pwc-archive/papers-with-abstracts huggingface dataset:
Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation
Authors: German Ros, Simon Stent, Pablo F. Alcantarilla, Tomoki Watanabe
Date: 2016-04-06 00:00:00
Tasks: Semantic Segmentation

In this work we investigate the problem of road scene semantic segmentation
using Deconvolutional Networks (DNs). Several constraints limit the practical
performance of DNs in this context: firstly, the paucity of existing pixel-wise
labelled training data, and secondly, the memory constraints of embedded
hardware, which rule out the practical use of state-of-the-art DN architectures
such as fully convolutional networks (FCN). To address the first constraint, we
introduce a Multi-Domain Road Scene Semantic Segmentation (MDRS3) dataset,
aggregating data from six existing densely and sparsely labelled datasets for
training our models, and two existing, separate datasets for testing their
generalisation performance. We show that, while MDRS3 offers a greater volume
and variety of data, end-to-end training of a memory efficient DN does not
yield satisfactory performance. We propose a new training strategy to overcome
this, based on (i) the creation of a best-possible source network (S-Net) from
the aggregated data, ignoring time and memory constraints; and (ii) the
transfer of knowledge from S-Net to the memory-efficient target network
(T-Net). We evaluate different techniques for S-Net creation and T-Net
transferral, and demonstrate that training a constrained deconvolutional
network in this manner can unlock better performance than existing training
approaches. Specifically, we show that a target network can be trained to
achieve improved accuracy versus an FCN despite using less than 1\% of the
memory. We believe that our approach can be useful beyond automotive scenarios
where labelled data is similarly scarce or fragmented and where practical
constraints exist on the desired model size. We make available our network
models and aggregated multi-domain dataset for reproducibility.
Generating Chinese Classical Poems with RNN Encoder-Decoder
Authors: Xiaoyuan Yi, Ruoyu Li, Maosong Sun
Date: 2016-04-06 00:00:00
Tasks: Decoder

We take the generation of Chinese classical poem lines as a
sequence-to-sequence learning problem, and build a novel system based on the
RNN Encoder-Decoder structure to generate quatrains (Jueju in Chinese), with a
topic word as input. Our system can jointly learn semantic meaning within a
single line, semantic relevance among lines in a poem, and the use of
structural, rhythmical and tonal patterns, without utilizing any constraint
templates. Experimental results show that our system outperforms other
competitive systems. We also find that the attention mechanism can capture the
word associations in Chinese classical poetry and inverting target lines in
training can improve performance.
Accurate and scalable social recommendation using mixed-membership stochastic block models
Authors: Antonia Godoy-Lorite, Roger Guimera, Cristopher Moore, Marta Sales-Pardo
Date: 2016-04-05 00:00:00
Tasks: Articles, Collaborative Filtering

With ever-increasing amounts of online information available, modeling and
predicting individual preferences-for books or articles, for example-is
becoming more and more important. Good predictions enable us to improve advice
to users, and obtain a better understanding of the socio-psychological
processes that determine those preferences. We have developed a collaborative
filtering model, with an associated scalable algorithm, that makes accurate
predictions of individuals' preferences. Our approach is based on the explicit
assumption that there are groups of individuals and of items, and that the
preferences of an individual for an item are determined only by their group
memberships. Importantly, we allow each individual and each item to belong
simultaneously to mixtures of different groups and, unlike many popular
approaches, such as matrix factorization, we do not assume implicitly or
explicitly that individuals in each group prefer items in a single group of
items. The resulting overlapping groups and the predicted preferences can be
inferred with a expectation-maximization algorithm whose running time scales
linearly (per iteration). Our approach enables us to predict individual
preferences in large datasets, and is considerably more accurate than the
current algorithms for such large datasets.
Simple and Efficient Learning using Privileged Information
Authors: Xinxing Xu, Joey Tianyi Zhou, IvorW. Tsang, Zheng Qin, Rick Siow Mong Goh, Yong liu
Date: 2016-04-06 00:00:00
Tasks: Form, Image Categorization

