pwc-archive/papers-with-abstracts huggingface dataset:
Learning to predict crisp boundaries
Authors: Ruoxi Deng, Chunhua Shen, Shengjun Liu, Huibing Wang, Xinru Liu
Date: 2018-07-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Boundary Detection, Edge Detection

Recent methods for boundary or edge detection built on Deep Convolutional
Neural Networks (CNNs) typically suffer from the issue of predicted edges being
thick and need post-processing to obtain crisp boundaries. Highly imbalanced
categories of boundary versus background in training data is one of main
reasons for the above problem. In this work, the aim is to make CNNs produce
sharp boundaries without post-processing. We introduce a novel loss for
boundary detection, which is very effective for classifying imbalanced data and
allows CNNs to produce crisp boundaries. Moreover, we propose an end-to-end
network which adopts the bottom-up/top-down architecture to tackle the task.
The proposed network effectively leverages hierarchical features and produces
pixel-accurate boundary mask, which is critical to reconstruct the edge map.
Our experiments illustrate that directly making crisp prediction not only
promotes the visual results of CNNs, but also achieves better results against
the state-of-the-art on the BSDS500 dataset (ODS F-score of .815) and the NYU
Depth dataset (ODS F-score of .762).
Towards a Deep Unified Framework for Nuclear Reactor Perturbation Analysis
Authors: Fabio De Sousa Ribeiro, Francesco Caliva, Dionysios Chionis, Abdelhamid Dokhane, Antonios Mylonakis, Christophe Demaziere, Georgios Leontidis, Stefanos Kollias
Date: 2018-07-26 00:00:00

In this paper, we take the first steps towards a novel unified framework for
the analysis of perturbations in both the Time and Frequency domains. The
identification of type and source of such perturbations is fundamental for
monitoring reactor cores and guarantee safety while running at nominal
conditions. A 3D Convolutional Neural Network (3D-CNN) was employed to analyse
perturbations happening in the frequency domain, such as an absorber of
variable strength or propagating perturbation. Recurrent neural networks (RNN),
specifically Long Short-Term Memory (LSTM) networks were used to study signal
sequences related to perturbations induced in the time domain, including the
vibrations of fuel assemblies and the fluctuations of thermal-hydraulic
parameters at the inlet of the reactor coolant loops. 512 dimensional
representations were extracted from the 3D-CNN and LSTM architectures, and used
as input to a fused multi-sigmoid classification layer to recognise the
perturbation type. If the perturbation is in the frequency domain, a separate
fully-connected layer utilises said representations to regress the coordinates
of its source. The results showed that the perturbation type can be recognised
with high accuracy in all cases, and frequency domain scenario sources can be
localised with high precision.
AlphaGAN: Generative adversarial networks for natural image matting
Authors: Sebastian Lutz, Konstantinos Amplianitis, Aljosa Smolic
Date: 2018-07-26 00:00:00
Tasks: Decoder, Generative Adversarial Network, Image Matting

We present the first generative adversarial network (GAN) for natural image
matting. Our novel generator network is trained to predict visually appealing
alphas with the addition of the adversarial loss from the discriminator that is
trained to classify well-composited images. Further, we improve existing
encoder-decoder architectures to better deal with the spatial localization
issues inherited in convolutional neural networks (CNN) by using dilated
convolutions to capture global context information without downscaling feature
maps and losing spatial information. We present state-of-the-art results on the
alphamatting online benchmark for the gradient error and give comparable
results in others. Our method is particularly well suited for fine structures
like hair, which is of great importance in practical matting applications, e.g.
in film/TV production.
Automatic Detection of Node-Replication Attack in Vehicular Ad-hoc Networks
Authors: Mohammed GH. I. AL Zamil
Date: 2018-07-26 00:00:00

Recent advances in smart cities applications enforce security threads such as
node replication attacks. Such attack is take place when the attacker plants a
replicated network node within the network. Vehicular Ad hoc networks are
connecting sensors that have limited resources and required the response time
to be as low as possible. In this type networks, traditional detection
algorithms of node replication attacks are not efficient. In this paper, we
propose an initial idea to apply a newly adapted statistical methodology that
can detect node replication attacks with high performance as compared to
state-of-the-art techniques. We provide a sufficient description of this
methodology and a road-map for testing and experiment its performance.
Concurrent Learning of Semantic Relations
Authors: Georgios Balikas, Gaël Dias, Rumen Moraliyski, Massih-Reza Amini
Date: 2018-07-26 00:00:00
Tasks: Multi-Task Learning, Relation

