pwc-archive/papers-with-abstracts huggingface dataset:
μ-MAR: Multiplane 3D Marker based Registration for Depth-sensing Cameras
Authors: Marcelo Saval-Calvo, Jorge Azorin-Lopez, Andres Fuster-Guillo, Higinio Mora-Mora
Date: 2017-08-04 00:00:00
Tasks: Object Reconstruction

Many applications including object reconstruction, robot guidance, and scene
mapping require the registration of multiple views from a scene to generate a
complete geometric and appearance model of it. In real situations,
transformations between views are unknown an it is necessary to apply expert
inference to estimate them. In the last few years, the emergence of low-cost
depth-sensing cameras has strengthened the research on this topic, motivating a
plethora of new applications. Although they have enough resolution and accuracy
for many applications, some situations may not be solved with general
state-of-the-art registration methods due to the Signal-to-Noise ratio (SNR)
and the resolution of the data provided. The problem of working with low SNR
data, in general terms, may appear in any 3D system, then it is necessary to
propose novel solutions in this aspect. In this paper, we propose a method,
{\mu}-MAR, able to both coarse and fine register sets of 3D points provided by
low-cost depth-sensing cameras, despite it is not restricted to these sensors,
into a common coordinate system. The method is able to overcome the noisy data
problem by means of using a model-based solution of multiplane registration.
Specifically, it iteratively registers 3D markers composed by multiple planes
extracted from points of multiple views of the scene. As the markers and the
object of interest are static in the scenario, the transformations obtained for
the markers are applied to the object in order to reconstruct it. Experiments
have been performed using synthetic and real data. The synthetic data allows a
qualitative and quantitative evaluation by means of visual inspection and
Hausdorff distance respectively. The real data experiments show the performance
of the proposal using data acquired by a Primesense Carmine RGB-D sensor. The
method has been compared to several state-of-the-art methods. The ...
Question Answering and Question Generation as Dual Tasks
Authors: Duyu Tang, Nan Duan, Tao Qin, Zhao Yan, Ming Zhou
Date: 2017-06-07 00:00:00
Tasks: Question Answering, Question Generation, Question-Generation

We study the problem of joint question answering (QA) and question generation
(QG) in this paper.
  Our intuition is that QA and QG have intrinsic connections and these two
tasks could improve each other.
  On one side, the QA model judges whether the generated question of a QG model
is relevant to the answer.
  On the other side, the QG model provides the probability of generating a
question given the answer, which is a useful evidence that in turn facilitates
QA.
  In this paper we regard QA and QG as dual tasks.
  We propose a training framework that trains the models of QA and QG
simultaneously, and explicitly leverages their probabilistic correlation to
guide the training process of both models.
  We implement a QG model based on sequence-to-sequence learning, and a QA
model based on recurrent neural network.
  As all the components of the QA and QG models are differentiable, all the
parameters involved in these two models could be conventionally learned with
back propagation.
  We conduct experiments on three datasets. Empirical results show that our
training framework improves both QA and QG tasks.
  The improved QA model performs comparably with strong baseline approaches on
all three datasets.
Submodular Trajectory Optimization for Aerial 3D Scanning
Authors: Mike Roberts, Debadeepta Dey, Anh Truong, Sudipta Sinha, Shital Shah, Ashish Kapoor, Pat Hanrahan, Neel Joshi
Date: 2017-05-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Trajectory Planning

