pwc-archive/papers-with-abstracts huggingface dataset:
A Differentially Private Kernel Two-Sample Test
Authors: Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, Mijung Park
Date: 2018-08-01 00:00:00
Tasks: Two-sample testing, Vocal Bursts Valence Prediction

Kernel two-sample testing is a useful statistical tool in determining whether
data samples arise from different distributions without imposing any parametric
assumptions on those distributions. However, raw data samples can expose
sensitive information about individuals who participate in scientific studies,
which makes the current tests vulnerable to privacy breaches. Hence, we design
a new framework for kernel two-sample testing conforming to differential
privacy constraints, in order to guarantee the privacy of subjects in the data.
Unlike existing differentially private parametric tests that simply add noise
to data, kernel-based testing imposes a challenge due to a complex dependence
of test statistics on the raw data, as these statistics correspond to
estimators of distances between representations of probability measures in
Hilbert spaces. Our approach considers finite dimensional approximations to
those representations. As a result, a simple chi-squared test is obtained,
where a test statistic depends on a mean and covariance of empirical
differences between the samples, which we perturb for a privacy guarantee. We
investigate the utility of our framework in two realistic settings and conclude
that our method requires only a relatively modest increase in sample size to
achieve a similar level of power to the non-private tests in both settings.
Deep Appearance Models for Face Rendering
Authors: Stephen Lombardi, Jason Saragih, Tomas Simon, Yaser Sheikh
Date: 2018-08-01 00:00:00

We introduce a deep appearance model for rendering the human face. Inspired
by Active Appearance Models, we develop a data-driven rendering pipeline that
learns a joint representation of facial geometry and appearance from a
multiview capture setup. Vertex positions and view-specific textures are
modeled using a deep variational autoencoder that captures complex nonlinear
effects while producing a smooth and compact latent representation.
View-specific texture enables the modeling of view-dependent effects such as
specularity. In addition, it can also correct for imperfect geometry stemming
from biased or low resolution estimates. This is a significant departure from
the traditional graphics pipeline, which requires highly accurate geometry as
well as all elements of the shading model to achieve realism through
physically-inspired light transport. Acquiring such a high level of accuracy is
difficult in practice, especially for complex and intricate parts of the face,
such as eyelashes and the oral cavity. These are handled naturally by our
approach, which does not rely on precise estimates of geometry. Instead, the
shading model accommodates deficiencies in geometry though the flexibility
afforded by the neural network employed. At inference time, we condition the
decoding network on the viewpoint of the camera in order to generate the
appropriate texture for rendering. The resulting system can be implemented
simply using existing rendering engines through dynamic textures with flat
lighting. This representation, together with a novel unsupervised technique for
mapping images to facial states, results in a system that is naturally suited
to real-time interactive settings such as Virtual Reality (VR).
Structured Differential Learning for Automatic Threshold Setting
Authors: Jonathan Connell, Benjamin Herta
Date: 2018-08-01 00:00:00

We introduce a technique that can automatically tune the parameters of a
rule-based computer vision system comprised of thresholds, combinational logic,
and time constants. This lets us retain the flexibility and perspicacity of a
conventionally structured system while allowing us to perform approximate
gradient descent using labeled data. While this is only a heuristic procedure,
as far as we are aware there is no other efficient technique for tuning such
systems. We describe the components of the system and the associated supervised
learning mechanism. We also demonstrate the utility of the algorithm by
comparing its performance versus hand tuning for an automotive headlight
controller. Despite having over 100 parameters, the method is able to
profitably adjust the system values given just the desired output for a number
of videos.
Imaginary Kinematics
Authors: Sabina Marchetti, Alessandro Antonucci
Date: 2018-08-01 00:00:00

