pwc-archive/papers-with-abstracts huggingface dataset:
Self-Supervised Relative Depth Learning for Urban Scene Understanding
Authors: Huaizu Jiang, Erik Learned-Miller, Gustav Larsson, Michael Maire, Greg Shakhnarovich
Date: 2017-12-13 00:00:00
Proceeding: ECCV 2018 9
Tasks: Depth Estimation, Monocular Depth Estimation, Motion Segmentation, Road Segmentation, Scene Understanding, Segmentation, Semantic Segmentation, Unsupervised Pre-training

As an agent moves through the world, the apparent motion of scene elements is
(usually) inversely proportional to their depth. It is natural for a learning
agent to associate image patterns with the magnitude of their displacement over
time: as the agent moves, faraway mountains don't move much; nearby trees move
a lot. This natural relationship between the appearance of objects and their
motion is a rich source of information about the world. In this work, we start
by training a deep network, using fully automatic supervision, to predict
relative scene depth from single images. The relative depth training images are
automatically derived from simple videos of cars moving through a scene, using
recent motion segmentation techniques, and no human-provided labels. This proxy
task of predicting relative depth from a single image induces features in the
network that result in large improvements in a set of downstream tasks
including semantic segmentation, joint road segmentation and car detection, and
monocular (absolute) depth estimation, over a network trained from scratch. The
improvement on the semantic segmentation task is greater than those produced by
any other automatically supervised methods. Moreover, for monocular depth
estimation, our unsupervised pre-training method even outperforms supervised
pre-training with ImageNet. In addition, we demonstrate benefits from learning
to predict (unsupervised) relative depth in the specific videos associated with
various downstream tasks. We adapt to the specific scenes in those tasks in an
unsupervised manner to improve performance. In summary, for semantic
segmentation, we present state-of-the-art results among methods that do not use
supervised pre-training, and we even exceed the performance of supervised
ImageNet pre-trained models for monocular depth estimation, achieving results
that are comparable with state-of-the-art methods.
Learning Descriptor Networks for 3D Shape Synthesis and Analysis
Authors: Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, Ying Nian Wu
Date: 2018-04-02 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Object Super-Resolution, Object, Super-Resolution

This paper proposes a 3D shape descriptor network, which is a deep
convolutional energy-based model, for modeling volumetric shape patterns. The
maximum likelihood training of the model follows an "analysis by synthesis"
scheme and can be interpreted as a mode seeking and mode shifting process. The
model can synthesize 3D shape patterns by sampling from the probability
distribution via MCMC such as Langevin dynamics. The model can be used to train
a 3D generator network via MCMC teaching. The conditional version of the 3D
shape descriptor net can be used for 3D object recovery and 3D object
super-resolution. Experiments demonstrate that the proposed model can generate
realistic 3D shape patterns and can be useful for 3D shape analysis.
Learning Intrinsic Image Decomposition from Watching the World
Authors: Zhengqi Li, Noah Snavely
Date: 2018-04-02 00:00:00
Proceeding: CVPR 2018 6
Tasks: Intrinsic Image Decomposition

Single-view intrinsic image decomposition is a highly ill-posed problem, and
so a promising approach is to learn from large amounts of data. However, it is
difficult to collect ground truth training data at scale for intrinsic images.
In this paper, we explore a different approach to learning intrinsic images:
observing image sequences over time depicting the same scene under changing
illumination, and learning single-view decompositions that are consistent with
these changes. This approach allows us to learn without ground truth
decompositions, and to instead exploit information available from multiple
images when training. Our trained model can then be applied at test time to
single views. We describe a new learning framework based on this idea,
including new loss functions that can be efficiently evaluated over entire
sequences. While prior learning-based methods achieve good performance on
specific benchmarks, we show that our approach generalizes well to several
diverse datasets, including MIT intrinsic images, Intrinsic Images in the Wild
and Shading Annotations in the Wild.
Average Biased ReLU Based CNN Descriptor for Improved Face Retrieval
Authors: Shiv Ram Dubey, Soumendu Chakraborty
Date: 2018-04-02 00:00:00
Tasks: Retrieval

