pwc-archive/papers-with-abstracts huggingface dataset:
Self Paced Adversarial Training for Multimodal Few-shot Learning
Authors: Frederik Pahde, Oleksiy Ostapenko, Patrick Jähnichen, Tassilo Klein, Moin Nabi
Date: 2018-11-22 00:00:00
Tasks: Few-Shot Learning, Generative Adversarial Network

State-of-the-art deep learning algorithms yield remarkable results in many
visual recognition tasks. However, they still fail to provide satisfactory
results in scarce data regimes. To a certain extent this lack of data can be
compensated by multimodal information. Missing information in one modality of a
single data point (e.g. an image) can be made up for in another modality (e.g.
a textual description). Therefore, we design a few-shot learning task that is
multimodal during training (i.e. image and text) and single-modal during test
time (i.e. image). In this regard, we propose a self-paced class-discriminative
generative adversarial network incorporating multimodality in the context of
few-shot learning. The proposed approach builds upon the idea of cross-modal
data generation in order to alleviate the data sparsity problem. We improve
few-shot learning accuracies on the finegrained CUB and Oxford-102 datasets.
Object-oriented Targets for Visual Navigation using Rich Semantic Representations
Authors: Jean-Benoit Delbrouck, Stéphane Dupont
Date: 2018-11-22 00:00:00
Tasks: Navigate, Object, Visual Navigation

When searching for an object humans navigate through a scene using semantic
information and spatial relationships. We look for an object using our
knowledge of its attributes and relationships with other objects to infer the
probable location. In this paper, we propose to tackle the visual navigation
problem using rich semantic representations of the observed scene and
object-oriented targets to train an agent. We show that both allows the agent
to generalize to new targets and unseen scene in a short amount of training
time.
Dual Reweighted Lp-Norm Minimization for Salt-and-pepper Noise Removal
Authors: Huiwen Dong, Jing Yu, Chuangbai Xiao
Date: 2018-11-22 00:00:00
Tasks: Salt-And-Pepper Noise Removal

The robust principal component analysis (RPCA), which aims to estimate underlying low-rank and sparse structures from the degraded observation data, has found wide applications in computer vision. It is usually replaced by the principal component pursuit (PCP) model in order to pursue the convex property, leading to the undesirable overshrink problem. In this paper, we propose a dual weighted lp-norm (DWLP) model with a more reasonable weighting rule and weaker powers, which greatly generalizes the previous work and provides a better approximation to the rank minimization problem for original matrix as well as the l0-norm minimization problem for sparse data. Moreover, an approximate closed-form solution is introduced to solve the lp-norm minimization, which has more stability in the nonconvex optimization and provides a more accurate estimation for the low-rank and sparse matrix recovery problem. We then apply the DWLP model to remove salt-and-pepper noise by exploiting the image nonlocal self-similarity. Both qualitative and quantitative experiments demonstrate that the proposed method outperforms other state-of-the-art methods. In terms of PSNR evaluation, our DWLP achieves about 7.188dB, 5.078dB, 3.854dB, 2.536dB and 0.158dB improvements over the current WSNM-RPCA under 10\% to 50\% salt-and-pepper noise with an interval 10\% respectively.
Generalized Range Moves
Authors: Richard Hartley, Thalaiyasingam Ajanthan
Date: 2018-11-22 00:00:00

We consider move-making algorithms for energy minimization of multi-label
Markov Random Fields (MRFs). Since this is not a tractable problem in general,
a commonly used heuristic is to minimize over subsets of labels and variables
in an iterative procedure. Such methods include {\alpha}-expansion,
{\alpha}{\beta}-swap, and range-moves. In each iteration, a small subset of
variables are active in the optimization, which diminishes their effectiveness,
and increases the required number of iterations. In this paper, we present a
method in which optimization can be carried out over all labels, and most, or
all variables at once. Experiments show substantial improvement with respect to
previous move-making algorithms.
MGANet: A Robust Model for Quality Enhancement of Compressed Video
Authors: Xiandong Meng, Xuan Deng, Shuyuan Zhu, Shuaicheng Liu, Chuan Wang, Chen Chen, Bing Zeng
Date: 2018-11-22 00:00:00
Tasks: Decoder, Video Compression