We introduce a novel class of adjustment rules for a collection of beliefs.
This is an extension of Lewis' imaging to absorb probabilistic evidence in
generalized settings. Unlike standard tools for belief revision, our proposal
may be used when information is inconsistent with an agent's belief base. We
show that the functionals we introduce are based on the imaginary counterpart
of probability kinematics for standard belief revision, and prove that, under
certain conditions, all standard postulates for belief revision are satisfied.
Generative Adversarial Frontal View to Bird View Synthesis
Authors: Xinge Zhu, Zhichao Yin, Jianping Shi, Hongsheng Li, Dahua Lin
Date: 2018-08-01 00:00:00
Tasks: Bird View Synthesis, Homography Estimation, Translation

Environment perception is an important task with great practical value and
bird view is an essential part for creating panoramas of surrounding
environment. Due to the large gap and severe deformation between the frontal
view and bird view, generating a bird view image from a single frontal view is
challenging. To tackle this problem, we propose the BridgeGAN, i.e., a novel
generative model for bird view synthesis. First, an intermediate view, i.e.,
homography view, is introduced to bridge the large gap. Next, conditioned on
the three views (frontal view, homography view and bird view) in our task, a
multi-GAN based model is proposed to learn the challenging cross-view
translation. Extensive experiments conducted on a synthetic dataset have
demonstrated that the images generated by our model are much better than those
generated by existing methods, with more consistent global appearance and
sharper details. Ablation studies and discussions show its reliability and
robustness in some challenging cases.
A Network Structure to Explicitly Reduce Confusion Errors in Semantic Segmentation
Authors: Qichuan Geng, Xinyu Huang, Zhong Zhou, Ruigang Yang
Date: 2018-08-01 00:00:00
Tasks: Image Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation

Confusing classes that are ubiquitous in real world often degrade performance
for many vision related applications like object detection, classification, and
segmentation. The confusion errors are not only caused by similar visual
patterns but also amplified by various factors during the training of our
designed models, such as reduced feature resolution in the encoding process or
imbalanced data distributions. A large amount of deep learning based network
structures has been proposed in recent years to deal with these individual
factors and improve network performance. However, to our knowledge, no existing
work in semantic image segmentation is designed to tackle confusion errors
explicitly. In this paper, we present a novel and general network structure
that reduces confusion errors in more direct manner and apply the network for
semantic segmentation. There are two major contributions in our network
structure: 1) We ensemble subnets with heterogeneous output spaces based on the
discriminative confusing groups. The training for each subnet can distinguish
confusing classes within the group without affecting unrelated classes outside
the group. 2) We propose an improved cross-entropy loss function that maximizes
the probability assigned to the correct class and penalizes the probabilities
assigned to the confusing classes at the same time. Our network structure is a
general structure and can be easily adapted to any other networks to further
reduce confusion errors. Without any changes in the feature encoder and
post-processing steps, our experiments demonstrate consistent and significant
improvements on different baseline models on Cityscapes and PASCAL VOC datasets
(e.g., 3.05% over ResNet-101 and 1.30% over ResNet-38).
Model-order selection in statistical shape models
Authors: Alma Eguizabal, Peter J. Schreier, David Ramírez
Date: 2018-08-01 00:00:00
Tasks: model

Statistical shape models enhance machine learning algorithms providing prior
information about deformation. A Point Distribution Model (PDM) is a popular
landmark-based statistical shape model for segmentation. It requires choosing a
model order, which determines how much of the variation seen in the training
data is accounted for by the PDM. A good choice of the model order depends on
the number of training samples and the noise level in the training data set.
Yet the most common approach for choosing the model order simply keeps a
predetermined percentage of the total shape variation. In this paper, we
present a technique for choosing the model order based on information-theoretic
criteria, and we show empirical evidence that the model order chosen by this
technique provides a good trade-off between over- and underfitting.
Learning Visual Question Answering by Bootstrapping Hard Attention
Authors: Mateusz Malinowski, Carl Doersch, Adam Santoro, Peter Battaglia
Date: 2018-08-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Hard Attention, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Attention mechanisms in biological perception are thought to select subsets
of perceptual information for more sophisticated processing which would be
prohibitive to perform on all sensory inputs. In computer vision, however,
there has been relatively little exploration of hard attention, where some
information is selectively ignored, in spite of the success of soft attention,
where information is re-weighted and aggregated, but never filtered out. Here,
we introduce a new approach for hard attention and find it achieves very
competitive performance on a recently-released visual question answering
datasets, equalling and in some cases surpassing similar soft attention
architectures while entirely ignoring some features. Even though the hard
attention mechanism is thought to be non-differentiable, we found that the
feature magnitudes correlate with semantic relevance, and provide a useful
signal for our mechanism's attentional selection criterion. Because hard
attention selects important features of the input information, it can also be
more efficient than analogous soft attention mechanisms. This is especially
important for recent approaches that use non-local pairwise operations, whereby
computational and memory costs are quadratic in the size of the set of
features.
TraMNet - Transition Matrix Network for Efficient Action Tube Proposals
Authors: Gurkirt Singh, Suman Saha, Fabio Cuzzolin
Date: 2018-08-01 00:00:00

