pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Disambiguate by Asking Discriminative Questions
Authors: Yining Li, Chen Huang, Xiaoou Tang, Chen-Change Loy
Date: 2017-08-09 00:00:00
Proceeding: ICCV 2017 10
Tasks: Benchmarking, Image Captioning, Question Answering, Question Generation, Question-Generation, Visual Question Answering, Visual Question Answering (VQA)

The ability to ask questions is a powerful tool to gather information in
order to learn about the world and resolve ambiguities. In this paper, we
explore a novel problem of generating discriminative questions to help
disambiguate visual instances. Our work can be seen as a complement and new
extension to the rich research studies on image captioning and question
answering. We introduce the first large-scale dataset with over 10,000
carefully annotated images-question tuples to facilitate benchmarking. In
particular, each tuple consists of a pair of images and 4.6 discriminative
questions (as positive samples) and 5.9 non-discriminative questions (as
negative samples) on average. In addition, we present an effective method for
visual discriminative question generation. The method can be trained in a
weakly supervised manner without discriminative images-question tuples but just
existing visual question answering datasets. Promising results are shown
against representative baselines through quantitative evaluations and user
studies.
Isointense infant brain MRI segmentation with a dilated convolutional neural network
Authors: Pim Moeskops, Josien P. W. Pluim
Date: 2017-08-09 00:00:00
Tasks: Infant Brain Mri Segmentation, MRI segmentation, Segmentation

Quantitative analysis of brain MRI at the age of 6 months is difficult
because of the limited contrast between white matter and gray matter. In this
study, we use a dilated triplanar convolutional neural network in combination
with a non-dilated 3D convolutional neural network for the segmentation of
white matter, gray matter and cerebrospinal fluid in infant brain MR images, as
provided by the MICCAI grand challenge on 6-month infant brain MRI
segmentation.
Extreme clicking for efficient object annotation
Authors: Dim P. Papadopoulos, Jasper R. R. Uijlings, Frank Keller, Vittorio Ferrari
Date: 2017-08-09 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object

Manually annotating object bounding boxes is central to building computer
vision datasets, and it is very time consuming (annotating ILSVRC [53] took 35s
for one high-quality box [62]). It involves clicking on imaginary corners of a
tight box around the object. This is difficult as these corners are often
outside the actual object and several adjustments are required to obtain a
tight box. We propose extreme clicking instead: we ask the annotator to click
on four physical points on the object: the top, bottom, left- and right-most
points. This task is more natural and these points are easy to find. We
crowd-source extreme point annotations for PASCAL VOC 2007 and 2012 and show
that (1) annotation time is only 7s per box, 5x faster than the traditional way
of drawing boxes [62]; (2) the quality of the boxes is as good as the original
ground-truth drawn the traditional way; (3) detectors trained on our
annotations are as accurate as those trained on the original ground-truth.
Moreover, our extreme clicking strategy not only yields box coordinates, but
also four accurate boundary points. We show (4) how to incorporate them into
GrabCut to obtain more accurate segmentations than those delivered when
initializing it from bounding boxes; (5) semantic segmentations models trained
on these segmentations outperform those trained on segmentations derived from
bounding boxes.
Convergence of Unregularized Online Learning Algorithms
Authors: Yunwen Lei, Lei Shi, Zheng-Chu Guo
Date: 2017-08-09 00:00:00

In this paper we study the convergence of online gradient descent algorithms
in reproducing kernel Hilbert spaces (RKHSs) without regularization. We
establish a sufficient condition and a necessary condition for the convergence
of excess generalization errors in expectation. A sufficient condition for the
almost sure convergence is also given. With high probability, we provide
explicit convergence rates of the excess generalization errors for both
averaged iterates and the last iterate, which in turn also imply convergence
rates with probability one. To our best knowledge, this is the first
high-probability convergence rate for the last iterate of online gradient
descent algorithms without strong convexity. Without any boundedness
assumptions on iterates, our results are derived by a novel use of two measures
of the algorithm's one-step progress, respectively by generalization errors and
by distances in RKHSs, where the variances of the involved martingales are
cancelled out by the descent property of the algorithm.
A Data Prism: Semi-Verified Learning in the Small-Alpha Regime
Authors: Michela Meister, Gregory Valiant
Date: 2017-08-09 00:00:00

