pwc-archive/papers-with-abstracts huggingface dataset:
Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration
Authors: Yi Chang, Luxin Yan, Houzhang Fang, Sheng Zhong, Zhijun Zhang
Date: 2017-09-01 00:00:00
Tasks: Deblurring, Denoising, Hyperspectral Image Denoising, Image Restoration, Super-Resolution

Hyperspectral imaging, providing abundant spatial and spectral information
simultaneously, has attracted a lot of interest in recent years. Unfortunately,
due to the hardware limitations, the hyperspectral image (HSI) is vulnerable to
various degradations, such noises (random noise, HSI denoising), blurs
(Gaussian and uniform blur, HSI deblurring), and down-sampled (both spectral
and spatial downsample, HSI super-resolution). Previous HSI restoration methods
are designed for one specific task only. Besides, most of them start from the
1-D vector or 2-D matrix models and cannot fully exploit the structurally
spectral-spatial correlation in 3-D HSI. To overcome these limitations, in this
work, we propose a unified low-rank tensor recovery model for comprehensive HSI
restoration tasks, in which non-local similarity between spectral-spatial cubic
and spectral correlation are simultaneously captured by 3-order tensors.
Further, to improve the capability and flexibility, we formulate it as a
weighted low-rank tensor recovery (WLRTR) model by treating the singular values
differently, and study its analytical solution. We also consider the exclusive
stripe noise in HSI as the gross error by extending WLRTR to robust principal
component analysis (WLRTR-RPCA). Extensive experiments demonstrate the proposed
WLRTR models consistently outperform state-of-the-arts in typical low level
vision HSI tasks, including denoising, destriping, deblurring and
super-resolution.
Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery
Authors: Ryuhei Hamaguchi, Aito Fujita, Keisuke Nemoto, Tomoyuki Imaizumi, Shuhei Hikosaka
Date: 2017-09-01 00:00:00
Tasks: Semantic Segmentation

Thanks to recent advances in CNNs, solid improvements have been made in
semantic segmentation of high resolution remote sensing imagery. However, most
of the previous works have not fully taken into account the specific
difficulties that exist in remote sensing tasks. One of such difficulties is
that objects are small and crowded in remote sensing imagery. To tackle with
this challenging task we have proposed a novel architecture called local
feature extraction (LFE) module attached on top of dilated front-end module.
The LFE module is based on our findings that aggressively increasing dilation
factors fails to aggregate local features due to sparsity of the kernel, and
detrimental to small objects. The proposed LFE module solves this problem by
aggregating local features with decreasing dilation factor. We tested our
network on three remote sensing datasets and acquired remarkably good results
for all datasets especially for small objects.
Robust PCA by Manifold Optimization
Authors: Teng Zhang, Yi Yang
Date: 2017-08-01 00:00:00

Robust PCA is a widely used statistical procedure to recover a underlying
low-rank matrix with grossly corrupted observations. This work considers the
problem of robust PCA as a nonconvex optimization problem on the manifold of
low-rank matrices, and proposes two algorithms (for two versions of
retractions) based on manifold optimization. It is shown that, with a proper
designed initialization, the proposed algorithms are guaranteed to converge to
the underlying low-rank matrix linearly. Compared with a previous work based on
the Burer-Monterio decomposition of low-rank matrices, the proposed algorithms
reduce the dependence on the conditional number of the underlying low-rank
matrix theoretically. Simulations and real data examples confirm the
competitive performance of our method.
A Survey of Multi-View Representation Learning
Authors: Yingming Li, Ming Yang, Zhongfei Zhang
Date: 2016-10-03 00:00:00
Tasks: Representation Learning, Survey