Drones equipped with cameras are emerging as a powerful tool for large-scale
aerial 3D scanning, but existing automatic flight planners do not exploit all
available information about the scene, and can therefore produce inaccurate and
incomplete 3D models. We present an automatic method to generate drone
trajectories, such that the imagery acquired during the flight will later
produce a high-fidelity 3D model. Our method uses a coarse estimate of the
scene geometry to plan camera trajectories that: (1) cover the scene as
thoroughly as possible; (2) encourage observations of scene geometry from a
diverse set of viewing angles; (3) avoid obstacles; and (4) respect a
user-specified flight time budget. Our method relies on a mathematical model of
scene coverage that exhibits an intuitive diminishing returns property known as
submodularity. We leverage this property extensively to design a trajectory
planning algorithm that reasons globally about the non-additive coverage reward
obtained across a trajectory, jointly with the cost of traveling between views.
We evaluate our method by using it to scan three large outdoor scenes, and we
perform a quantitative evaluation using a photorealistic video game simulator.
HAMSI: A Parallel Incremental Optimization Algorithm Using Quadratic Approximations for Solving Partially Separable Problems
Authors: Kamer Kaya, Figen Öztoprak, Ş. İlker Birbil, A. Taylan Cemgil, Umut Şimşekli, Nurdan Kuru, Hazal Koptagel, M. Kaan Öztürk
Date: 2015-09-05 00:00:00

We propose HAMSI (Hessian Approximated Multiple Subsets Iteration), which is
a provably convergent, second order incremental algorithm for solving
large-scale partially separable optimization problems. The algorithm is based
on a local quadratic approximation, and hence, allows incorporating curvature
information to speed-up the convergence. HAMSI is inherently parallel and it
scales nicely with the number of processors. Combined with techniques for
effectively utilizing modern parallel computer architectures, we illustrate
that the proposed method converges more rapidly than a parallel stochastic
gradient descent when both methods are used to solve large-scale matrix
factorization problems. This performance gain comes only at the expense of
using memory that scales linearly with the total size of the optimization
variables. We conclude that HAMSI may be considered as a viable alternative in
many large scale problems, where first order methods based on variants of
stochastic gradient descent are applicable.
Hashtag Healthcare: From Tweets to Mental Health Journals Using Deep Transfer Learning
Authors: Benjamin Shickel, Martin Heesacker, Sherry Benton, Parisa Rashidi
Date: 2017-08-04 00:00:00
Tasks: Transfer Learning

As the popularity of social media platforms continues to rise, an
ever-increasing amount of human communication and self- expression takes place
online. Most recent research has focused on mining social media for public user
opinion about external entities such as product reviews or sentiment towards
political news. However, less attention has been paid to analyzing users'
internalized thoughts and emotions from a mental health perspective. In this
paper, we quantify the semantic difference between public Tweets and private
mental health journals used in online cognitive behavioral therapy. We will use
deep transfer learning techniques for analyzing the semantic gap between the
two domains. We show that for the task of emotional valence prediction, social
media can be successfully harnessed to create more accurate, robust, and
personalized mental health models. Our results suggest that the semantic gap
between public and private self-expression is small, and that utilizing the
abundance of available social media is one way to overcome the small sample
sizes of mental health data, which are commonly limited by availability and
privacy concerns.
Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis
Authors: Rui Huang, Shu Zhang, Tianyu Li, Ran He
Date: 2017-04-13 00:00:00
Proceeding: ICCV 2017 10
Tasks: Decoder, Face Recognition, Generative Adversarial Network

Photorealistic frontal view synthesis from a single face image has a wide
range of applications in the field of face recognition. Although data-driven
deep learning methods have been proposed to address this problem by seeking
solutions from ample face data, this problem is still challenging because it is
intrinsically ill-posed. This paper proposes a Two-Pathway Generative
Adversarial Network (TP-GAN) for photorealistic frontal view synthesis by
simultaneously perceiving global structures and local details. Four landmark
located patch networks are proposed to attend to local textures in addition to
the commonly used global encoder-decoder network. Except for the novel
architecture, we make this ill-posed problem well constrained by introducing a
combination of adversarial loss, symmetry loss and identity preserving loss.
The combined loss function leverages both frontal face distribution and
pre-trained discriminative deep face models to guide an identity preserving
inference of frontal views from profiles. Different from previous deep learning
methods that mainly rely on intermediate features for recognition, our method
directly leverages the synthesized identity preserving image for downstream
tasks like face recognition and attribution estimation. Experimental results
demonstrate that our method not only presents compelling perceptual results but
also outperforms state-of-the-art results on large pose face recognition.
A Measure for Dialog Complexity and its Application in Streamlining Service Operations
Authors: Q. Vera Liao, Biplav Srivastava, Pavan Kapanipathi
Date: 2017-08-04 00:00:00