We consider a model of unreliable or crowdsourced data where there is an
underlying set of $n$ binary variables, each evaluator contributes a (possibly
unreliable or adversarial) estimate of the values of some subset of $r$ of the
variables, and the learner is given the true value of a constant number of
variables. We show that, provided an $\alpha$-fraction of the evaluators are
"good" (either correct, or with independent noise rate $p < 1/2$), then the
true values of a $(1-\epsilon)$ fraction of the $n$ underlying variables can be
deduced as long as $\alpha > 1/(2-2p)^r$. This setting can be viewed as an
instance of the semi-verified learning model introduced in [CSV17], which
explores the tradeoff between the number of items evaluated by each worker and
the fraction of good evaluators. Our results require the number of evaluators
to be extremely large, $>n^r$, although our algorithm runs in linear time,
$O_{r,\epsilon}(n)$, given query access to the large dataset of evaluations.
This setting and results can also be viewed as examining a general class of
semi-adversarial CSPs with a planted assignment.
  This parameter regime where the fraction of reliable data is small, is
relevant to a number of practical settings. For example, settings where one has
a large dataset of customer preferences, with each customer specifying
preferences for a small (constant) number of items, and the goal is to
ascertain the preferences of a specific demographic of interest. Our results
show that this large dataset (which lacks demographic information) can be
leveraged together with the preferences of the demographic of interest for a
constant number of randomly selected items, to recover an accurate estimate of
the entire set of preferences. In this sense, our results can be viewed as a
"data prism" allowing one to extract the behavior of specific cohorts from a
large, mixed, dataset.
Gaussian Prototypical Networks for Few-Shot Learning on Omniglot
Authors: Stanislav Fort
Date: 2017-08-09 00:00:00
Proceeding: ICLR 2018 1
Tasks: Classification, Clustering, Few-Shot Learning, General Classification

We propose a novel architecture for $k$-shot classification on the Omniglot
dataset. Building on prototypical networks, we extend their architecture to
what we call Gaussian prototypical networks. Prototypical networks learn a map
between images and embedding vectors, and use their clustering for
classification. In our model, a part of the encoder output is interpreted as a
confidence region estimate about the embedding point, and expressed as a
Gaussian covariance matrix. Our network then constructs a direction and class
dependent distance metric on the embedding space, using uncertainties of
individual data points as weights. We show that Gaussian prototypical networks
are a preferred architecture over vanilla prototypical networks with an
equivalent number of parameters. We report state-of-the-art performance in
1-shot and 5-shot classification both in 5-way and 20-way regime (for 5-shot
5-way, we are comparable to previous state-of-the-art) on the Omniglot dataset.
We explore artificially down-sampling a fraction of images in the training set,
which improves our performance even further. We therefore hypothesize that
Gaussian prototypical networks might perform better in less homogeneous,
noisier datasets, which are commonplace in real world applications.
Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition
Authors: Feng Liu, Qijun Zhao, Xiaoming Liu, Dan Zeng
Date: 2017-08-09 00:00:00
Tasks: 3D Face Reconstruction, Face Alignment, Face Recognition, Face Reconstruction

