pwc-archive/papers-with-abstracts huggingface dataset:
IDK Cascades: Fast Deep Learning by Learning not to Overthink
Authors: Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, Joseph E. Gonzalez
Date: 2017-06-03 00:00:00
Tasks: Deep Learning, Dialogue Generation, Prediction

Advances in deep learning have led to substantial increases in prediction
accuracy but have been accompanied by increases in the cost of rendering
predictions. We conjecture that fora majority of real-world inputs, the recent
advances in deep learning have created models that effectively "overthink" on
simple inputs. In this paper, we revisit the classic question of building model
cascades that primarily leverage class asymmetry to reduce cost. We introduce
the "I Don't Know"(IDK) prediction cascades framework, a general framework to
systematically compose a set of pre-trained models to accelerate inference
without a loss in prediction accuracy. We propose two search based methods for
constructing cascades as well as a new cost-aware objective within this
framework. The proposed IDK cascade framework can be easily adopted in the
existing model serving systems without additional model re-training. We
evaluate the proposed techniques on a range of benchmarks to demonstrate the
effectiveness of the proposed framework.
Contrast Enhancement of Brightness-Distorted Images by Improved Adaptive Gamma Correction
Authors: Gang Cao, Lihui Huang, Huawei Tian, Xianglin Huang, Yongbin Wang, Ruicong Zhi
Date: 2017-09-13 00:00:00

As an efficient image contrast enhancement (CE) tool, adaptive gamma correction (AGC) was previously proposed by relating gamma parameter with cumulative distribution function (CDF) of the pixel gray levels within an image. ACG deals well with most dimmed images, but fails for globally bright images and the dimmed images with local bright regions. Such two categories of brightness-distorted images are universal in real scenarios, such as improper exposure and white object regions. In order to attenuate such deficiencies, here we propose an improved AGC algorithm. The novel strategy of negative images is used to realize CE of the bright images, and the gamma correction modulated by truncated CDF is employed to enhance the dimmed ones. As such, local over-enhancement and structure distortion can be alleviated. Both qualitative and quantitative experimental results show that our proposed method yields consistently good CE results.
Exploiting skeletal structure in computer vision annotation with Benders decomposition
Authors: Shaofei Wang, Konrad Kording, Julian Yarkony
Date: 2017-09-13 00:00:00
Tasks: Pose Estimation

Many annotation problems in computer vision can be phrased as integer linear
programs (ILPs). The use of standard industrial solvers does not to exploit the
underlying structure of such problems eg, the skeleton in pose estimation. The
leveraging of the underlying structure in conjunction with industrial solvers
promises increases in both speed and accuracy. Such structure can be exploited
using Bender's decomposition, a technique from operations research, that solves
complex ILPs or mixed integer linear programs by decomposing them into
sub-problems that communicate via a master problem. The intuition is that
conditioned on a small subset of the variables the solution to the remaining
variables can be computed easily by taking advantage of properties of the ILP
constraint matrix such as block structure. In this paper we apply Benders
decomposition to a typical problem in computer vision where we have many
sub-ILPs (eg, partitioning of detections, body-parts) coupled to a master ILP
(eg, constructing skeletons). Dividing inference problems into a master problem
and sub-problems motivates the development of a plethora of novel models, and
inference approaches for the field of computer vision.
Switching nonparametric regression models for multi-curve data
Authors: Camila P. E. de Souza, Nancy E. Heckman, Helena Xu
Date: 2015-04-10 00:00:00
Tasks: regression

We develop and apply an approach for analyzing multi-curve data where each
curve is driven by a latent state process. The state at any particular point
determines a smooth function, forcing the individual curve to switch from one
function to another. Thus each curve follows what we call a switching
nonparametric regression model. We develop an EM algorithm to estimate the
model parameters. We also obtain standard errors for the parameter estimates of
the state process. We consider several types of state processes: independent
and identically distributed, independent but depending on a covariate and
Markov. Simulation studies show the frequentist properties of our estimates. We
apply our methods to a data set of a building's power usage.
A Review of Evaluation Techniques for Social Dialogue Systems
Authors: Amanda Cercas Curry, Helen Hastie, Verena Rieser
Date: 2017-09-13 00:00:00
Tasks: valid