The Support Vector Machine using Privileged Information (SVM+) has been
proposed to train a classifier to utilize the additional privileged information
that is only available in the training phase but not available in the test
phase. In this work, we propose an efficient solution for SVM+ by simply
utilizing the squared hinge loss instead of the hinge loss as in the existing
SVM+ formulation, which interestingly leads to a dual form with less variables
and in the same form with the dual of the standard SVM. The proposed algorithm
is utilized to leverage the additional web knowledge that is only available
during training for the image categorization tasks. The extensive experimental
results on both Caltech101 andWebQueries datasets show that our proposed method
can achieve a factor of up to hundred times speedup with the comparable
accuracy when compared with the existing SVM+ method.
Comments on: "A Random Forest Guided Tour" by G. Biau and E. Scornet
Authors: Sylvain Arlot, Robin Genuer
Date: 2016-04-06 00:00:00
Tasks: Survey

This paper is a comment on the survey paper by Biau and Scornet (2016) about
random forests. We focus on the problem of quantifying the impact of each
ingredient of random forests on their performance. We show that such a
quantification is possible for a simple pure forest , leading to conclusions
that could apply more generally. Then, we consider "hold-out" random forests,
which are a good middle point between "toy" pure forests and Breiman's original
random forests.
LOMo: Latent Ordinal Model for Facial Analysis in Videos
Authors: Karan Sikka, Gaurav Sharma, Marian Bartlett
Date: 2016-04-06 00:00:00
Proceeding: CVPR 2016 6
Tasks: Multiple Instance Learning, Weakly-supervised Learning

We study the problem of facial analysis in videos. We propose a novel weakly
supervised learning method that models the video event (expression, pain etc.)
as a sequence of automatically mined, discriminative sub-events (eg. onset and
offset phase for smile, brow lower and cheek raise for pain). The proposed
model is inspired by the recent works on Multiple Instance Learning and latent
SVM/HCRF- it extends such frameworks to model the ordinal or temporal aspect in
the videos, approximately. We obtain consistent improvements over relevant
competitive baselines on four challenging and publicly available video based
facial analysis datasets for prediction of expression, clinical pain and intent
in dyadic conversations. In combination with complimentary features, we report
state-of-the-art results on these datasets.
Parameterized Analysis of Multi-objective Evolutionary Algorithms and the Weighted Vertex Cover Problem
Authors: Mojgan Pourhassan, Feng Shi, Frank Neumann
Date: 2016-04-06 00:00:00
Tasks: Diversity, Evolutionary Algorithms

A rigorous runtime analysis of evolutionary multi-objective optimization for
the classical vertex cover problem in the context of parameterized complexity
analysis has been presented by Kratsch and Neumann (2013). In this paper, we
extend the analysis to the weighted vertex cover problem and provide a fixed
parameter evolutionary algorithm with respect to OPT, the cost of the the
optimal solution for the problem. Moreover, using a diversity mechanisms, we
present a multi-objective evolutionary algorithm that finds a 2-approximation
in expected polynomial time and introduce a population-based evolutionary
algorithm which finds a $(1+\varepsilon)$-approximation in expected time
$O(n\cdot 2^{\min \{n,2(1- \varepsilon)OPT \}} + n^3)$.
Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs
Authors: Wei Shen, Kai Zhao, Yuan Jiang, Yan Wang, Zhijiang Zhang, Xiang Bai
Date: 2016-03-31 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object, object-detection, Object Detection

Object skeleton is a useful cue for object detection, complementary to the
object contour, as it provides a structural representation to describe the
relationship among object parts. While object skeleton extraction in natural
images is a very challenging problem, as it requires the extractor to be able
to capture both local and global image context to determine the intrinsic scale
of each skeleton pixel. Existing methods rely on per-pixel based multi-scale
feature computation, which results in difficult modeling and high time
consumption. In this paper, we present a fully convolutional network with
multiple scale-associated side outputs to address this problem. By observing
the relationship between the receptive field sizes of the sequential stages in
the network and the skeleton scales they can capture, we introduce a
scale-associated side output to each stage. We impose supervision to different
stages by guiding the scale-associated side outputs toward groundtruth
skeletons of different scales. The responses of the multiple scale-associated
side outputs are then fused in a scale-specific way to localize skeleton pixels
with multiple scales effectively. Our method achieves promising results on two
skeleton extraction datasets, and significantly outperforms other competitors.
Keyboard Based Control of Four Dimensional Rotations
Authors: Akira Kageyama
Date: 2016-04-06 00:00:00