Face alignment and 3D face reconstruction are traditionally accomplished as
separated tasks. By exploring the strong correlation between 2D landmarks and
3D shapes, in contrast, we propose a joint face alignment and 3D face
reconstruction method to simultaneously solve these two problems for 2D face
images of arbitrary poses and expressions. This method, based on a summation
model of 3D faces and cascaded regression in 2D and 3D shape spaces,
iteratively and alternately applies two cascaded regressors, one for updating
2D landmarks and the other for 3D shape. The 3D shape and the landmarks are
correlated via a 3D-to-2D mapping matrix, which is updated in each iteration to
refine the location and visibility of 2D landmarks. Unlike existing methods,
the proposed method can fully automatically generate both
pose-and-expression-normalized (PEN) and expressive 3D faces and localize both
visible and invisible 2D landmarks. Based on the PEN 3D faces, we devise a
method to enhance face recognition accuracy across poses and expressions. Both
linear and nonlinear implementations of the proposed method are presented and
evaluated in this paper. Extensive experiments show that the proposed method
can achieve the state-of-the-art accuracy in both face alignment and 3D face
reconstruction, and benefit face recognition owing to its reconstructed PEN 3D
face.
Probabilistic Neural Network with Complex Exponential Activation Functions in Image Recognition using Deep Learning Framework
Authors: Andrey Savchenko
Date: 2017-08-09 00:00:00
Tasks: Face Recognition, General Classification, Transfer Learning

If the training dataset is not very large, image recognition is usually
implemented with the transfer learning methods. In these methods the features
are extracted using a deep convolutional neural network, which was
preliminarily trained with an external very-large dataset. In this paper we
consider the nonparametric classification of extracted feature vectors with the
probabilistic neural network (PNN). The number of neurons at the pattern layer
of the PNN is equal to the database size, which causes the low recognition
performance and high memory space complexity of this network. We propose to
overcome these drawbacks by replacing the exponential activation function in
the Gaussian Parzen kernel to the complex exponential functions in the Fej\'er
kernel. We demonstrate that in this case it is possible to implement the
network with the number of neurons in the pattern layer proportional to the
cubic root of the database size. Thus, the proposed modification of the PNN
makes it possible to significantly decrease runtime and memory complexities
without loosing its main advantages, namely, extremely fast training procedure
and the convergence to the optimal Bayesian decision. An experimental study in
visual object category classification and unconstrained face recognition with
contemporary deep neural networks have shown, that our approach obtains very
efficient and rather accurate decisions for the small training sample in
comparison with the well-known classifiers.
Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting
Authors: Donghyeon Cho, Jinsun Park, Tae-Hyun Oh, Yu-Wing Tai, In So Kweon
Date: 2017-08-09 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Retargeting, Self-Supervised Learning

This paper proposes a weakly- and self-supervised deep convolutional neural
network (WSSDCNN) for content-aware image retargeting. Our network takes a
source image and a target aspect ratio, and then directly outputs a retargeted
image. Retargeting is performed through a shift map, which is a pixel-wise
mapping from the source to the target grid. Our method implicitly learns an
attention map, which leads to a content-aware shift map for image retargeting.
As a result, discriminative parts in an image are preserved, while background
regions are adjusted seamlessly. In the training phase, pairs of an image and
its image-level annotation are used to compute content and structure losses. We
demonstrate the effectiveness of our proposed method for a retargeting
application with insightful analyses.
DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
Authors: Zili Yi, Hao Zhang, Ping Tan, Minglun Gong
Date: 2017-04-08 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image-to-Image Translation, Translation