In contrast with goal-oriented dialogue, social dialogue has no clear measure
of task success. Consequently, evaluation of these systems is notoriously hard.
In this paper, we review current evaluation methods, focusing on automatic
metrics. We conclude that turn-based metrics often ignore the context and do
not account for the fact that several replies are valid, while end-of-dialogue
rewards are mainly hand-crafted. Both lack grounding in human perceptions.
Multiple Instance Hyperspectral Target Characterization
Authors: Alina Zare, Changzhe Jiao, Taylor Glenn
Date: 2016-06-20 00:00:00

In this paper, two methods for multiple instance target characterization,
MI-SMF and MI-ACE, are presented. MI-SMF and MI-ACE estimate a discriminative
target signature from imprecisely-labeled and mixed training data. In many
applications, such as sub-pixel target detection in remotely-sensed
hyperspectral imagery, accurate pixel-level labels on training data is often
unavailable and infeasible to obtain. Furthermore, since sub-pixel targets are
smaller in size than the resolution of a single pixel, training data is
comprised only of mixed data points (in which target training points are
mixtures of responses from both target and non-target classes). Results show
improved, consistent performance over existing multiple instance concept
learning methods on several hyperspectral sub-pixel target detection problems.
Transfer learning for music classification and regression tasks
Authors: Keunwoo Choi, György Fazekas, Mark Sandler, Kyunghyun Cho
Date: 2017-03-27 00:00:00
Tasks: Classification, General Classification, Music Classification, Music Tagging, regression, Transfer Learning

In this paper, we present a transfer learning approach for music
classification and regression tasks. We propose to use a pre-trained convnet
feature, a concatenated feature vector using the activations of feature maps of
multiple layers in a trained convolutional network. We show how this convnet
feature can serve as general-purpose music representation. In the experiments,
a convnet is trained for music tagging and then transferred to other
music-related classification and regression tasks. The convnet feature
outperforms the baseline MFCC feature in all the considered tasks and several
previous approaches that are aggregating MFCCs as well as low- and high-level
music features.
On Early-stage Debunking Rumors on Twitter: Leveraging the Wisdom of Weak Learners
Authors: Tu Nguyen, Cheng Li, Claudia Niederée
Date: 2017-09-13 00:00:00
Tasks: General Classification, Time Series, Time Series Analysis

Recently a lot of progress has been made in rumor modeling and rumor detection for micro-blogging streams. However, existing automated methods do not perform very well for early rumor detection, which is crucial in many settings, e.g., in crisis situations. One reason for this is that aggregated rumor features such as propagation features, which work well on the long run, are - due to their accumulating characteristic - not very helpful in the early phase of a rumor. In this work, we present an approach for early rumor detection, which leverages Convolutional Neural Networks for learning the hidden representations of individual rumor-related tweets to gain insights on the credibility of each tweets. We then aggregate the predictions from the very beginning of a rumor to obtain the overall event credits (so-called wisdom), and finally combine it with a time series based rumor classification model. Our extensive experiments show a clearly improved classification performance within the critical very first hours of a rumor. For a better understanding, we also conduct an extensive feature evaluation that emphasized on the early stage and shows that the low-level credibility has best predictability at all phases of the rumor lifetime.
An efficient clustering algorithm from the measure of local Gaussian distribution
Authors: Yuan-Yen Tai
Date: 2017-09-13 00:00:00
Tasks: Clustering