Discovering whether words are semantically related and identifying the
specific semantic relation that holds between them is of crucial importance for
NLP as it is essential for tasks like query expansion in IR. Within this
context, different methodologies have been proposed that either exclusively
focus on a single lexical relation (e.g. hypernymy vs. random) or learn
specific classifiers capable of identifying multiple semantic relations (e.g.
hypernymy vs. synonymy vs. random). In this paper, we propose another way to
look at the problem that relies on the multi-task learning paradigm. In
particular, we want to study whether the learning process of a given semantic
relation (e.g. hypernymy) can be improved by the concurrent learning of another
semantic relation (e.g. co-hyponymy). Within this context, we particularly
examine the benefits of semi-supervised learning where the training of a
prediction function is performed over few labeled data jointly with many
unlabeled ones. Preliminary results based on simple learning strategies and
state-of-the-art distributional feature representations show that concurrent
learning can lead to improvements in a vast majority of tested situations.
Loosely-Coupled Semi-Direct Monocular SLAM
Authors: Seong Hun Lee, Javier Civera
Date: 2018-07-26 00:00:00
Tasks: Simultaneous Localization and Mapping

We propose a novel semi-direct approach for monocular simultaneous
localization and mapping (SLAM) that combines the complementary strengths of
direct and feature-based methods. The proposed pipeline loosely couples direct
odometry and feature-based SLAM to perform three levels of parallel
optimizations: (1) photometric bundle adjustment (BA) that jointly optimizes
the local structure and motion, (2) geometric BA that refines keyframe poses
and associated feature map points, and (3) pose graph optimization to achieve
global map consistency in the presence of loop closures. This is achieved in
real-time by limiting the feature-based operations to marginalized keyframes
from the direct odometry module. Exhaustive evaluation on two benchmark
datasets demonstrates that our system outperforms the state-of-the-art
monocular odometry and SLAM systems in terms of overall accuracy and
robustness.
A Linear Constrained Optimization Benchmark For Probabilistic Search Algorithms: The Rotated Klee-Minty Problem
Authors: Michael Hellwig, Hans-Georg Beyer
Date: 2018-07-26 00:00:00
Tasks: Benchmarking, Evolutionary Algorithms, Relation

The development, assessment, and comparison of randomized search algorithms
heavily rely on benchmarking. Regarding the domain of constrained optimization,
the number of currently available benchmark environments bears no relation to
the number of distinct problem features. The present paper advances a proposal
of a scalable linear constrained optimization problem that is suitable for
benchmarking Evolutionary Algorithms. By comparing two recent EA variants, the
linear benchmarking environment is demonstrated.
A Better Baseline for AVA
Authors: Rohit Girdhar, João Carreira, Carl Doersch, Andrew Zisserman
Date: 2018-07-26 00:00:00
Tasks: Action Localization, Action Recognition

We introduce a simple baseline for action localization on the AVA dataset.
The model builds upon the Faster R-CNN bounding box detection framework,
adapted to operate on pure spatiotemporal features - in our case produced
exclusively by an I3D model pretrained on Kinetics. This model obtains 21.9%
average AP on the validation set of AVA v2.1, up from 14.5% for the best RGB
spatiotemporal model used in the original AVA paper (which was pretrained on
Kinetics and ImageNet), and up from 11.3 of the publicly available baseline
using a ResNet101 image feature extractor, that was pretrained on ImageNet. Our
final model obtains 22.8%/21.9% mAP on the val/test sets and outperforms all
submissions to the AVA challenge at CVPR 2018.
Level-Based Analysis of the Univariate Marginal Distribution Algorithm
Authors: Duc-Cuong Dang, Per Kristian Lehre, Phan Trung Hai Nguyen
Date: 2018-07-26 00:00:00