Recently, multi-view representation learning has become a rapidly growing
direction in machine learning and data mining areas. This paper introduces two
categories for multi-view representation learning: multi-view representation
alignment and multi-view representation fusion. Consequently, we first review
the representative methods and theories of multi-view representation learning
based on the perspective of alignment, such as correlation-based alignment.
Representative examples are canonical correlation analysis (CCA) and its
several extensions. Then from the perspective of representation fusion we
investigate the advancement of multi-view representation learning that ranges
from generative methods including multi-modal topic learning, multi-view sparse
coding, and multi-view latent space Markov networks, to neural network-based
methods including multi-modal autoencoders, multi-view convolutional neural
networks, and multi-modal recurrent neural networks. Further, we also
investigate several important applications of multi-view representation
learning. Overall, this survey aims to provide an insightful overview of
theoretical foundation and state-of-the-art developments in the field of
multi-view representation learning and to help researchers find the most
appropriate tools for particular applications.
Reasoning with shapes: profiting cognitive susceptibilities to infer linear mapping transformations between shapes
Authors: Vahid Jalili
Date: 2017-09-01 00:00:00
Tasks: Scene Understanding

Visual information plays an indispensable role in our daily interactions with
environment. Such information is manipulated for a wide range of purposes
spanning from basic object and material perception to complex gesture
interpretations. There have been novel studies in cognitive science for
in-depth understanding of visual information manipulation, which lead to answer
questions such as: how we infer 2D/3D motion from a sequence of 2D images? how
we understand a motion from a single image frame? how we see forest avoiding
trees?
  Leveraging on congruence, linear mapping transformation determination between
a set of shapes facilitate motion perception. Present study methodizes recent
discoveries of human cognitive ability for scene understanding. The proposed
method processes images hierarchically, that is an iterative analysis of scene
abstractions using a rapidly converging heuristic iterative method. The method
hierarchically abstracts images; the abstractions are represented in polar
coordinate system, and any two consecutive abstractions have incremental level
of details. The method then creates a graph of approximated linear mapping
transformations based on circular shift permutations of hierarchical
abstractions. The graph is then traversed in best-first fashion to find best
linear mapping transformation. The accuracy of the proposed method is assessed
using normal, noisy, and deformed images. Additionally, the present study
deduces (i) the possibility of determining optimal mapping linear
transformations in logarithmic iterations with respect to the precision of
results, and (ii) computational cost is independent from the resolution of
input shapes.
Order-Planning Neural Text Generation From Structured Data
Authors: Lei Sha, Lili Mou, Tianyu Liu, Pascal Poupart, Sujian Li, Baobao Chang, Zhifang Sui
Date: 2017-09-01 00:00:00
Tasks: Decoder, Question Answering, Table-to-Text Generation, Text Generation

Generating texts from structured data (e.g., a table) is important for
various natural language processing tasks such as question answering and dialog
systems. In recent studies, researchers use neural language models and
encoder-decoder frameworks for table-to-text generation. However, these neural
network-based approaches do not model the order of contents during text
generation. When a human writes a summary based on a given table, he or she
would probably consider the content order before wording. In a biography, for
example, the nationality of a person is typically mentioned before occupation
in a biography. In this paper, we propose an order-planning text generation
model to capture the relationship between different fields and use such
relationship to make the generated text more fluent and smooth. We conducted
experiments on the WikiBio dataset and achieve significantly higher performance
than previous methods in terms of BLEU, ROUGE, and NIST scores.
Learning what to read: Focused machine reading
Authors: Enrique Noriega-Atala, Marco A. Valenzuela-Escarcega, Clayton T. Morrison, Mihai Surdeanu
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Reading Comprehension, Reinforcement Learning, Reinforcement Learning (RL)

Recent efforts in bioinformatics have achieved tremendous progress in the
machine reading of biomedical literature, and the assembly of the extracted
biochemical interactions into large-scale models such as protein signaling
pathways. However, batch machine reading of literature at today's scale (PubMed
alone indexes over 1 million papers per year) is unfeasible due to both cost
and processing overhead. In this work, we introduce a focused reading approach
to guide the machine reading of biomedical literature towards what literature
should be read to answer a biomedical query as efficiently as possible. We
introduce a family of algorithms for focused reading, including an intuitive,
strong baseline, and a second approach which uses a reinforcement learning (RL)
framework that learns when to explore (widen the search) or exploit (narrow
it). We demonstrate that the RL approach is capable of answering more queries
than the baseline, while being more efficient, i.e., reading fewer documents.
Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings
Authors: Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu
Date: 2017-09-01 00:00:00