Aiming at applications to the scientific visualization of three dimensional
simulations with time evolution, a keyboard based control method to specify
rotations in four dimensions is proposed. It is known that four dimensional
rotations are generally so-called double rotations, and a double rotation is a
combination of simultaneously applied two simple rotations. The proposed method
can specify both the simple and double rotations by single key typings of the
keyboard. The method is tested in visualizations of a regular pentachoron in
four dimensional space by a hyperplane slicing.
A Focused Dynamic Attention Model for Visual Question Answering
Authors: Ilija Ilievski, Shuicheng Yan, Jiashi Feng
Date: 2016-04-06 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual Question and Answering (VQA) problems are attracting increasing
interest from multiple research disciplines. Solving VQA problems requires
techniques from both computer vision for understanding the visual contents of a
presented image or video, as well as the ones from natural language processing
for understanding semantics of the question and generating the answers.
Regarding visual content modeling, most of existing VQA methods adopt the
strategy of extracting global features from the image or video, which
inevitably fails in capturing fine-grained information such as spatial
configuration of multiple objects. Extracting features from auto-generated
regions -- as some region-based image recognition methods do -- cannot
essentially address this problem and may introduce some overwhelming irrelevant
features with the question. In this work, we propose a novel Focused Dynamic
Attention (FDA) model to provide better aligned image content representation
with proposed questions. Being aware of the key words in the question, FDA
employs off-the-shelf object detector to identify important regions and fuse
the information from the regions and global features via an LSTM unit. Such
question-driven representations are then combined with question representation
and fed into a reasoning unit for generating the answers. Extensive evaluation
on a large-scale benchmark dataset, VQA, clearly demonstrate the superior
performance of FDA over well-established baselines.
Non-iterative rigid 2D/3D point-set registration using semidefinite programming
Authors: Yuehaw Khoo, Ankur Kapoor
Date: 2015-01-04 00:00:00
Tasks: Pose Estimation

We describe a convex programming framework for pose estimation in 2D/3D
point-set registration with unknown point correspondences. We give two
mixed-integer nonlinear program (MINP) formulations of the 2D/3D registration
problem when there are multiple 2D images, and propose convex relaxations for
both of the MINPs to semidefinite programs (SDP) that can be solved efficiently
by interior point methods. Our approach to the 2D/3D registration problem is
non-iterative in nature as we jointly solve for pose and correspondence.
Furthermore, these convex programs can readily incorporate feature descriptors
of points to enhance registration results. We prove that the convex programs
exactly recover the solution to the original nonconvex 2D/3D registration
problem under noiseless condition. We apply these formulations to the
registration of 3D models of coronary vessels to their 2D projections obtained
from multiple intra-operative fluoroscopic images. For this application, we
experimentally corroborate the exact recovery property in the absence of noise
and further demonstrate robustness of the convex programs in the presence of
noise.
Learning A Deep $\ell_\infty$ Encoder for Hashing
Authors: Zhangyang Wang, Yingzhen Yang, Shiyu Chang, Qing Ling, Thomas S. Huang
Date: 2016-04-06 00:00:00
Tasks: Quantization

We investigate the $\ell_\infty$-constrained representation which
demonstrates robustness to quantization errors, utilizing the tool of deep
learning. Based on the Alternating Direction Method of Multipliers (ADMM), we
formulate the original convex minimization problem as a feed-forward neural
network, named \textit{Deep $\ell_\infty$ Encoder}, by introducing the novel
Bounded Linear Unit (BLU) neuron and modeling the Lagrange multipliers as
network biases. Such a structural prior acts as an effective network
regularization, and facilitates the model initialization. We then investigate
the effective use of the proposed model in the application of hashing, by
coupling the proposed encoders under a supervised pairwise loss, to develop a
\textit{Deep Siamese $\ell_\infty$ Network}, which can be optimized from end to
end. Extensive experiments demonstrate the impressive performances of the
proposed model. We also provide an in-depth analysis of its behaviors against
the competitors.
Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks
Authors: Haonan Yu, Jiang Wang, Zhiheng Huang, Yi Yang, Wei Xu
Date: 2015-10-26 00:00:00
Proceeding: CVPR 2016 6
Tasks: Sentence, Video Captioning