Conditional Generative Adversarial Networks (GANs) for cross-domain
image-to-image translation have made much progress recently. Depending on the
task complexity, thousands to millions of labeled image pairs are needed to
train a conditional GAN. However, human labeling is expensive, even
impractical, and large quantities of data may not always be available. Inspired
by dual learning from natural language translation, we develop a novel dual-GAN
mechanism, which enables image translators to be trained from two sets of
unlabeled images from two domains. In our architecture, the primal GAN learns
to translate images from domain U to those in domain V, while the dual GAN
learns to invert the task. The closed loop made by the primal and dual tasks
allows images from either domain to be translated and then reconstructed. Hence
a loss function that accounts for the reconstruction error of images can be
used to train the translators. Experiments on multiple image translation tasks
with unlabeled data show considerable performance gain of DualGAN over a single
GAN. For some tasks, DualGAN can even achieve comparable or slightly better
results than conditional GAN trained on fully labeled data.
Optimal Identity Testing with High Probability
Authors: Ilias Diakonikolas, Themis Gouleakis, John Peebles, Eric Price
Date: 2017-08-09 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We study the problem of testing identity against a given distribution with a
focus on the high confidence regime. More precisely, given samples from an
unknown distribution $p$ over $n$ elements, an explicitly given distribution
$q$, and parameters $0< \epsilon, \delta < 1$, we wish to distinguish, {\em
with probability at least $1-\delta$}, whether the distributions are identical
versus $\varepsilon$-far in total variation distance. Most prior work focused
on the case that $\delta = \Omega(1)$, for which the sample complexity of
identity testing is known to be $\Theta(\sqrt{n}/\epsilon^2)$. Given such an
algorithm, one can achieve arbitrarily small values of $\delta$ via black-box
amplification, which multiplies the required number of samples by
$\Theta(\log(1/\delta))$.
  We show that black-box amplification is suboptimal for any $\delta = o(1)$,
and give a new identity tester that achieves the optimal sample complexity. Our
new upper and lower bounds show that the optimal sample complexity of identity
testing is \[
  \Theta\left( \frac{1}{\epsilon^2}\left(\sqrt{n \log(1/\delta)} +
\log(1/\delta) \right)\right) \] for any $n, \varepsilon$, and $\delta$. For
the special case of uniformity testing, where the given distribution is the
uniform distribution $U_n$ over the domain, our new tester is surprisingly
simple: to test whether $p = U_n$ versus $d_{\mathrm TV}(p, U_n) \geq
\varepsilon$, we simply threshold $d_{\mathrm TV}(\widehat{p}, U_n)$, where
$\widehat{p}$ is the empirical probability distribution. The fact that this
simple "plug-in" estimator is sample-optimal is surprising, even in the
constant $\delta$ case. Indeed, it was believed that such a tester would not
attain sublinear sample complexity even for constant values of $\varepsilon$
and $\delta$.
Sequential Dual Deep Learning with Shape and Texture Features for Sketch Recognition
Authors: Qi Jia, Meiyu Yu, Xin Fan, Haojie Li
Date: 2017-08-09 00:00:00
Tasks: Sketch Recognition

Recognizing freehand sketches with high arbitrariness is greatly challenging.
Most existing methods either ignore the geometric characteristics or treat
sketches as handwritten characters with fixed structural ordering.
Consequently, they can hardly yield high recognition performance even though
sophisticated learning techniques are employed. In this paper, we propose a
sequential deep learning strategy that combines both shape and texture
features. A coded shape descriptor is exploited to characterize the geometry of
sketch strokes with high flexibility, while the outputs of constitutional
neural networks (CNN) are taken as the abstract texture feature. We develop
dual deep networks with memorable gated recurrent units (GRUs), and
sequentially feed these two types of features into the dual networks,
respectively. These dual networks enable the feature fusion by another gated
recurrent unit (GRU), and thus accurately recognize sketches invariant to
stroke ordering. The experiments on the TU-Berlin data set show that our method
outperforms the average of human and state-of-the-art algorithms even when
significant shape and appearance variations occur.
Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
Authors: Damien Teney, Peter Anderson, Xiaodong He, Anton Van Den Hengel
Date: 2017-08-09 00:00:00
Proceeding: CVPR 2018 6
Tasks: GPU, Visual Question Answering, Visual Question Answering (VQA)

This paper presents a state-of-the-art model for visual question answering
(VQA), which won the first place in the 2017 VQA Challenge. VQA is a task of
significant importance for research in artificial intelligence, given its
multimodal nature, clear evaluation protocol, and potential real-world
applications. The performance of deep neural networks for VQA is very dependent
on choices of architectures and hyperparameters. To help further research in
the area, we describe in detail our high-performing, though relatively simple
model. Through a massive exploration of architectures and hyperparameters
representing more than 3,000 GPU-hours, we identified tips and tricks that lead
to its success, namely: sigmoid outputs, soft training targets, image features
from bottom-up attention, gated tanh activations, output embeddings initialized
using GloVe and Google Images, large mini-batches, and smart shuffling of
training data. We provide a detailed analysis of their impact on performance to
assist others in making an appropriate selection.
Enabling Massive Deep Neural Networks with the GraphBLAS
Authors: Jeremy Kepner, Manoj Kumar, José Moreira, Pratap Pattnaik, Mauricio Serrano, Henry Tufo
Date: 2017-08-09 00:00:00
Tasks: Math