This paper presents a convergence analysis of kernel-based quadrature rules
in misspecified settings, focusing on deterministic quadrature in Sobolev
spaces. In particular, we deal with misspecified settings where a test
integrand is less smooth than a Sobolev RKHS based on which a quadrature rule
is constructed. We provide convergence guarantees based on two different
assumptions on a quadrature rule: one on quadrature weights, and the other on
design points. More precisely, we show that convergence rates can be derived
(i) if the sum of absolute weights remains constant (or does not increase
quickly), or (ii) if the minimum distance between design points does not
decrease very quickly. As a consequence of the latter result, we derive a rate
of convergence for Bayesian quadrature in misspecified settings. We reveal a
condition on design points to make Bayesian quadrature robust to
misspecification, and show that, under this condition, it may adaptively
achieve the optimal rate of convergence in the Sobolev space of a lesser order
(i.e., of the unknown smoothness of a test integrand), under a slightly
stronger regularity condition on the integrand.
Towards a Dedicated Computer Vision Tool set for Crowd Simulation Models
Authors: Sultan Daud Khan, Muhammad Saqib, Michael Blumenstein
Date: 2017-09-01 00:00:00
Tasks: Crowd Counting

As the population of world is increasing, and even more concentrated in urban
areas, ensuring public safety is becoming a taunting job for security personnel
and crowd managers. Mass events like sports, festivals, concerts, political
gatherings attract thousand of people in a constraint environment,therefore
adequate safety measures should be adopted. Despite safety measures, crowd
disasters still occur frequently. Understanding underlying dynamics and
behavior of crowd is becoming areas of interest for most of computer
scientists. In recent years, researchers developed several models for
understanding crowd dynamics. These models should be properly calibrated and
validated by means of data acquired in the field. In this paper, we developed a
computer vision tool set that can be helpful not only in initializing the crowd
simulation models but can also validate the simulation results. The main
features of proposed tool set are: (1) Crowd flow segmentation and crowd
counting, (2) Identifying source/sink location for understanding crowd
behavior, (3) Group detection and tracking in crowds.
Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Network
Authors: ByeoungDo Kim, Chang Mook Kang, Seung Hi Lee, Hyunmin Chae, Jaekyum Kim, Chung Choo Chung, Jun Won Choi
Date: 2017-04-24 00:00:00
Tasks: Model Optimization, Prediction, Trajectory Prediction

In this paper, we propose an efficient vehicle trajectory prediction
framework based on recurrent neural network. Basically, the characteristic of
the vehicle's trajectory is different from that of regular moving objects since
it is affected by various latent factors including road structure, traffic
rules, and driver's intention. Previous state of the art approaches use
sophisticated vehicle behavior model describing these factors and derive the
complex trajectory prediction algorithm, which requires a system designer to
conduct intensive model optimization for practical use. Our approach is
data-driven and simple to use in that it learns complex behavior of the
vehicles from the massive amount of trajectory data through deep neural network
model. The proposed trajectory prediction method employs the recurrent neural
network called long short-term memory (LSTM) to analyze the temporal behavior
and predict the future coordinate of the surrounding vehicles. The proposed
scheme feeds the sequence of vehicles' coordinates obtained from sensor
measurements to the LSTM and produces the probabilistic information on the
future location of the vehicles over occupancy grid map. The experiments
conducted using the data collected from highway driving show that the proposed
method can produce reasonably good estimate of future trajectory.
Context Based Visual Content Verification
Authors: Martin Lukac, Aigerim Bazarbayeva, Michitaka Kameyama
Date: 2017-09-01 00:00:00
Tasks: Attribute