The convolutional neural networks (CNN), including AlexNet, GoogleNet, VGGNet, etc. extract features for many computer vision problems which are very discriminative. The trained CNN model over one dataset performs reasonably well whereas on another dataset of similar type the hand-designed feature descriptor outperforms the same trained CNN model. The Rectified Linear Unit (ReLU) layer discards some values in order to introduce the non-linearity. In this paper, it is proposed that the discriminative ability of deep image representation using trained model can be improved by Average Biased ReLU (AB-ReLU) at the last few layers. Basically, AB-ReLU improves the discriminative ability in two ways: 1) it exploits some of the discriminative and discarded negative information of ReLU and 2) it also neglects the irrelevant and positive information used in ReLU. The VGGFace model trained in MatConvNet over the VGG-Face dataset is used as the feature descriptor for face retrieval over other face datasets. The proposed approach is tested over six challenging, unconstrained and robust face datasets (PubFig, LFW, PaSC, AR, FERET and ExtYale) and also on a large scale face dataset (PolyUNIR) in retrieval framework. It is observed that the AB-ReLU outperforms the ReLU when used with a pre-trained VGGFace model over the face datasets. The validation error by training the network after replacing all ReLUs with AB-ReLUs is also observed to be favorable over each dataset. The AB-ReLU even outperforms the state-of-the-art activation functions, such as Sigmoid, ReLU, Leaky ReLU and Flexible ReLU over all seven face datasets.
Multilayer Complex Network Descriptors for Color-Texture Characterization
Authors: Leonardo F. S. Scabini, Rayner H M Condori, Wesley N Gonçalves, Odemir M. Bruno
Date: 2018-04-02 00:00:00
Tasks: Texture Classification

A new method based on complex networks is proposed for color-texture
analysis. The proposal consists on modeling the image as a multilayer complex
network where each color channel is a layer, and each pixel (in each color
channel) is represented as a network vertex. The network dynamic evolution is
accessed using a set of modeling parameters (radii and thresholds), and new
characterization techniques are introduced to capt information regarding within
and between color channel spatial interaction. An automatic and adaptive
approach for threshold selection is also proposed. We conduct classification
experiments on 5 well-known datasets: Vistex, Usptex, Outex13, CURet and MBT.
Results among various literature methods are compared, including deep
convolutional neural networks with pre-trained architectures. The proposed
method presented the highest overall performance over the 5 datasets, with 97.7
of mean accuracy against 97.0 achieved by the ResNet convolutional neural
network with 50 layers.
Regional Priority Based Anomaly Detection using Autoencoders
Authors: Shruti Mittal, Dattaraj Rao
Date: 2018-04-02 00:00:00
Tasks: Anomaly Detection, Decoder, Denoising, Image Denoising, object-detection, Object Detection, Region Proposal

In the recent times, autoencoders, besides being used for compression, have
been proven quite useful even for regenerating similar images or help in image
denoising. They have also been explored for anomaly detection in a few cases.
However, due to location invariance property of convolutional neural network,
autoencoders tend to learn from or search for learned features in the complete
image. This creates issues when all the items in the image are not equally
important and their location matters. For such cases, a semi supervised
solution - regional priority based autoencoder (RPAE) has been proposed. In
this model, similar to object detection models, a region proposal network
identifies the relevant areas in the images as belonging to one of the
predefined categories and then those bounding boxes are fed into appropriate
decoder based on the category they belong to. Finally, the error scores from
all the decoders are combined based on their importance to provide total
reconstruction error.
Real Time Sentiment Change Detection of Twitter Data Streams
Authors: Sotiris K. Tasoulis, Aristidis G. Vrahatis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos
Date: 2018-04-02 00:00:00
Tasks: Change Detection, Sentiment Analysis, Twitter Sentiment Analysis

In the past few years, there has been a huge growth in Twitter sentiment
analysis having already provided a fair amount of research on sentiment
detection of public opinion among Twitter users. Given the fact that Twitter
messages are generated constantly with dizzying rates, a huge volume of
streaming data is created, thus there is an imperative need for accurate
methods for knowledge discovery and mining of this information. Although there
exists a plethora of twitter sentiment analysis methods in the recent
literature, the researchers have shifted to real-time sentiment identification
on twitter streaming data, as expected. A major challenge is to deal with the
Big Data challenges arising in Twitter streaming applications concerning both
Volume and Velocity. Under this perspective, in this paper, a methodological
approach based on open source tools is provided for real-time detection of
changes in sentiment that is ultra efficient with respect to both memory
consumption and computational cost. This is achieved by iteratively collecting
tweets in real time and discarding them immediately after their process. For
this purpose, we employ the Lexicon approach for sentiment characterizations,
while change detection is achieved through appropriate control charts that do
not require historical information. We believe that the proposed methodology
provides the trigger for a potential large-scale monitoring of threads in an
attempt to discover fake news spread or propaganda efforts in their early
stages. Our experimental real-time analysis based on a recent hashtag provides
evidence that the proposed approach can detect meaningful sentiment changes
across a hashtags lifetime.
Empirical Variance Minimization with Applications in Variance Reduction and Optimal Control
Authors: D. Belomestny, L. Iosipoi, Q. Paris, N. Zhivotovskiy
Date: 2017-12-13 00:00:00

We study the problem of empirical minimization for variance-type functionals over functional classes. Sharp non-asymptotic bounds for the excess variance are derived under mild conditions. In particular, it is shown that under some restrictions imposed on the functional class fast convergence rates can be achieved including the optimal non-parametric rates for expressive classes in the non-Donsker regime under some additional assumptions. Our main applications include variance reduction and optimal control.
A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking
Authors: M. Saquib Sarfraz, Arne Schumann, Andreas Eberle, Rainer Stiefelhagen
Date: 2017-11-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Person Re-Identification, Re-Ranking, Retrieval