Estimation of Distribution Algorithms (EDAs) are stochastic heuristics that
search for optimal solutions by learning and sampling from probabilistic
models. Despite their popularity in real-world applications, there is little
rigorous understanding of their performance. Even for the Univariate Marginal
Distribution Algorithm (UMDA) -- a simple population-based EDA assuming
independence between decision variables -- the optimisation time on the linear
problem OneMax was until recently undetermined. The incomplete theoretical
understanding of EDAs is mainly due to lack of appropriate analytical tools.
  We show that the recently developed level-based theorem for non-elitist
populations combined with anti-concentration results yield upper bounds on the
expected optimisation time of the UMDA. This approach results in the bound
$\mathcal{O}(n\lambda\log \lambda+n^2)$ on two problems, LeadingOnes and
BinVal, for population sizes $\lambda>\mu=\Omega(\log n)$, where $\mu$ and
$\lambda$ are parameters of the algorithm. We also prove that the UMDA with
population sizes $\mu\in \mathcal{O}(\sqrt{n}) \cap \Omega(\log n)$ optimises
OneMax in expected time $\mathcal{O}(\lambda n)$, and for larger population
sizes $\mu=\Omega(\sqrt{n}\log n)$, in expected time
$\mathcal{O}(\lambda\sqrt{n})$. The facility and generality of our arguments
suggest that this is a promising approach to derive bounds on the expected
optimisation time of EDAs.
Motion Feature Network: Fixed Motion Filter for Action Recognition
Authors: Myunggi Lee, Seungeui Lee, Sungjoon Son, Gyu-tae Park, Nojun Kwak
Date: 2018-07-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Recognition, Action Recognition In Videos, Optical Flow Estimation, Temporal Action Localization

Spatio-temporal representations in frame sequences play an important role in
the task of action recognition. Previously, a method of using optical flow as a
temporal information in combination with a set of RGB images that contain
spatial information has shown great performance enhancement in the action
recognition tasks. However, it has an expensive computational cost and requires
two-stream (RGB and optical flow) framework. In this paper, we propose MFNet
(Motion Feature Network) containing motion blocks which make it possible to
encode spatio-temporal information between adjacent frames in a unified network
that can be trained end-to-end. The motion block can be attached to any
existing CNN-based action recognition frameworks with only a small additional
cost. We evaluated our network on two of the action recognition datasets
(Jester and Something-Something) and achieved competitive performances for both
datasets by training the networks from scratch.
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
Authors: Dongqing Zhang, Jiaolong Yang, Dongqiangzi Ye, Gang Hua
Date: 2018-07-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Quantization

Although weight and activation quantization is an effective approach for Deep
Neural Network (DNN) compression and has a lot of potentials to increase
inference speed leveraging bit-operations, there is still a noticeable gap in
terms of prediction accuracy between the quantized model and the full-precision
model. To address this gap, we propose to jointly train a quantized,
bit-operation-compatible DNN and its associated quantizers, as opposed to using
fixed, handcrafted quantization schemes such as uniform or logarithmic
quantization. Our method for learning the quantizers applies to both network
weights and activations with arbitrary-bit precision, and our quantizers are
easy to train. The comprehensive experiments on CIFAR-10 and ImageNet datasets
show that our method works consistently well for various network structures
such as AlexNet, VGG-Net, GoogLeNet, ResNet, and DenseNet, surpassing previous
quantization methods in terms of accuracy by an appreciable margin. Code
available at https://github.com/Microsoft/LQ-Nets
A Tensor Factorization Method for 3D Super-Resolution with Application to Dental CT
Authors: Janka Hatvani, Adrian Basarab, Jean-Yves Tourneret, Miklós Gyöngy, Denis Kouamé
Date: 2018-07-26 00:00:00
Tasks: Super-Resolution