In this paper, I will introduce a fast and novel clustering algorithm based on Gaussian distribution and it can guarantee the separation of each cluster centroid as a given parameter, $d_s$. The worst run time complexity of this algorithm is approximately $\sim$O$(T\times N \times \log(N))$ where $T$ is the iteration steps and $N$ is the number of features.
Deep Ordinal Ranking for Multi-Category Diagnosis of Alzheimer's Disease using Hippocampal MRI data
Authors: Hongming Li, Mohamad Habes, Yong Fan
Date: 2017-09-05 00:00:00
Tasks: Binary Classification, Classification, General Classification

Increasing effort in brain image analysis has been dedicated to early
diagnosis of Alzheimer's disease (AD) based on neuroimaging data. Most existing
studies have been focusing on binary classification problems, e.g.,
distinguishing AD patients from normal control (NC) elderly or mild cognitive
impairment (MCI) individuals from NC elderly. However, identifying individuals
with AD and MCI, especially MCI individuals who will convert to AD (progressive
MCI, pMCI), in a single setting, is needed to achieve the goal of early
diagnosis of AD. In this paper, we propose a deep ordinal ranking model for
distinguishing NC, stable MCI (sMCI), pMCI, and AD at an individual subject
level, taking into account the inherent ordinal severity of brain degeneration
caused by normal aging, MCI, and AD, rather than formulating the classification
as a multi-category classification problem. The proposed deep ordinal ranking
model focuses on the hippocampal morphology of individuals and learns
informative and discriminative features automatically. Experiment results based
on a large cohort of individuals from the Alzheimer's Disease Neuroimaging
Initiative (ADNI) indicate that the proposed method can achieve better
performance than traditional multi-category classification techniques using
shape and radiomics features from structural magnetic resonance imaging (MRI)
data.
Linguistic Features of Genre and Method Variation in Translation: A Computational Perspective
Authors: Ekaterina Lapshninova-Koltunski, Marcos Zampieri
Date: 2017-09-13 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification, Translation

In this paper we describe the use of text classification methods to
investigate genre and method variation in an English - German translation
corpus. For this purpose we use linguistically motivated features representing
texts using a combination of part-of-speech tags arranged in bigrams, trigrams,
and 4-grams. The classification method used in this paper is a Bayesian
classifier with Laplace smoothing. We use the output of the classifiers to
carry out an extensive feature analysis on the main difference between genres
and methods of translation.
GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval
Authors: Longhui Wei, Shiliang Zhang, Hantao Yao, Wen Gao, Qi Tian
Date: 2017-09-13 00:00:00
Tasks: Person Re-Identification, Representation Learning, Retrieval

The huge variance of human pose and the misalignment of detected human images
significantly increase the difficulty of person Re-Identification (Re-ID).
Moreover, efficient Re-ID systems are required to cope with the massive visual
data being produced by video surveillance systems. Targeting to solve these
problems, this work proposes a Global-Local-Alignment Descriptor (GLAD) and an
efficient indexing and retrieval framework, respectively. GLAD explicitly
leverages the local and global cues in human body to generate a discriminative
and robust representation. It consists of part extraction and descriptor
learning modules, where several part regions are first detected and then deep
neural networks are designed for representation learning on both the local and
global regions. A hierarchical indexing and retrieval framework is designed to
eliminate the huge redundancy in the gallery set, and accelerate the online
Re-ID procedure. Extensive experimental results show GLAD achieves competitive
accuracy compared to the state-of-the-art methods. Our retrieval framework
significantly accelerates the online Re-ID procedure without loss of accuracy.
Therefore, this work has potential to work better on person Re-ID tasks in real
scenarios.
ExprGAN: Facial Expression Editing with Controllable Expression Intensity
Authors: Hui Ding, Kumar Sricharan, Rama Chellappa
Date: 2017-09-12 00:00:00
Tasks: Data Augmentation, Decoder, Generative Adversarial Network, Image Retrieval, Incremental Learning, Retrieval