We present an approach that exploits hierarchical Recurrent Neural Networks
(RNNs) to tackle the video captioning problem, i.e., generating one or multiple
sentences to describe a realistic video. Our hierarchical framework contains a
sentence generator and a paragraph generator. The sentence generator produces
one simple short sentence that describes a specific short video interval. It
exploits both temporal- and spatial-attention mechanisms to selectively focus
on visual elements during generation. The paragraph generator captures the
inter-sentence dependency by taking as input the sentential embedding produced
by the sentence generator, combining it with the paragraph history, and
outputting the new initial state for the sentence generator. We evaluate our
approach on two large-scale benchmark datasets: YouTubeClips and
TACoS-MultiLevel. The experiments demonstrate that our approach significantly
outperforms the current state-of-the-art methods with BLEU@4 scores 0.499 and
0.305 respectively.
Template Adaptation for Face Verification and Identification
Authors: Nate Crosswhite, Jeffrey Byrne, Omkar M. Parkhi, Chris Stauffer, Qiong Cao, Andrew Zisserman
Date: 2016-03-12 00:00:00
Tasks: Face Identification, Face Recognition, Face Verification, Metric Learning, Transfer Learning

Face recognition performance evaluation has traditionally focused on
one-to-one verification, popularized by the Labeled Faces in the Wild dataset
for imagery and the YouTubeFaces dataset for videos. In contrast, the newly
released IJB-A face recognition dataset unifies evaluation of one-to-many face
identification with one-to-one face verification over templates, or sets of
imagery and videos for a subject. In this paper, we study the problem of
template adaptation, a form of transfer learning to the set of media in a
template. Extensive performance evaluations on IJB-A show a surprising result,
that perhaps the simplest method of template adaptation, combining deep
convolutional network features with template specific linear SVMs, outperforms
the state-of-the-art by a wide margin. We study the effects of template size,
negative set construction and classifier fusion on performance, then compare
template adaptation to convolutional networks with metric learning, 2D and 3D
alignment. Our unexpected conclusion is that these other methods, when combined
with template adaptation, all achieve nearly the same top performance on IJB-A
for template-based face verification and identification.
Highly accurate gaze estimation using a consumer RGB-depth sensor
Authors: Reza Shoja Ghiass, Ognjen Arandjelovic
Date: 2016-04-05 00:00:00
Tasks: Gaze Estimation, Head Pose Estimation, Pose Estimation

Determining the direction in which a person is looking is an important
problem in a wide range of HCI applications. In this paper we describe a highly
accurate algorithm that performs gaze estimation using an affordable and widely
available device such as Kinect. The method we propose starts by performing
accurate head pose estimation achieved by fitting a person specific morphable
model of the face to depth data. The ordinarily competing requirements of high
accuracy and high speed are met concurrently by formulating the fitting
objective function as a combination of terms which excel either in accurate or
fast fitting, and then by adaptively adjusting their relative contributions
throughout fitting. Following pose estimation, pose normalization is done by
re-rendering the fitted model as a frontal face. Finally gaze estimates are
obtained through regression from the appearance of the eyes in synthetic,
normalized images. Using EYEDIAP, the standard public dataset for the
evaluation of gaze estimation algorithms from RGB-D data, we demonstrate that
our method greatly outperforms the state of the art.
A Hierarchical Deep Temporal Model for Group Activity Recognition
Authors: Moustafa Ibrahim, Srikanth Muralidharan, Zhiwei Deng, Arash Vahdat, Greg Mori
Date: 2015-11-19 00:00:00
Proceeding: CVPR 2016 6
Tasks: Activity Recognition, Group Activity Recognition