Current state-of-the-art methods solve spatiotemporal action localisation by
extending 2D anchors to 3D-cuboid proposals on stacks of frames, to generate
sets of temporally connected bounding boxes called \textit{action micro-tubes}.
However, they fail to consider that the underlying anchor proposal hypotheses
should also move (transition) from frame to frame, as the actor or the camera
does. Assuming we evaluate $n$ 2D anchors in each frame, then the number of
possible transitions from each 2D anchor to the next, for a sequence of $f$
consecutive frames, is in the order of $O(n^f)$, expensive even for small
values of $f$. To avoid this problem, we introduce a Transition-Matrix-based
Network (TraMNet) which relies on computing transition probabilities between
anchor proposals while maximising their overlap with ground truth bounding
boxes across frames, and enforcing sparsity via a transition threshold. As the
resulting transition matrix is sparse and stochastic, this reduces the proposal
hypothesis search space from $O(n^f)$ to the cardinality of the thresholded
matrix. At training time, transitions are specific to cell locations of the
feature maps, so that a sparse (efficient) transition matrix is used to train
the network. At test time, a denser transition matrix can be obtained either by
decreasing the threshold or by adding to it all the relative transitions
originating from any cell location, allowing the network to handle transitions
in the test data that might not have been present in the training data, and
making detection translation-invariant. Finally, we show that our network can
handle sparse annotations such as those available in the DALY dataset. We
report extensive experiments on the DALY, UCF101-24 and Transformed-UCF101-24
datasets to support our claims.
Attention-based Pyramid Aggregation Network for Visual Place Recognition
Authors: Yingying Zhu, Jiong Wang, Lingxi Xie, Liang Zheng
Date: 2018-08-01 00:00:00
Tasks: Image Retrieval, Retrieval, Visual Place Recognition

Visual place recognition is challenging in the urban environment and is
usually viewed as a large scale image retrieval task. The intrinsic challenges
in place recognition exist that the confusing objects such as cars and trees
frequently occur in the complex urban scene, and buildings with repetitive
structures may cause over-counting and the burstiness problem degrading the
image representations. To address these problems, we present an Attention-based
Pyramid Aggregation Network (APANet), which is trained in an end-to-end manner
for place recognition. One main component of APANet, the spatial pyramid
pooling, can effectively encode the multi-size buildings containing
geo-information. The other one, the attention block, is adopted as a region
evaluator for suppressing the confusing regional features while highlighting
the discriminative ones. When testing, we further propose a simple yet
effective PCA power whitening strategy, which significantly improves the widely
used PCA whitening by reasonably limiting the impact of over-counting.
Experimental evaluations demonstrate that the proposed APANet outperforms the
state-of-the-art methods on two place recognition benchmarks, and generalizes
well on standard image retrieval datasets.
Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs
Authors: Francisco M. Castro, Nicolás Guil, Manuel J. Marín-Jiménez, Jesús Pérez-Serrano, Manuel Ujaldón
Date: 2018-08-01 00:00:00
Tasks: GPU, Object Recognition