In video compression, most of the existing deep learning approaches
concentrate on the visual quality of a single frame, while ignoring the useful
priors as well as the temporal information of adjacent frames. In this paper,
we propose a multi-frame guided attention network (MGANet) to enhance the
quality of compressed videos. Our network is composed of a temporal encoder
that discovers inter-frame relations, a guided encoder-decoder subnet that
encodes and enhances the visual patterns of target frame, and a
multi-supervised reconstruction component that aggregates information to
predict details. We design a bidirectional residual convolutional LSTM unit to
implicitly discover frames variations over time with respect to the target
frame. Meanwhile, the guided map is proposed to guide our network to
concentrate more on the block boundary. Our approach takes advantage of
intra-frame prior information and inter-frame information to improve the
quality of compressed video. Experimental results show the robustness and
superior performance of the proposed method.Code is available at
https://github.com/mengab/MGANet
IEGAN: Multi-purpose Perceptual Quality Image Enhancement Using Generative Adversarial Network
Authors: Soumya Shubhra Ghosh, Yang Hua, Sankha Subhra Mukherjee, Neil Robertson
Date: 2018-11-22 00:00:00
Tasks: Generative Adversarial Network, Image Enhancement, Super-Resolution

Despite the breakthroughs in quality of image enhancement, an end-to-end
solution for simultaneous recovery of the finer texture details and sharpness
for degraded images with low resolution is still unsolved. Some existing
approaches focus on minimizing the pixel-wise reconstruction error which
results in a high peak signal-to-noise ratio. The enhanced images fail to
provide high-frequency details and are perceptually unsatisfying, i.e., they
fail to match the quality expected in a photo-realistic image. In this paper,
we present Image Enhancement Generative Adversarial Network (IEGAN), a
versatile framework capable of inferring photo-realistic natural images for
both artifact removal and super-resolution simultaneously. Moreover, we propose
a new loss function consisting of a combination of reconstruction loss, feature
loss and an edge loss counterpart. The feature loss helps to push the output
image to the natural image manifold and the edge loss preserves the sharpness
of the output image. The reconstruction loss provides low-level semantic
information to the generator regarding the quality of the generated images
compared to the original. Our approach has been experimentally proven to
recover photo-realistic textures from heavily compressed low-resolution images
on public benchmarks and our proposed high-resolution World100 dataset.
Uncalibrated Non-Rigid Factorisation by Independent Subspace Analysis
Authors: Sami Sebastian Brandt, Hanno Ackermann, Stella Grasshof
Date: 2018-11-22 00:00:00

We propose a general, prior-free approach for the uncalibrated non-rigid
structure-from-motion problem for modelling and analysis of non-rigid objects
such as human faces. The word general refers to an approach that recovers the
non-rigid affine structure and motion from 2D point correspondences by assuming
that (1) the non-rigid shapes are generated by a linear combination of rigid 3D
basis shapes, (2) that the non-rigid shapes are affine in nature, i.e., they
can be modelled as deviations from the mean, rigid shape, (3) and that the
basis shapes are statistically independent. In contrast to the majority of
existing works, no prior information is assumed for the structure and motion
apart from the assumption the that underlying basis shapes are statistically
independent. The independent 3D shape bases are recovered by independent
subspace analysis (ISA). Likewise, in contrast to the most previous approaches,
no calibration information is assumed for affine cameras; the reconstruction is
solved up to a global affine ambiguity that makes our approach simple but
efficient. In the experiments, we evaluated the method with several standard
data sets including a real face expression data set of 7200 faces with 2D point
correspondences and unknown 3D structure and motion for which we obtained
promising results.
BRDF Estimation of Complex Materials with Nested Learning
Authors: Raquel Vidaurre, Dan Casas, Elena Garces, Jorge Lopez-Moreno
Date: 2018-11-22 00:00:00
Tasks: BRDF estimation