In group activity recognition, the temporal dynamics of the whole activity
can be inferred based on the dynamics of the individual people representing the
activity. We build a deep model to capture these dynamics based on LSTM
(long-short term memory) models. To make use of these ob- servations, we
present a 2-stage deep temporal model for the group activity recognition
problem. In our model, a LSTM model is designed to represent action dynamics of
in- dividual people in a sequence and another LSTM model is designed to
aggregate human-level information for whole activity understanding. We evaluate
our model over two datasets: the collective activity dataset and a new volley-
ball dataset. Experimental results demonstrate that our proposed model improves
group activity recognition perfor- mance with compared to baseline methods.
dMath: A Scalable Linear Algebra and Math Library for Heterogeneous GP-GPU Architectures
Authors: Steven Eliuk, Cameron Upright, Anthony Skjellum
Date: 2016-04-05 00:00:00
Tasks: GPU, Management, Math

A new scalable parallel math library, dMath, is presented in this paper that
demonstrates leading scaling when using intranode, or internode,
hybrid-parallelism for deep-learning. dMath provides easy-to-use distributed
base primitives and a variety of domain-specific algorithms. These include
matrix multiplication, convolutions, and others allowing for rapid development
of highly scalable applications, including Deep Neural Networks (DNN), whereas
previously one was restricted to libraries that provided effective primitives
for only a single GPU, like Nvidia cublas and cudnn or DNN primitives from
Nervana neon framework. Development of HPC software is difficult,
labor-intensive work, requiring a unique skill set. dMath allows a wide range
of developers to utilize parallel and distributed hardware easily. One
contribution of this approach is that data is stored persistently on the GPU
hardware, avoiding costly transfers between host and device. Advanced memory
management techniques are utilized, including caching of transferred data and
memory reuse through pooling. A key contribution of dMath is that it delivers
performance, portability, and productivity to its specific domain of support.
It enables algorithm and application programmers to quickly solve problems
without managing the significant complexity associated with multi-level
parallelism.
Heavy hitters via cluster-preserving clustering
Authors: Kasper Green Larsen, Jelani Nelson, Huy L. Nguyen, Mikkel Thorup
Date: 2016-04-05 00:00:00
Tasks: Clustering

In turnstile $\ell_p$ $\varepsilon$-heavy hitters, one maintains a
high-dimensional $x\in\mathbb{R}^n$ subject to $\texttt{update}(i,\Delta)$
causing $x_i\leftarrow x_i + \Delta$, where $i\in[n]$, $\Delta\in\mathbb{R}$.
Upon receiving a query, the goal is to report a small list $L\subset[n]$, $|L|
= O(1/\varepsilon^p)$, containing every "heavy hitter" $i\in[n]$ with $|x_i|
\ge \varepsilon \|x_{\overline{1/\varepsilon^p}}\|_p$, where $x_{\overline{k}}$
denotes the vector obtained by zeroing out the largest $k$ entries of $x$ in
magnitude.
  For any $p\in(0,2]$ the CountSketch solves $\ell_p$ heavy hitters using
$O(\varepsilon^{-p}\log n)$ words of space with $O(\log n)$ update time,
$O(n\log n)$ query time to output $L$, and whose output after any query is
correct with high probability (whp) $1 - 1/poly(n)$. Unfortunately the query
time is very slow. To remedy this, the work [CM05] proposed for $p=1$ in the
strict turnstile model, a whp correct algorithm achieving suboptimal space
$O(\varepsilon^{-1}\log^2 n)$, worse update time $O(\log^2 n)$, but much better
query time $O(\varepsilon^{-1}poly(\log n))$.
  We show this tradeoff between space and update time versus query time is
unnecessary. We provide a new algorithm, ExpanderSketch, which in the most
general turnstile model achieves optimal $O(\varepsilon^{-p}\log n)$ space,
$O(\log n)$ update time, and fast $O(\varepsilon^{-p}poly(\log n))$ query time,
and whp correctness. Our main innovation is an efficient reduction from the
heavy hitters to a clustering problem in which each heavy hitter is encoded as
some form of noisy spectral cluster in a much bigger graph, and the goal is to
identify every cluster. Since every heavy hitter must be found, correctness
requires that every cluster be found. We then develop a "cluster-preserving
clustering" algorithm, partitioning the graph into clusters without destroying
any original cluster.