Available super-resolution techniques for 3D images are either
computationally inefficient prior-knowledge-based iterative techniques or deep
learning methods which require a large database of known low- and
high-resolution image pairs. A recently introduced tensor-factorization-based
approach offers a fast solution without the use of known image pairs or strict
prior assumptions. In this article this factorization framework is investigated
for single image resolution enhancement with an off-line estimate of the system
point spread function. The technique is applied to 3D cone beam computed
tomography for dental image resolution enhancement. To demonstrate the
efficiency of our method, it is compared to a recent state-of-the-art iterative
technique using low-rank and total variation regularizations. In contrast to
this comparative technique, the proposed reconstruction technique gives a
2-order-of-magnitude improvement in running time -- 2 minutes compared to 2
hours for a dental volume of 282$\times$266$\times$392 voxels. Furthermore, it
also offers slightly improved quantitative results (peak signal-to-noise ratio,
segmentation quality). Another advantage of the presented technique is the low
number of hyperparameters. As demonstrated in this paper, the framework is not
sensitive to small changes of its parameters, proposing an ease of use.
Towards Optimal Power Control via Ensembling Deep Neural Networks
Authors: Fei Liang, Cong Shen, Wei Yu, Feng Wu
Date: 2018-07-26 00:00:00

A deep neural network (DNN) based power control method is proposed, which
aims at solving the non-convex optimization problem of maximizing the sum rate
of a multi-user interference channel. Towards this end, we first present PCNet,
which is a multi-layer fully connected neural network that is specifically
designed for the power control problem. PCNet takes the channel coefficients as
input and outputs the transmit power of all users. A key challenge in training
a DNN for the power control problem is the lack of ground truth, i.e., the
optimal power allocation is unknown. To address this issue, PCNet leverages the
unsupervised learning strategy and directly maximizes the sum rate in the
training phase. Observing that a single PCNet does not globally outperform the
existing solutions, we further propose ePCNet, a network ensemble with multiple
PCNets trained independently. Simulation results show that for the standard
symmetric multi-user Gaussian interference channel, ePCNet can outperform all
state-of-the-art power control methods by 1.2%-4.6% under a variety of system
configurations. Furthermore, the performance improvement of ePCNet comes with a
reduced computational complexity.
Move Forward and Tell: A Progressive Generator of Video Descriptions
Authors: Yilei Xiong, Bo Dai, Dahua Lin
Date: 2018-07-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Descriptive, Sentence, Video Captioning

We present an efficient framework that can generate a coherent paragraph to
describe a given video. Previous works on video captioning usually focus on
video clips. They typically treat an entire video as a whole and generate the
caption conditioned on a single embedding. On the contrary, we consider videos
with rich temporal structures and aim to generate paragraph descriptions that
can preserve the story flow while being coherent and concise. Towards this
goal, we propose a new approach, which produces a descriptive paragraph by
assembling temporally localized descriptions. Given a video, it selects a
sequence of distinctive clips and generates sentences thereon in a coherent
manner. Particularly, the selection of clips and the production of sentences
are done jointly and progressively driven by a recurrent network -- what to
describe next depends on what have been said before. Here, the recurrent
network is learned via self-critical sequence training with both sentence-level
and paragraph-level rewards. On the ActivityNet Captions dataset, our method
demonstrated the capability of generating high-quality paragraph descriptions
for videos. Compared to those by other methods, the descriptions produced by
our method are often more relevant, more coherent, and more concise.
Instance Segmentation by Deep Coloring
Authors: Victor Kulikov, Victor Yurchenko, Victor Lempitsky
Date: 2018-07-26 00:00:00
Tasks: Autonomous Driving, Instance Segmentation, Plant Phenotyping, Segmentation, Semantic Segmentation

We propose a new and, arguably, a very simple reduction of instance
segmentation to semantic segmentation. This reduction allows to train
feed-forward non-recurrent deep instance segmentation systems in an end-to-end
fashion using architectures that have been proposed for semantic segmentation.
Our approach proceeds by introducing a fixed number of labels (colors) and then
dynamically assigning object instances to those labels during training
(coloring). A standard semantic segmentation objective is then used to train a
network that can color previously unseen images. At test time, individual
object instances can be recovered from the output of the trained convolutional
network using simple connected component analysis. In the experimental
validation, the coloring approach is shown to be capable of solving diverse
instance segmentation tasks arising in autonomous driving (the Cityscapes
benchmark), plant phenotyping (the CVPPP leaf segmentation challenge), and
high-throughput microscopy image analysis.
  The source code is publicly available:
https://github.com/kulikovv/DeepColoring.
Deep Pictorial Gaze Estimation
Authors: Seonwook Park, Adrian Spurr, Otmar Hilliges
Date: 2018-07-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Gaze Estimation