Deep Learning (DL) applications are gaining momentum in the realm of
Artificial Intelligence, particularly after GPUs have demonstrated remarkable
skills for accelerating their challenging computational requirements. Within
this context, Convolutional Neural Network (CNN) models constitute a
representative example of success on a wide set of complex applications,
particularly on datasets where the target can be represented through a
hierarchy of local features of increasing semantic complexity. In most of the
real scenarios, the roadmap to improve results relies on CNN settings involving
brute force computation, and researchers have lately proven Nvidia GPUs to be
one of the best hardware counterparts for acceleration. Our work complements
those findings with an energy study on critical parameters for the deployment
of CNNs on flagship image and video applications: object recognition and people
identification by gait, respectively. We evaluate energy consumption on four
different networks based on the two most popular ones (ResNet/AlexNet): ResNet
(167 layers), a 2D CNN (15 layers), a CaffeNet (25 layers) and a ResNetIm (94
layers) using batch sizes of 64, 128 and 256, and then correlate those with
speed-up and accuracy to determine optimal settings. Experimental results on a
multi-GPU server endowed with twin Maxwell and twin Pascal Titan X GPUs
demonstrate that energy correlates with performance and that Pascal may have up
to 40% gains versus Maxwell. Larger batch sizes extend performance gains and
energy savings, but we have to keep an eye on accuracy, which sometimes shows a
preference for small batches. We expect this work to provide a preliminary
guidance for a wide set of CNN and DL applications in modern HPC times, where
the GFLOPS/w ratio constitutes the primary goal.
Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm
Authors: Zechun Liu, Baoyuan Wu, Wenhan Luo, Xin Yang, Wei Liu, Kwang-Ting Cheng
Date: 2018-08-01 00:00:00
Proceeding: ECCV 2018 9

In this work, we study the 1-bit convolutional neural networks (CNNs), of
which both the weights and activations are binary. While being efficient, the
classification accuracy of the current 1-bit CNNs is much worse compared to
their counterpart real-valued CNN models on the large-scale dataset, like
ImageNet. To minimize the performance gap between the 1-bit and real-valued CNN
models, we propose a novel model, dubbed Bi-Real net, which connects the real
activations (after the 1-bit convolution and/or BatchNorm layer, before the
sign function) to activations of the consecutive block, through an identity
shortcut. Consequently, compared to the standard 1-bit CNN, the
representational capability of the Bi-Real net is significantly enhanced and
the additional cost on computation is negligible. Moreover, we develop a
specific training algorithm including three technical novelties for 1- bit
CNNs. Firstly, we derive a tight approximation to the derivative of the
non-differentiable sign function with respect to activation. Secondly, we
propose a magnitude-aware gradient with respect to the weight for updating the
weight parameters. Thirdly, we pre-train the real-valued CNN model with a clip
function, rather than the ReLU function, to better initialize the Bi-Real net.
Experiments on ImageNet show that the Bi-Real net with the proposed training
algorithm achieves 56.4% and 62.2% top-1 accuracy with 18 layers and 34 layers,
respectively. Compared to the state-of-the-arts (e.g., XNOR Net), Bi-Real net
achieves up to 10% higher top-1 accuracy with more memory saving and lower
computational cost. Keywords: binary neural network, 1-bit CNNs,
1-layer-per-block
Recurrent neural networks for aortic image sequence segmentation with sparse annotations
Authors: Wenjia Bai, Hideaki Suzuki, Chen Qin, Giacomo Tarroni, Ozan Oktay, Paul M. Matthews, Daniel Rueckert
Date: 2018-08-01 00:00:00
Tasks: Anatomy, Medical Image Analysis, Segmentation