In this paper the intermediary visual content verification method based on
multi-level co-occurrences is studied. The co-occurrence statistics are in
general used to determine relational properties between objects based on
information collected from data. As such these measures are heavily subject to
relative number of occurrences and give only limited amount of accuracy when
predicting objects in real world. In order to improve the accuracy of this
method in the verification task, we include the context information such as
location, type of environment etc. In order to train our model we provide new
annotated dataset the Advanced Attribute VOC (AAVOC) that contains additional
properties of the image. We show that the usage of context greatly improve the
accuracy of verification with up to 16% improvement.
Single Shot Text Detector with Regional Attention
Authors: Pan He, Weilin Huang, Tong He, Qile Zhu, Yu Qiao, Xiaolin Li
Date: 2017-09-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Scene Text Detection

We present a novel single-shot text detector that directly outputs word-level
bounding boxes in a natural image. We propose an attention mechanism which
roughly identifies text regions via an automatically learned attentional map.
This substantially suppresses background interference in the convolutional
features, which is the key to producing accurate inference of words,
particularly at extremely small sizes. This results in a single model that
essentially works in a coarse-to-fine manner. It departs from recent FCN- based
text detectors which cascade multiple FCN models to achieve an accurate
prediction. Furthermore, we develop a hierarchical inception module which
efficiently aggregates multi-scale inception features. This enhances local
details, and also encodes strong context information, allow- ing the detector
to work reliably on multi-scale and multi- orientation text with single-scale
images. Our text detector achieves an F-measure of 77% on the ICDAR 2015 bench-
mark, advancing the state-of-the-art results in [18, 28]. Demo is available at:
http://sstd.whuang.org/.
Low Permutation-rank Matrices: Structural Properties and Noisy Completion
Authors: Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
Date: 2017-09-01 00:00:00
Tasks: Matrix Completion

We consider the problem of noisy matrix completion, in which the goal is to
reconstruct a structured matrix whose entries are partially observed in noise.
Standard approaches to this underdetermined inverse problem are based on
assuming that the underlying matrix has low rank, or is well-approximated by a
low rank matrix. In this paper, we propose a richer model based on what we term
the "permutation-rank" of a matrix. We first describe how the classical
non-negative rank model enforces restrictions that may be undesirable in
practice, and how and these restrictions can be avoided by using the richer
permutation-rank model. Second, we establish the minimax rates of estimation
under the new permutation-based model, and prove that surprisingly, the minimax
rates are equivalent up to logarithmic factors to those for estimation under
the typical low rank model. Third, we analyze a computationally efficient
singular-value-thresholding algorithm, known to be optimal for the low-rank
setting, and show that it also simultaneously yields a consistent estimator for
the low-permutation rank setting. Finally, we present various structural
results characterizing the uniqueness of the permutation-rank decomposition,
and characterizing convex approximations of the permutation-rank polytope.
"i have a feeling trump will win..................": Forecasting Winners and Losers from User Predictions on Twitter
Authors: Sandesh Swamy, Alan Ritter, Marie-Catherine de Marneffe
Date: 2017-07-22 00:00:00
Proceeding: EMNLP 2017 9

Social media users often make explicit predictions about upcoming events.
Such statements vary in the degree of certainty the author expresses toward the
outcome:"Leonardo DiCaprio will win Best Actor" vs. "Leonardo DiCaprio may win"
or "No way Leonardo wins!". Can popular beliefs on social media predict who
will win? To answer this question, we build a corpus of tweets annotated for
veridicality on which we train a log-linear classifier that detects positive
veridicality with high precision. We then forecast uncertain outcomes using the
wisdom of crowds, by aggregating users' explicit predictions. Our method for
forecasting winners is fully automated, relying only on a set of contenders as
input. It requires no training data of past outcomes and outperforms sentiment
and tweet volume baselines on a broad range of contest prediction tasks. We
further demonstrate how our approach can be used to measure the reliability of
individual accounts' predictions and retrospectively identify surprise
outcomes.
Content-based similar document image retrieval using fusion of CNN features
Authors: Mao Tan, Si-Ping Yuan, Yong-Xin Su
Date: 2017-03-23 00:00:00
Tasks: Image Retrieval, Optical Character Recognition (OCR), Retrieval, text similarity