Dialog is a natural modality for interaction between customers and businesses
in the service industry. As customers call up the service provider, their
interactions may be routine or extraordinary. We believe that these
interactions, when seen as dialogs, can be analyzed to obtain a better
understanding of customer needs and how to efficiently address them. We
introduce the idea of a dialog complexity measure to characterize multi-party
interactions, propose a general data-driven method to calculate it, use it to
discover insights in public and enterprise dialog datasets, and demonstrate its
beneficial usage in facilitating better handling of customer requests and
evaluating service agents.
A novel metaheuristic method for solving constrained engineering optimization problems: Drone Squadron Optimization
Authors: Vinícius Veloso de Melo
Date: 2017-08-04 00:00:00

Several constrained optimization problems have been adequately solved over
the years thanks to advances in the metaheuristics area. In this paper, we
evaluate a novel self-adaptive and auto-constructive metaheuristic called Drone
Squadron Optimization (DSO) in solving constrained engineering design problems.
This paper evaluates DSO with death penalty on three widely tested engineering
design problems. Results show that the proposed approach is competitive with
some very popular metaheuristics.
Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference
Authors: Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, Diana Inkpen
Date: 2017-08-04 00:00:00
Proceeding: WS 2017 9
Tasks: Natural Language Inference, Natural Language Understanding, Sentence

The RepEval 2017 Shared Task aims to evaluate natural language understanding
models for sentence representation, in which a sentence is represented as a
fixed-length vector with neural networks and the quality of the representation
is tested with a natural language inference task. This paper describes our
system (alpha) that is ranked among the top in the Shared Task, on both the
in-domain test set (obtaining a 74.9% accuracy) and on the cross-domain test
set (also attaining a 74.9% accuracy), demonstrating that the model generalizes
well to the cross-domain data. Our model is equipped with intra-sentence
gated-attention composition which helps achieve a better performance. In
addition to submitting our model to the Shared Task, we have also tested it on
the Stanford Natural Language Inference (SNLI) dataset. We obtain an accuracy
of 85.5%, which is the best reported result on SNLI when cross-sentence
attention is not allowed, the same condition enforced in RepEval 2017.
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Authors: Chen Sun, Abhinav Shrivastava, Saurabh Singh, Abhinav Gupta
Date: 2017-07-10 00:00:00
Proceeding: ICCV 2017 10
Tasks: Deep Learning, image-classification, Image Classification, object-detection, Object Detection, Pose Estimation, Representation Learning, Semantic Segmentation

The success of deep learning in vision can be attributed to: (a) models with
high capacity; (b) increased computational power; and (c) availability of
large-scale labeled data. Since 2012, there have been significant advances in
representation capabilities of the models and computational capabilities of
GPUs. But the size of the biggest dataset has surprisingly remained constant.
What will happen if we increase the dataset size by 10x or 100x? This paper
takes a step towards clearing the clouds of mystery surrounding the
relationship between `enormous data' and visual deep learning. By exploiting
the JFT-300M dataset which has more than 375M noisy labels for 300M images, we
investigate how the performance of current vision tasks would change if this
data was used for representation learning. Our paper delivers some surprising
(and some expected) findings. First, we find that the performance on vision
tasks increases logarithmically based on volume of training data size. Second,
we show that representation learning (or pre-training) still holds a lot of
promise. One can improve performance on many vision tasks by just training a
better base model. Finally, as expected, we present new state-of-the-art
results for different vision tasks including image classification, object
detection, semantic segmentation and human pose estimation. Our sincere hope is
that this inspires vision community to not undervalue the data and develop
collective efforts in building larger datasets.
Line Profile Based Segmentation Algorithm for Touching Corn Kernels
Authors: Ali Mahdi, Jun Qin
Date: 2017-06-01 00:00:00
Tasks: General Classification, Image Segmentation, Segmentation, Semantic Segmentation