Estimating human gaze from natural eye images only is a challenging task.
Gaze direction can be defined by the pupil- and the eyeball center where the
latter is unobservable in 2D images. Hence, achieving highly accurate gaze
estimates is an ill-posed problem. In this paper, we introduce a novel deep
neural network architecture specifically designed for the task of gaze
estimation from single eye input. Instead of directly regressing two angles for
the pitch and yaw of the eyeball, we regress to an intermediate pictorial
representation which in turn simplifies the task of 3D gaze direction
estimation. Our quantitative and qualitative results show that our approach
achieves higher accuracies than the state-of-the-art and is robust to variation
in gaze, head pose and image quality.
A Data-driven Prior on Facet Orientation for Semantic Mesh Labeling
Authors: Andrea Romanoni, Matteo Matteucci
Date: 2018-07-26 00:00:00

Mesh labeling is the key problem of classifying the facets of a 3D mesh with
a label among a set of possible ones. State-of-the-art methods model mesh
labeling as a Markov Random Field over the facets. These algorithms map image
segmentations to the mesh by minimizing an energy function that comprises a
data term, a smoothness terms, and class-specific priors. The latter favor a
labeling with respect to another depending on the orientation of the facet
normals. In this paper we propose a novel energy term that acts as a prior, but
does not require any prior knowledge about the scene nor scene-specific
relationship among classes. It bootstraps from a coarse mapping of the 2D
segmentations on the mesh, and it favors the facets to be labeled according to
the statistics of the mesh normals in their neighborhood. We tested our
approach against five different datasets and, even if we do not inject prior
knowledge, our method adapts to the data and overcomes the state-of-the-art.
Naturalistic Driver Intention and Path Prediction using Recurrent Neural Networks
Authors: Alex Zyner, Stewart Worrall, Eduardo Nebot
Date: 2018-07-26 00:00:00
Tasks: Autonomous Vehicles, Clustering, Trajectory Prediction

Understanding the intentions of drivers at intersections is a critical
component for autonomous vehicles. Urban intersections that do not have traffic
signals are a common epicentre of highly variable vehicle movement and
interactions. We present a method for predicting driver intent at urban
intersections through multi-modal trajectory prediction with uncertainty. Our
method is based on recurrent neural networks combined with a mixture density
network output layer. To consolidate the multi-modal nature of the output
probability distribution, we introduce a clustering algorithm that extracts the
set of possible paths that exist in the prediction output, and ranks them
according to likelihood. To verify the method's performance and
generalizability, we present a real-world dataset that consists of over 23,000
vehicles traversing five different intersections, collected using a vehicle
mounted Lidar based tracking system. An array of metrics is used to demonstrate
the performance of the model against several baselines.
Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN
Authors: Deepak Babu Sam, Neeraj N Sajjan, R. Venkatesh Babu
Date: 2018-07-26 00:00:00
Proceeding: CVPR 2018 6
Tasks: Crowd Counting, Diversity

Automated counting of people in crowd images is a challenging task. The major
difficulty stems from the large diversity in the way people appear in crowds.
In fact, features available for crowd discrimination largely depend on the
crowd density to the extent that people are only seen as blobs in a highly
dense scene. We tackle this problem with a growing CNN which can progressively
increase its capacity to account for the wide variability seen in crowd scenes.
Our model starts from a base CNN density regressor, which is trained in
equivalence on all types of crowd images. In order to adapt with the huge
diversity, we create two child regressors which are exact copies of the base
CNN. A differential training procedure divides the dataset into two clusters
and fine-tunes the child networks on their respective specialties.
Consequently, without any hand-crafted criteria for forming specialties, the
child regressors become experts on certain types of crowds. The child networks
are again split recursively, creating two experts at every division. This
hierarchical training leads to a CNN tree, where the child regressors are more
fine experts than any of their parents. The leaf nodes are taken as the final
experts and a classifier network is then trained to predict the correct
specialty for a given test image patch. The proposed model achieves higher
count accuracy on major crowd datasets. Further, we analyse the characteristics
of specialties mined automatically by our method.