The estimation of the optical properties of a material from RGB-images is an
important but extremely ill-posed problem in Computer Graphics. While recent
works have successfully approached this problem even from just a single
photograph, significant simplifications of the material model are assumed,
limiting the usability of such methods. The detection of complex material
properties such as anisotropy or Fresnel effect remains an unsolved challenge.
We propose a novel method that predicts the model parameters of an
artist-friendly, physically-based BRDF, from only two low-resolution shots of
the material. Thanks to a novel combination of deep neural networks in a nested
architecture, we are able to handle the ambiguities given by the
non-orthogonality and non-convexity of the parameter space. To train the
network, we generate a novel dataset of physically-based synthetic images. We
prove that our model can recover new properties like anisotropy, index of
refraction and a second reflectance color, for materials that have tinted
specular reflections or whose albedo changes at glancing angles.
Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets with Multi-stream Inputs
Authors: Chaoyun Zhang, Rui Li, Woojin Kim, Daesub Yoon, Paul Patras
Date: 2018-11-22 00:00:00
Tasks: Classification, General Classification

Understanding driver activity is vital for in-vehicle systems that aim to reduce the incidence of car accidents rooted in cognitive distraction. Automating real-time behavior recognition while ensuring actions classification with high accuracy is however challenging, given the multitude of circumstances surrounding drivers, the unique traits of individuals, and the computational constraints imposed by in-vehicle embedded platforms. Prior work fails to jointly meet these runtime/accuracy requirements and mostly rely on a single sensing modality, which in turn can be a single point of failure. In this paper, we harness the exceptional feature extraction abilities of deep learning and propose a dedicated Interwoven Deep Convolutional Neural Network (InterCNN) architecture to tackle the problem of accurate classification of driver behaviors in real-time. The proposed solution exploits information from multi-stream inputs, i.e., in-vehicle cameras with different fields of view and optical flows computed based on recorded images, and merges through multiple fusion layers abstract features that it extracts. This builds a tight ensembling system, which significantly improves the robustness of the model. In addition, we introduce a temporal voting scheme based on historical inference instances, to enhance the classification accuracy. Experiments conducted with a dataset that we collect in a mock-up car environment demonstrate that the proposed InterCNN with MobileNet convolutional blocks can classify 9 different behaviors with 73.97% accuracy, and 5 'aggregated' behaviors with 81.66% accuracy. We further show that our architecture is highly computationally efficient, as it performs inferences within 15ms, which satisfies the real-time constraints of intelligent cars. Nevertheless, our InterCNN is robust to lossy input, as the classification remains accurate when two input streams are occluded.
Conditioning Optimization of Extreme Learning Machine by Multitask Beetle Antennae Swarm Algorithm
Authors: Xixian Zhang, Zhijing Yang, Faxian Cao, Jiangzhong Cao, Meilin Wang, Nian Cai
Date: 2018-11-22 00:00:00