Deep Neural Networks (DNNs) have emerged as a core tool for machine learning.
The computations performed during DNN training and inference are dominated by
operations on the weight matrices describing the DNN. As DNNs incorporate more
stages and more nodes per stage, these weight matrices may be required to be
sparse because of memory limitations. The GraphBLAS.org math library standard
was developed to provide high performance manipulation of sparse weight
matrices and input/output vectors. For sufficiently sparse matrices, a sparse
matrix library requires significantly less memory than the corresponding dense
matrix implementation. This paper provides a brief description of the
mathematics underlying the GraphBLAS. In addition, the equations of a typical
DNN are rewritten in a form designed to use the GraphBLAS. An implementation of
the DNN is given using a preliminary GraphBLAS C library. The performance of
the GraphBLAS implementation is measured relative to a standard dense linear
algebra library implementation. For various sizes of DNN weight matrices, it is
shown that the GraphBLAS sparse implementation outperforms a BLAS dense
implementation as the weight matrix becomes sparser.
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network
Authors: Jared Katzman, Uri Shaham, Jonathan Bates, Alexander Cloninger, Tingting Jiang, Yuval Kluger
Date: 2016-06-02 00:00:00
Tasks: Feature Engineering, Predicting Patient Outcomes, Recommendation Systems, Survival Analysis

Medical practitioners use survival models to explore and understand the
relationships between patients' covariates (e.g. clinical and genetic features)
and the effectiveness of various treatment options. Standard survival models
like the linear Cox proportional hazards model require extensive feature
engineering or prior medical knowledge to model treatment interaction at an
individual level. While nonlinear survival methods, such as neural networks and
survival forests, can inherently model these high-level interaction terms, they
have yet to be shown as effective treatment recommender systems. We introduce
DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art
survival method for modeling interactions between a patient's covariates and
treatment effectiveness in order to provide personalized treatment
recommendations. We perform a number of experiments training DeepSurv on
simulated and real survival data. We demonstrate that DeepSurv performs as well
as or better than other state-of-the-art survival models and validate that
DeepSurv successfully models increasingly complex relationships between a
patient's covariates and their risk of failure. We then show how DeepSurv
models the relationship between a patient's features and effectiveness of
different treatment options to show how DeepSurv can be used to provide
individual treatment recommendations. Finally, we train DeepSurv on real
clinical studies to demonstrate how it's personalized treatment recommendations
would increase the survival time of a set of patients. The predictive and
modeling capabilities of DeepSurv will enable medical researchers to use deep
neural networks as a tool in their exploration, understanding, and prediction
of the effects of a patient's characteristics on their risk of failure.
What Actions are Needed for Understanding Human Actions in Videos?
Authors: Gunnar A. Sigurdsson, Olga Russakovsky, Abhinav Gupta
Date: 2017-08-09 00:00:00
Proceeding: ICCV 2017 10
Tasks: Benchmarking

What is the right way to reason about human activities? What directions
forward are most promising? In this work, we analyze the current state of human
activity understanding in videos. The goal of this paper is to examine
datasets, evaluation metrics, algorithms, and potential future directions. We
look at the qualitative attributes that define activities such as pose
variability, brevity, and density. The experiments consider multiple
state-of-the-art algorithms and multiple datasets. The results demonstrate that
while there is inherent ambiguity in the temporal extent of activities, current
datasets still permit effective benchmarking. We discover that fine-grained
understanding of objects and pose when combined with temporal reasoning is
likely to yield substantial improvements in algorithmic accuracy. We present
the many kinds of information that will be needed to achieve substantial gains
in activity understanding: objects, verbs, intent, and sequential reasoning.
The software and additional information will be made available to provide other
researchers detailed diagnostics to understand their own algorithms.