also demonstrate weak scaling
on a synthetic dataset with 1024 subjects, on up to 1024 nodes and 32,768
cores.
Conditional Sparse Linear Regression
Authors: Brendan Juba
Date: 2016-08-18 00:00:00
Tasks: regression

Machine learning and statistics typically focus on building models that
capture the vast majority of the data, possibly ignoring a small subset of data
as "noise" or "outliers." By contrast, here we consider the problem of jointly
identifying a significant (but perhaps small) segment of a population in which
there is a highly sparse linear regression fit, together with the coefficients
for the linear fit. We contend that such tasks are of interest both because the
models themselves may be able to achieve better predictions in such special
cases, but also because they may aid our understanding of the data. We give
algorithms for such problems under the sup norm, when this unknown segment of
the population is described by a k-DNF condition and the regression fit is
s-sparse for constant k and s. For the variants of this problem when the
regression fit is not so sparse or using expected error, we also give a
preliminary algorithm and highlight the question as a challenge for future
work.
Text Line Identification in Tagore's Manuscript
Authors: Chandranath Adak, Bidyut. B. Chaudhuri
Date: 2014-08-29 00:00:00

In this paper, a text line identification method is proposed. The text lines
of printed document are easy to segment due to uniform straightness of the
lines and sufficient gap between the lines. But in handwritten documents, the
line is non-uniform and interline gaps are variable. We take Rabindranath
Tagore's manuscript as it is one of the most difficult manuscripts that contain
doodles. Our method consists of a pre-processing stage to clean the document
image. Then we separate doodles from the manuscript to get the textual region.
After that we identify the text lines on the manuscript. For text line
identification, we use window examination, black run-length smearing,
horizontal histogram and connected component analysis.
Effective Multi-step Temporal-Difference Learning for Non-Linear Function Approximation
Authors: Harm van Seijen
Date: 2016-08-18 00:00:00

Multi-step temporal-difference (TD) learning, where the update targets
contain information from multiple time steps ahead, is one of the most popular
forms of TD learning for linear function approximation. The reason is that
multi-step methods often yield substantially better performance than their
single-step counter-parts, due to a lower bias of the update targets. For
non-linear function approximation, however, single-step methods appear to be
the norm. Part of the reason could be that on many domains the popular
multi-step methods TD($\lambda$) and Sarsa($\lambda$) do not perform well when
combined with non-linear function approximation. In particular, they are very
susceptible to divergence of value estimates. In this paper, we identify the
reason behind this. Furthermore, based on our analysis, we propose a new
multi-step TD method for non-linear function approximation that addresses this
issue. We confirm the effectiveness of our method using two benchmark tasks
with neural networks as function approximation.
SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification
Authors: Liang Wu, Fred Morstatter, Huan Liu
Date: 2016-08-17 00:00:00
Tasks: General Classification, Sentiment Analysis, Sentiment Classification

Sentiment in social media is increasingly considered as an important resource
for customer segmentation, market understanding, and tackling other
socio-economic issues. However, sentiment in social media is difficult to
measure since user-generated content is usually short and informal. Although
many traditional sentiment analysis methods have been proposed, identifying
slang sentiment words remains untackled. One of the reasons is that slang
sentiment words are not available in existing dictionaries or sentiment
lexicons. To this end, we propose to build the first sentiment dictionary of
slang words to aid sentiment analysis of social media content. It is laborious
and time-consuming to collect and label the sentiment polarity of a
comprehensive list of slang words. We present an approach to leverage web
resources to construct an extensive Slang Sentiment word Dictionary (SlangSD)
that is easy to maintain and extend. SlangSD is publicly available for research
purposes. We empirically show the advantages of using SlangSD, the newly-built
slang sentiment word dictionary for sentiment classification, and provide
examples demonstrating its ease of use with an existing sentiment system.
A Bayesian Network approach to County-Level Corn Yield Prediction using historical data and expert knowledge
Authors: Vikas Chawla, Hsiang Sing Naik, Adedotun Akintayo, Dermot Hayes, Patrick Schnable, Baskar Ganapathysubramanian, Soumik Sarkar
Date: 2016-08-17 00:00:00
Tasks: Management