Extreme learning machine (ELM) as a simple and rapid neural network has been
shown its good performance in various areas. Different from the general single
hidden layer feedforward neural network (SLFN), the input weights and biases in
hidden layer of ELM are generated randomly, so that it only takes a little
computation overhead to train the model. However, the strategy of selecting
input weights and biases at random may result in ill-posed problem. Aiming to
optimize the conditioning of ELM, we propose an effective particle swarm
heuristic algorithm called Multitask Beetle Antennae Swarm Algorithm (MBAS),
which is inspired by the structures of artificial bee colony (ABS) algorithm
and Beetle Antennae Search (BAS) algorithm. Then, the proposed MBAS is applied
to optimize the input weights and biases of ELM. Experiment results show that
the proposed method is capable of simultaneously reducing the condition number
and regression error, and achieving good generalization performances.
Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning
Authors: Sainbayar Sukhbaatar, Emily Denton, Arthur Szlam, Rob Fergus
Date: 2018-11-22 00:00:00
Tasks: Hierarchical Reinforcement Learning, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In hierarchical reinforcement learning a major challenge is determining
appropriate low-level policies. We propose an unsupervised learning scheme,
based on asymmetric self-play from Sukhbaatar et al. (2018), that automatically
learns a good representation of sub-goals in the environment and a low-level
policy that can execute them. A high-level policy can then direct the lower one
by generating a sequence of continuous sub-goal vectors. We evaluate our model
using Mazebase and Mujoco environments, including the challenging AntGather
task. Visualizations of the sub-goal embeddings reveal a logical decomposition
of tasks within the environment. Quantitatively, our approach obtains
compelling performance gains over non-hierarchical approaches.
Feature-based groupwise registration of historical aerial images to present-day ortho-photo maps
Authors: Sebastian Zambanini
Date: 2018-11-22 00:00:00

In this paper, we address the registration of historical WWII images to
present-day ortho-photo maps for the purpose of geolocalization. Due to the
challenging nature of this problem, we propose to register the images jointly
as a group rather than in a step-by-step manner. To this end, we exploit Hough
Voting spaces as pairwise registration estimators and show how they can be
integrated into a probabilistic groupwise registration framework that can be
efficiently optimized. The feature-based nature of our registration framework
allows to register images with a-priori unknown translational and rotational
relations, and is also able to handle scale changes of up to 30% in our test
data due to a final geometrically guided matching step. The superiority of the
proposed method over existing pairwise and groupwise registration methods is
demonstrated on eight highly challenging sets of historical images with
corresponding ortho-photo maps.
Online Collective Animal Movement Activity Recognition
Authors: Kehinde Owoeye, Stephen Hailes
Date: 2018-11-22 00:00:00
Tasks: Activity Recognition

Learning the activities of animals is important for the purpose of monitoring
their welfare vis a vis their behaviour with respect to their environment and
conspecifics. While previous works have largely focused on activity recognition
in a single animal, little or no work has been done in learning the collective
behaviour of animals. In this work, we address the problem of recognising the
collective movement activities of a group of sheep in a flock. We present a
discriminative framework that learns to track the positions and velocities of
all the animals in the flock in an online manner whilst estimating their
collective activity. We investigate the performance of two simple deep network
architectures and show that we can learn the collective activities with good
accuracy even when the distribution of the activities is skewed.
PSICA: decision trees for probabilistic subgroup identification with categorical treatments
Authors: Oleg Sysoev, Krzysztof Bartoszek, Eva-Charlotte Ekstrom, Katarina Ekholm Selling
Date: 2018-11-22 00:00:00
Tasks: Nutrition

Personalized medicine aims at identifying best treatments for a patient with
given characteristics. It has been shown in the literature that these methods
can lead to great improvements in medicine compared to traditional methods
prescribing the same treatment to all patients. Subgroup identification is a
branch of personalized medicine which aims at finding subgroups of the patients
with similar characteristics for which some of the investigated treatments have
a better effect than the other treatments. A number of approaches based on
decision trees has been proposed to identify such subgroups, but most of them
focus on the two-arm trials (control/treatment) while a few methods consider
quantitative treatments (defined by the dose). However, no subgroup
identification method exists that can predict the best treatments in a scenario
with a categorical set of treatments. We propose a novel method for subgroup
identification in categorical treatment scenarios. This method outputs a
decision tree showing the probabilities of a given treatment being the best for
a given group of patients as well as labels showing the possible best
treatments. The method is implemented in an R package \textbf{psica} available
at CRAN. In addition to numerical simulations based on artificial data, we
present an analysis of a community-based nutrition intervention trial that
justifies the validity of our method.
Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing
Authors: Bas H. M. van der Velden, Bob D. de Vos, Claudette E. Loo, Hugo J. Kuijf, Ivana Isgum, Kenneth G. A. Gilhuijs
Date: 2018-11-22 00:00:00
Tasks: Tumor Segmentation