Image segmentation of touching objects plays a key role in providing accurate
classification for computer vision technologies. A new line profile based
imaging segmentation algorithm has been developed to provide a robust and
accurate segmentation of a group of touching corns. The performance of the line
profile based algorithm has been compared to a watershed based imaging
segmentation algorithm. Both algorithms are tested on three different patterns
of images, which are isolated corns, single-lines, and random distributed
formations. The experimental results show that the algorithm can segment a
large number of touching corn kernels efficiently and accurately.
MemexQA: Visual Memex Question Answering
Authors: Lu Jiang, Junwei Liang, Liangliang Cao, Yannis Kalantidis, Sachin Farfade, Alexander Hauptmann
Date: 2017-08-04 00:00:00
Tasks: Memex Question Answering, Question Answering, Video Question Answering

This paper proposes a new task, MemexQA: given a collection of photos or
videos from a user, the goal is to automatically answer questions that help
users recover their memory about events captured in the collection. Towards
solving the task, we 1) present the MemexQA dataset, a large, realistic
multimodal dataset consisting of real personal photos and crowd-sourced
questions/answers, 2) propose MemexNet, a unified, end-to-end trainable network
architecture for image, text and video question answering. Experimental results
on the MemexQA dataset demonstrate that MemexNet outperforms strong baselines
and yields the state-of-the-art on this novel and challenging task. The
promising results on TextQA and VideoQA suggest MemexNet's efficacy and
scalability across various QA tasks.
Fashion Forward: Forecasting Visual Style in Fashion
Authors: Ziad Al-Halah, Rainer Stiefelhagen, Kristen Grauman
Date: 2017-05-18 00:00:00
Proceeding: ICCV 2017 10

What is the future of fashion? Tackling this question from a data-driven vision perspective, we propose to forecast visual style trends before they occur. We introduce the first approach to predict the future popularity of styles discovered from fashion images in an unsupervised manner. Using these styles as a basis, we train a forecasting model to represent their trends over time. The resulting model can hypothesize new mixtures of styles that will become popular in the future, discover style dynamics (trendy vs. classic), and name the key visual attributes that will dominate tomorrow's fashion. We demonstrate our idea applied to three datasets encapsulating 80,000 fashion products sold across six years on Amazon. Results indicate that fashion forecasting benefits greatly from visual analysis, much more than textual or meta-data cues surrounding products.
Flare Prediction Using Photospheric and Coronal Image Data
Authors: Eric Jonas, Monica G. Bobra, Vaishaal Shankar, J. Todd Hoeksema, Benjamin Recht
Date: 2017-08-03 00:00:00
Tasks: Prediction

The precise physical process that triggers solar flares is not currently
understood. Here we attempt to capture the signature of this mechanism in solar
image data of various wavelengths and use these signatures to predict flaring
activity. We do this by developing an algorithm that [1] automatically
generates features in 5.5 TB of image data taken by the Solar Dynamics
Observatory of the solar photosphere, chromosphere, transition region, and
corona during the time period between May 2010 and May 2014, [2] combines these
features with other features based on flaring history and a physical
understanding of putative flaring processes, and [3] classifies these features
to predict whether a solar active region will flare within a time period of $T$
hours, where $T$ = 2 and 24. We find that when optimizing for the True Skill
Score (TSS), photospheric vector magnetic field data combined with flaring
history yields the best performance, and when optimizing for the area under the
precision-recall curve, all the data are helpful. Our model performance yields
a TSS of $0.84 \pm 0.03$ and $0.81 \pm 0.03$ in the $T$ = 2 and 24 hour cases,
respectively, and a value of $0.13 \pm 0.07$ and $0.43 \pm 0.08$ for the area
under the precision-recall curve in the $T$ = 2 and 24 hour cases,
respectively. These relatively high scores are similar to, but not greater
than, other attempts to predict solar flares. Given the similar values of
algorithm performance across various types of models reported in the
literature, we conclude that we can expect a certain baseline predictive
capacity using these data. This is the first attempt to predict solar flares
using photospheric vector magnetic field data as well as multiple wavelengths
of image data from the chromosphere, transition region, and corona.
TALL: Temporal Activity Localization via Language Query
Authors: Jiyang Gao, Chen Sun, Zhenheng Yang, Ram Nevatia
Date: 2017-05-05 00:00:00
Proceeding: ICCV 2017 10
Tasks: Natural Language Queries, regression, Sentence, Temporal Localization