Facial expression editing is a challenging task as it needs a high-level
semantic understanding of the input face image. In conventional methods, either
paired training data is required or the synthetic face resolution is low.
Moreover, only the categories of facial expression can be changed. To address
these limitations, we propose an Expression Generative Adversarial Network
(ExprGAN) for photo-realistic facial expression editing with controllable
expression intensity. An expression controller module is specially designed to
learn an expressive and compact expression code in addition to the
encoder-decoder network. This novel architecture enables the expression
intensity to be continuously adjusted from low to high. We further show that
our ExprGAN can be applied for other tasks, such as expression transfer, image
retrieval, and data augmentation for training improved face expression
recognition models. To tackle the small size of the training database, an
effective incremental learning scheme is proposed. Quantitative and qualitative
evaluations on the widely used Oulu-CASIA dataset demonstrate the effectiveness
of ExprGAN.
A Deep Cascade Network for Unaligned Face Attribute Classification
Authors: Hui Ding, Hao Zhou, Shaohua Kevin Zhou, Rama Chellappa
Date: 2017-09-12 00:00:00
Tasks: Attribute, Classification, General Classification, Model Compression

Humans focus attention on different face regions when recognizing face
attributes. Most existing face attribute classification methods use the whole
image as input. Moreover, some of these methods rely on fiducial landmarks to
provide defined face parts. In this paper, we propose a cascade network that
simultaneously learns to localize face regions specific to attributes and
performs attribute classification without alignment. First, a weakly-supervised
face region localization network is designed to automatically detect regions
(or parts) specific to attributes. Then multiple part-based networks and a
whole-image-based network are separately constructed and combined together by
the region switch layer and attribute relation layer for final attribute
classification. A multi-net learning method and hint-based model compression is
further proposed to get an effective localization model and a compact
classification model, respectively. Our approach achieves significantly better
performance than state-of-the-art methods on unaligned CelebA dataset, reducing
the classification error by 30.9%.
Reading Scene Text with Attention Convolutional Sequence Modeling
Authors: Yunze Gao, Yingying Chen, Jinqiao Wang, Hanqing Lu
Date: 2017-09-13 00:00:00
Tasks: Scene Text Recognition

Reading text in the wild is a challenging task in the field of computer
vision. Existing approaches mainly adopted Connectionist Temporal
Classification (CTC) or Attention models based on Recurrent Neural Network
(RNN), which is computationally expensive and hard to train. In this paper, we
present an end-to-end Attention Convolutional Network for scene text
recognition. Firstly, instead of RNN, we adopt the stacked convolutional layers
to effectively capture the contextual dependencies of the input sequence, which
is characterized by lower computational complexity and easier parallel
computation. Compared to the chain structure of recurrent networks, the
Convolutional Neural Network (CNN) provides a natural way to capture long-term
dependencies between elements, which is 9 times faster than Bidirectional Long
Short-Term Memory (BLSTM). Furthermore, in order to enhance the representation
of foreground text and suppress the background noise, we incorporate the
residual attention modules into a small densely connected network to improve
the discriminability of CNN features. We validate the performance of our
approach on the standard benchmarks, including the Street View Text, IIIT5K and
ICDAR datasets. As a result, state-of-the-art or highly-competitive performance
and efficiency show the superiority of the proposed approach.
A Survey of Calibration Methods for Optical See-Through Head-Mounted Displays
Authors: Jens Grubert, Yuta Itoh, Kenneth Moser, J. Edward Swan II
Date: 2017-09-13 00:00:00
Tasks: Position