Rapid increase of digitized document give birth to high demand of document
image retrieval. While conventional document image retrieval approaches depend
on complex OCR-based text recognition and text similarity detection, this paper
proposes a new content-based approach, in which more attention is paid to
features extraction and fusion. In the proposed approach, multiple features of
document images are extracted by different CNN models. After that, the
extracted CNN features are reduced and fused into weighted average feature.
Finally, the document images are ranked based on feature similarity to a
provided query image. Experimental procedure is performed on a group of
document images that transformed from academic papers, which contain both
English and Chinese document, the results show that the proposed approach has
good ability to retrieve document images with similar text content, and the
fusion of CNN features can effectively improve the retrieval accuracy.
Macro Grammars and Holistic Triggering for Efficient Semantic Parsing
Authors: Yuchen Zhang, Panupong Pasupat, Percy Liang
Date: 2017-07-25 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Semantic Parsing, Sentence, Sentence Similarity

To learn a semantic parser from denotations, a learning algorithm must search
over a combinatorially large space of logical forms for ones consistent with
the annotated denotations. We propose a new online learning algorithm that
searches faster as training progresses. The two key ideas are using macro
grammars to cache the abstract patterns of useful logical forms found thus far,
and holistic triggering to efficiently retrieve the most relevant patterns
based on sentence similarity. On the WikiTableQuestions dataset, we first
expand the search space of an existing model to improve the state-of-the-art
accuracy from 38.7% to 42.7%, and then use macro grammars and holistic
triggering to achieve an 11x speedup and an accuracy of 43.7%.
Structural Embedding of Syntactic Trees for Machine Comprehension
Authors: Rui Liu, Junjie Hu, Wei Wei, Zi Yang, Eric Nyberg
Date: 2017-03-02 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Question Answering, Reading Comprehension

Deep neural networks for machine comprehension typically utilizes only word
or character embeddings without explicitly taking advantage of structured
linguistic information such as constituency trees and dependency trees. In this
paper, we propose structural embedding of syntactic trees (SEST), an algorithm
framework to utilize structured information and encode them into vector
representations that can boost the performance of algorithms for the machine
comprehension. We evaluate our approach using a state-of-the-art neural
attention model on the SQuAD dataset. Experimental results demonstrate that our
model can accurately identify the syntactic boundaries of the sentences and
extract answers that are syntactically coherent over the baseline methods.
FacePoseNet: Making a Case for Landmark-Free Face Alignment
Authors: Feng-Ju Chang, Anh Tuan Tran, Tal Hassner, Iacopo Masi, Ram Nevatia, Gerard Medioni
Date: 2017-08-24 00:00:00
Tasks: 3D Face Alignment, Face Alignment, Face Identification, Face Recognition, Face Verification, Facial Landmark Detection

We show how a simple convolutional neural network (CNN) can be trained to
accurately and robustly regress 6 degrees of freedom (6DoF) 3D head pose,
directly from image intensities. We further explain how this FacePoseNet (FPN)
can be used to align faces in 2D and 3D as an alternative to explicit facial
landmark detection for these tasks. We claim that in many cases the standard
means of measuring landmark detector accuracy can be misleading when comparing
different face alignments. Instead, we compare our FPN with existing methods by
evaluating how they affect face recognition accuracy on the IJB-A and IJB-B
benchmarks: using the same recognition pipeline, but varying the face alignment
method. Our results show that (a) better landmark detection accuracy measured
on the 300W benchmark does not necessarily imply better face recognition
accuracy. (b) Our FPN provides superior 2D and 3D face alignment on both
benchmarks. Finally, (c), FPN aligns faces at a small fraction of the
computational cost of comparably accurate landmark detectors. For many
purposes, FPN is thus a far faster and far more accurate face alignment method
than using facial landmark detectors.