This paper focuses on temporal localization of actions in untrimmed videos.
Existing methods typically train classifiers for a pre-defined list of actions
and apply them in a sliding window fashion. However, activities in the wild
consist of a wide combination of actors, actions and objects; it is difficult
to design a proper activity list that meets users' needs. We propose to
localize activities by natural language queries. Temporal Activity Localization
via Language (TALL) is challenging as it requires: (1) suitable design of text
and video representations to allow cross-modal matching of actions and language
queries; (2) ability to locate actions accurately given features from sliding
windows of limited granularity. We propose a novel Cross-modal Temporal
Regression Localizer (CTRL) to jointly model text query and video clips, output
alignment scores and action boundary regression results for candidate clips.
For evaluation, we adopt TaCoS dataset, and build a new dataset for this task
on top of Charades by adding sentence temporal annotations, called
Charades-STA. We also build complex sentence queries in Charades-STA for test.
Experimental results show that CTRL outperforms previous methods significantly
on both datasets.
Faster Discovery of Faster System Configurations with Spectral Learning
Authors: Vivek Nair, Tim Menzies, Norbert Siegmund, Sven Apel
Date: 2017-01-27 00:00:00
Tasks: Dimensionality Reduction

Despite the huge spread and economical importance of configurable software
systems, there is unsatisfactory support in utilizing the full potential of
these systems with respect to finding performance-optimal configurations. Prior
work on predicting the performance of software configurations suffered from
either (a) requiring far too many sample configurations or (b) large variances
in their predictions. Both these problems can be avoided using the WHAT
spectral learner. WHAT's innovation is the use of the spectrum (eigenvalues) of
the distance matrix between the configurations of a configurable software
system, to perform dimensionality reduction. Within that reduced configuration
space, many closely associated configurations can be studied by executing only
a few sample configurations. For the subject systems studied here, a few dozen
samples yield accurate and stable predictors - less than 10% prediction error,
with a standard deviation of less than 2%. When compared to the state of the
art, WHAT (a) requires 2 to 10 times fewer samples to achieve similar
prediction accuracies, and (b) its predictions are more stable (i.e., have
lower standard deviation). Furthermore, we demonstrate that predictive models
generated by WHAT can be used by optimizers to discover system configurations
that closely approach the optimal performance.
Automatic Spatially-aware Fashion Concept Discovery
Authors: Xintong Han, Zuxuan Wu, Phoenix X. Huang, Xiao Zhang, Menglong Zhu, Yuan Li, Yang Zhao, Larry S. Davis
Date: 2017-08-03 00:00:00
Proceeding: ICCV 2017 10
Tasks: Attribute, Clustering, Image Retrieval with Multi-Modal Query, Retrieval

This paper proposes an automatic spatially-aware concept discovery approach
using weakly labeled image-text data from shopping websites. We first fine-tune
GoogleNet by jointly modeling clothing images and their corresponding
descriptions in a visual-semantic embedding space. Then, for each attribute
(word), we generate its spatially-aware representation by combining its
semantic word vector representation with its spatial representation derived
from the convolutional maps of the fine-tuned network. The resulting
spatially-aware representations are further used to cluster attributes into
multiple groups to form spatially-aware concepts (e.g., the neckline concept
might consist of attributes like v-neck, round-neck, etc). Finally, we
decompose the visual-semantic embedding space into multiple concept-specific
subspaces, which facilitates structured browsing and attribute-feedback product
retrieval by exploiting multimodal linguistic regularities. We conducted
extensive experiments on our newly collected Fashion200K dataset, and results
on clustering quality evaluation and attribute-feedback product retrieval task
demonstrate the effectiveness of our automatically discovered spatially-aware
concepts.