Optical see-through head-mounted displays (OST HMDs) are a major output
medium for Augmented Reality, which have seen significant growth in popularity
and usage among the general public due to the growing release of
consumer-oriented models, such as the Microsoft Hololens. Unlike Virtual
Reality headsets, OST HMDs inherently support the addition of
computer-generated graphics directly into the light path between a user's eyes
and their view of the physical world. As with most Augmented and Virtual
Reality systems, the physical position of an OST HMD is typically determined by
an external or embedded 6-Degree-of-Freedom tracking system. However, in order
to properly render virtual objects, which are perceived as spatially aligned
with the physical environment, it is also necessary to accurately measure the
position of the user's eyes within the tracking system's coordinate frame. For
over 20 years, researchers have proposed various calibration methods to
determine this needed eye position. However, to date, there has not been a
comprehensive overview of these procedures and their requirements. Hence, this
paper surveys the field of calibration methods for OST HMDs. Specifically, it
provides insights into the fundamentals of calibration techniques, and presents
an overview of both manual and automatic approaches, as well as evaluation
methods and metrics. Finally, it also identifies opportunities for future
research. % relative to the tracking coordinate system, and, hence, its
position in 3D space.
Biased Importance Sampling for Deep Neural Network Training
Authors: Angelos Katharopoulos, François Fleuret
Date: 2017-05-31 00:00:00
Tasks: image-classification, Image Classification, Language Modeling, Language Modelling, Stochastic Optimization

Importance sampling has been successfully used to accelerate stochastic
optimization in many convex problems. However, the lack of an efficient way to
calculate the importance still hinders its application to Deep Learning.
  In this paper, we show that the loss value can be used as an alternative
importance metric, and propose a way to efficiently approximate it for a deep
model, using a small model trained for that purpose in parallel.
  This method allows in particular to utilize a biased gradient estimate that
implicitly optimizes a soft max-loss, and leads to better generalization
performance. While such method suffers from a prohibitively high variance of
the gradient estimate when using a standard stochastic optimizer, we show that
when it is combined with our sampling mechanism, it results in a reliable
procedure.
  We showcase the generality of our method by testing it on both image
classification and language modeling tasks using deep convolutional and
recurrent neural networks. In particular, our method results in 30% faster
training of a CNN for CIFAR10 than when using uniform sampling.
Densely tracking sequences of 3D face scans
Authors: Huaxiong Ding, Liming Chen
Date: 2017-09-13 00:00:00

3D face dense tracking aims to find dense inter-frame correspondences in a
sequence of 3D face scans and constitutes a powerful tool for many face
analysis tasks, e.g., 3D dynamic facial expression analysis. The majority of
the existing methods just fit a 3D face surface or model to a 3D target surface
without considering temporal information between frames. In this paper, we
propose a novel method for densely tracking sequences of 3D face scans, which
ex- tends the non-rigid ICP algorithm by adding a novel specific criterion for
temporal information. A novel fitting framework is presented for automatically
tracking a full sequence of 3D face scans. The results of experiments carried
out on the BU4D-FE database are promising, showing that the proposed algorithm
outperforms state-of-the-art algorithms for 3D face dense tracking.
A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration
Authors: Bartolomeo Della Corte, Igor Bogoslavskyi, Cyrill Stachniss, Giorgio Grisetti
Date: 2017-09-13 00:00:00
Tasks: C++ code, Point Cloud Registration

The ability to build maps is a key functionality for the majority of mobile
robots. A central ingredient to most mapping systems is the registration or
alignment of the recorded sensor data. In this paper, we present a general
methodology for photometric registration that can deal with multiple different
cues. We provide examples for registering RGBD as well as 3D LIDAR data. In
contrast to popular point cloud registration approaches such as ICP our method
does not rely on explicit data association and exploits multiple modalities
such as raw range and image data streams. Color, depth, and normal information
are handled in an uniform manner and the registration is obtained by minimizing
the pixel-wise difference between two multi-channel images. We developed a
flexible and general framework and implemented our approach inside that
framework. We also released our implementation as open source C++ code. The
experiments show that our approach allows for an accurate registration of the
sensor data without requiring an explicit data association or model-specific
adaptations to datasets or sensors. Our approach exploits the different cues in
a natural and consistent way and the registration can be done at framerate for
a typical range or imaging sensor.