Crop yield forecasting is the methodology of predicting crop yields prior to
harvest. The availability of accurate yield prediction frameworks have enormous
implications from multiple standpoints, including impact on the crop commodity
futures markets, formulation of agricultural policy, as well as crop insurance
rating. The focus of this work is to construct a corn yield predictor at the
county scale. Corn yield (forecasting) depends on a complex, interconnected set
of variables that include economic, agricultural, management and meteorological
factors. Conventional forecasting is either knowledge-based computer programs
(that simulate plant-weather-soil-management interactions) coupled with
targeted surveys or statistical model based. The former is limited by the need
for painstaking calibration, while the latter is limited to univariate analysis
or similar simplifying assumptions that fail to capture the complex
interdependencies affecting yield. In this paper, we propose a data-driven
approach that is "gray box" i.e. that seamlessly utilizes expert knowledge in
constructing a statistical network model for corn yield forecasting. Our
multivariate gray box model is developed on Bayesian network analysis to build
a Directed Acyclic Graph (DAG) between predictors and yield. Starting from a
complete graph connecting various carefully chosen variables and yield, expert
knowledge is used to prune or strengthen edges connecting variables.
Subsequently the structure (connectivity and edge weights) of the DAG that
maximizes the likelihood of observing the training data is identified via
optimization. We curated an extensive set of historical data (1948-2012) for
each of the 99 counties in Iowa as data to train the model.
Optimal Management of Naturally Regenerating Uneven-aged Forests
Authors: Ankur Sinha, Janne Rämö, Pekka Malo, Markku Kallio, Olli Tahvonen
Date: 2016-08-17 00:00:00
Tasks: Management

A shift from even-aged forest management to uneven-aged management practices
leads to a problem rather different from the existing straightforward practice
that follows a rotation cycle of artificial regeneration, thinning of inferior
trees and a clearcut. A lack of realistic models and methods suggesting how to
manage uneven-aged stands in a way that is economically viable and ecologically
sustainable creates difficulties in adopting this new management practice. To
tackle this problem, we make a two-fold contribution in this paper. The first
contribution is the proposal of an algorithm that is able to handle a realistic
uneven-aged stand management model that is otherwise computationally tedious
and intractable. The model considered in this paper is an empirically estimated
size-structured ecological model for uneven-aged spruce forests. The second
contribution is on the sensitivity analysis of the forest model with respect to
a number of important parameters. The analysis provides us an insight into the
behavior of the uneven-aged forest model.
Evolutionary Approaches to Optimization Problems in Chimera Topologies
Authors: Roberto Santana, Zheng Zhu, Helmut G. Katzgraber
Date: 2016-08-17 00:00:00
Tasks: Evolutionary Algorithms

Chimera graphs define the topology of one of the first commercially available
quantum computers. A variety of optimization problems have been mapped to this
topology to evaluate the behavior of quantum enhanced optimization heuristics
in relation to other optimizers, being able to efficiently solve problems
classically to use them as benchmarks for quantum machines. In this paper we
investigate for the first time the use of Evolutionary Algorithms (EAs) on
Ising spin glass instances defined on the Chimera topology. Three genetic
algorithms (GAs) and three estimation of distribution algorithms (EDAs) are
evaluated over $1000$ hard instances of the Ising spin glass constructed from
Sidon sets. We focus on determining whether the information about the topology
of the graph can be used to improve the results of EAs and on identifying the
characteristics of the Ising instances that influence the success rate of GAs
and EDAs.
Scene Labeling Through Knowledge-Based Rules Employing Constrained Integer Linear Programing
Authors: Nasim Souly, Mubarak Shah
Date: 2016-08-17 00:00:00
Tasks: Scene Labeling

Scene labeling task is to segment the image into meaningful regions and
categorize them into classes of objects which comprised the image. Commonly
used methods typically find the local features for each segment and label them
using classifiers. Afterward, labeling is smoothed in order to make sure that
neighboring regions receive similar labels. However, they ignore expressive and
non-local dependencies among regions due to expensive training and inference.
In this paper, we propose to use high-level knowledge regarding rules in the
inference to incorporate dependencies among regions in the image to improve
scores of classification. Towards this aim, we extract these rules from data
and transform them into constraints for Integer Programming to optimize the
structured problem of assigning labels to super-pixels (consequently pixels) of
an image. In addition, we propose to use soft-constraints in some scenarios,
allowing violating the constraint by imposing a penalty, to make the model more
flexible. We assessed our approach on three datasets and obtained promising
results.