Segmentation of image sequences is an important task in medical image
analysis, which enables clinicians to assess the anatomy and function of moving
organs. However, direct application of a segmentation algorithm to each time
frame of a sequence may ignore the temporal continuity inherent in the
sequence. In this work, we propose an image sequence segmentation algorithm by
combining a fully convolutional network with a recurrent neural network, which
incorporates both spatial and temporal information into the segmentation task.
A key challenge in training this network is that the available manual
annotations are temporally sparse, which forbids end-to-end training. We
address this challenge by performing non-rigid label propagation on the
annotations and introducing an exponentially weighted loss function for
training. Experiments on aortic MR image sequences demonstrate that the
proposed method significantly improves both accuracy and temporal smoothness of
segmentation, compared to a baseline method that utilises spatial information
only. It achieves an average Dice metric of 0.960 for the ascending aorta and
0.953 for the descending aorta.
Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining
Authors: Yundong Zhang, Juan Carlos Niebles, Alvaro Soto
Date: 2018-08-01 00:00:00
Tasks: Question Answering, Visual Grounding, Visual Question Answering, Visual Question Answering (VQA)

A key aspect of VQA models that are interpretable is their ability to ground
their answers to relevant regions in the image. Current approaches with this
capability rely on supervised learning and human annotated groundings to train
attention mechanisms inside the VQA architecture. Unfortunately, obtaining
human annotations specific for visual grounding is difficult and expensive. In
this work, we demonstrate that we can effectively train a VQA architecture with
grounding supervision that can be automatically obtained from available region
descriptions and object annotations. We also show that our model trained with
this mined supervision generates visual groundings that achieve a higher
correlation with respect to manually-annotated groundings, meanwhile achieving
state-of-the-art VQA accuracy.
Saliency for Fine-grained Object Recognition in Domains with Scarce Training Data
Authors: Carola Figueroa Flores, Abel Gonzalez-García, Joost Van de Weijer, Bogdan Raducanu
Date: 2018-08-01 00:00:00
Tasks: Object Recognition

This paper investigates the role of saliency to improve the classification accuracy of a Convolutional Neural Network (CNN) for the case when scarce training data is available. Our approach consists in adding a saliency branch to an existing CNN architecture which is used to modulate the standard bottom-up visual features from the original image input, acting as an attentional mechanism that guides the feature extraction process. The main aim of the proposed approach is to enable the effective training of a fine-grained recognition model with limited training samples and to improve the performance on the task, thereby alleviating the need to annotate large dataset. % The vast majority of saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline. Our proposed pipeline allows to evaluate saliency methods for the high-level task of object recognition. We perform extensive experiments on various fine-grained datasets (Flowers, Birds, Cars, and Dogs) under different conditions and show that saliency can considerably improve the network's performance, especially for the case of scarce training data. Furthermore, our experiments show that saliency methods that obtain improved saliency maps (as measured by traditional saliency benchmarks) also translate to saliency methods that yield improved performance gains when applied in an object recognition pipeline.
A Review on the Application of Natural Computing in Environmental Informatics
Authors: Andreas Kamilaris
Date: 2018-08-01 00:00:00

Natural computing offers new opportunities to understand, model and analyze
the complexity of the physical and human-created environment. This paper
examines the application of natural computing in environmental informatics, by
investigating related work in this research field. Various nature-inspired
techniques are presented, which have been employed to solve different relevant
problems. Advantages and disadvantages of these techniques are discussed,
together with analysis of how natural computing is generally used in
environmental research.
Subitizing with Variational Autoencoders
Authors: Rijnder Wever, Tom F. H. Runia
Date: 2018-08-01 00:00:00

Numerosity, the number of objects in a set, is a basic property of a given
visual scene. Many animals develop the perceptual ability to subitize: the
near-instantaneous identification of the numerosity in small sets of visual
items. In computer vision, it has been shown that numerosity emerges as a
statistical property in neural networks during unsupervised learning from
simple synthetic images. In this work, we focus on more complex natural images
using unsupervised hierarchical neural networks. Specifically, we show that
variational autoencoders are able to spontaneously perform subitizing after
training without supervision on a large amount images from the Salient Object
Subitizing dataset. While our method is unable to outperform supervised
convolutional networks for subitizing, we observe that the networks learn to
encode numerosity as basic visual property. Moreover, we find that the learned
representations are likely invariant to object area; an observation in
alignment with studies on biological neural networks in cognitive neuroscience.