Response of breast cancer to neoadjuvant chemotherapy (NAC) can be monitored
using the change in visible tumor on magnetic resonance imaging (MRI). In our
current workflow, seed points are manually placed in areas of enhancement
likely to contain cancer. A constrained volume growing method uses these
manually placed seed points as input and generates a tumor segmentation. This
method is rigorously validated using complete pathological embedding. In this
study, we propose to exploit deep learning for fast and automatic seed point
detection, replacing manual seed point placement in our existing and
well-validated workflow. The seed point generator was developed in early breast
cancer patients with pathology-proven segmentations (N=100), operated shortly
after MRI. It consisted of an ensemble of three independently trained fully
convolutional dilated neural networks that classified breast voxels as tumor or
non-tumor. Subsequently, local maxima were used as seed points for volume
growing in patients receiving NAC (N=10). The percentage of tumor volume change
was evaluated against semi-automatic segmentations. The primary cancer was
localized in 95% of the tumors at the cost of 0.9 false positive per patient.
False positives included focally enhancing regions of unknown origin and parts
of the intramammary blood vessels. Volume growing from the seed points showed a
median tumor volume decrease of 70% (interquartile range: 50%-77%), comparable
to the semi-automatic segmentations (median: 70%, interquartile range 23%-76%).
To conclude, a fast and automatic seed point generator was developed, fully
automating a well-validated semi-automatic workflow for response monitoring of
breast cancer to neoadjuvant chemotherapy.
Mask R-CNN with Pyramid Attention Network for Scene Text Detection
Authors: Zhida Huang, Zhuoyao Zhong, Lei Sun, Qiang Huo
Date: 2018-11-22 00:00:00
Tasks: Curved Text Detection, Scene Text Detection, Text Detection

In this paper, we present a new Mask R-CNN based text detection approach
which can robustly detect multi-oriented and curved text from natural scene
images in a unified manner. To enhance the feature representation ability of
Mask R-CNN for text detection tasks, we propose to use the Pyramid Attention
Network (PAN) as a new backbone network of Mask R-CNN. Experiments demonstrate
that PAN can suppress false alarms caused by text-like backgrounds more
effectively. Our proposed approach has achieved superior performance on both
multi-oriented (ICDAR-2015, ICDAR-2017 MLT) and curved (SCUT-CTW1500) text
detection benchmark tasks by only using single-scale and single-model testing.
Enhanced Expressive Power and Fast Training of Neural Networks by Random Projections
Authors: Jian-Feng Cai, Dong Li, Jiaze Sun, Ke Wang
Date: 2018-11-22 00:00:00
Tasks: compressed sensing, Dimensionality Reduction

Random projections are able to perform dimension reduction efficiently for
datasets with nonlinear low-dimensional structures. One well-known example is
that random matrices embed sparse vectors into a low-dimensional subspace
nearly isometrically, known as the restricted isometric property in compressed
sensing. In this paper, we explore some applications of random projections in
deep neural networks. We provide the expressive power of fully connected neural
networks when the input data are sparse vectors or form a low-dimensional
smooth manifold. We prove that the number of neurons required for approximating
a Lipschitz function with a prescribed precision depends on the sparsity or the
dimension of the manifold and weakly on the dimension of the input vector. The
key in our proof is that random projections embed stably the set of sparse
vectors or a low-dimensional smooth manifold into a low-dimensional subspace.
Based on this fact, we also propose some new neural network models, where at
each layer the input is first projected onto a low-dimensional subspace by a
random projection and then the standard linear connection and non-linear
activation are applied. In this way, the number of parameters in neural
networks is significantly reduced, and therefore the training of neural
networks can be accelerated without too much performance loss.