Person re identification is a challenging retrieval task that requires
matching a person's acquired image across non overlapping camera views. In this
paper we propose an effective approach that incorporates both the fine and
coarse pose information of the person to learn a discriminative embedding. In
contrast to the recent direction of explicitly modeling body parts or
correcting for misalignment based on these, we show that a rather
straightforward inclusion of acquired camera view and/or the detected joint
locations into a convolutional neural network helps to learn a very effective
representation. To increase retrieval performance, re-ranking techniques based
on computed distances have recently gained much attention. We propose a new
unsupervised and automatic re-ranking framework that achieves state-of-the-art
re-ranking performance. We show that in contrast to the current
state-of-the-art re-ranking methods our approach does not require to compute
new rank lists for each image pair (e.g., based on reciprocal neighbors) and
performs well by using simple direct rank list based comparison or even by just
using the already computed euclidean distances between the images. We show that
both our learned representation and our re-ranking method achieve
state-of-the-art performance on a number of challenging surveillance image and
video datasets.
  The code is available online at:
https://github.com/pse-ecn/pose-sensitive-embedding
Exploring to learn visual saliency: The RL-IAC approach
Authors: Celine Craye, Timothee Lesort, David Filliat, Jean-Francois Goudou
Date: 2018-04-02 00:00:00
Tasks: Object Localization, Reinforcement Learning

The problem of object localization and recognition on autonomous mobile
robots is still an active topic. In this context, we tackle the problem of
learning a model of visual saliency directly on a robot. This model, learned
and improved on-the-fly during the robot's exploration provides an efficient
tool for localizing relevant objects within their environment. The proposed
approach includes two intertwined components. On the one hand, we describe a
method for learning and incrementally updating a model of visual saliency from
a depth-based object detector. This model of saliency can also be exploited to
produce bounding box proposals around objects of interest. On the other hand,
we investigate an autonomous exploration technique to efficiently learn such a
saliency model. The proposed exploration, called Reinforcement
Learning-Intelligent Adaptive Curiosity (RL-IAC) is able to drive the robot's
exploration so that samples selected by the robot are likely to improve the
current model of saliency. We then demonstrate that such a saliency model
learned directly on a robot outperforms several state-of-the-art saliency
techniques, and that RL-IAC can drastically decrease the required time for
learning a reliable saliency model.
Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks
Authors: Dongwook Lee, Jaejun Yoo, Sungho Tak, Jong Chul Ye
Date: 2018-04-02 00:00:00
Tasks: compressed sensing

Accelerated magnetic resonance (MR) scan acquisition with compressed sensing
(CS) and parallel imaging is a powerful method to reduce MR imaging scan time.
However, many reconstruction algorithms have high computational costs. To
address this, we investigate deep residual learning networks to remove aliasing
artifacts from artifact corrupted images. The proposed deep residual learning
networks are composed of magnitude and phase networks that are separately
trained. If both phase and magnitude information are available, the proposed
algorithm can work as an iterative k-space interpolation algorithm using
framelet representation. When only magnitude data is available, the proposed
approach works as an image domain post-processing algorithm. Even with strong
coherent aliasing artifacts, the proposed network successfully learned and
removed the aliasing artifacts, whereas current parallel and CS reconstruction
methods were unable to remove these artifacts. Comparisons using single and
multiple coil show that the proposed residual network provides good
reconstruction results with orders of magnitude faster computational time than
existing compressed sensing methods. The proposed deep learning framework may
have a great potential for accelerated MR reconstruction by generating accurate
results immediately.
A Vehicle Detection Approach using Deep Learning Methodologies
Authors: Abdullah Asim Yilmaz, Mehmet Serdar Guzel, Iman Askerbeyli, Erkan Bostanci
Date: 2018-04-02 00:00:00
Tasks: Deep Learning, vehicle detection

The purpose of this study is to successfully train our vehicle detector using
R-CNN, Faster R-CNN deep learning methods on a sample vehicle data sets and to
optimize the success rate of the trained detector by providing efficient
results for vehicle detection by testing the trained vehicle detector on the
test data. The working method consists of six main stages. These are
respectively; loading the data set, the design of the convolutional neural
network, configuration of training options, training of the Faster R-CNN object
detector and evaluation of trained detector. In addition, in the scope of the
study, Faster R-CNN, R-CNN deep learning methods were mentioned and
experimental analysis comparisons were made with the results obtained from
vehicle detection.
Multi-scale Location-aware Kernel Representation for Object Detection
Authors: Hao Wang, Qilong Wang, Mingqi Gao, Peihua Li, WangMeng Zuo
Date: 2018-04-02 00:00:00
Proceeding: CVPR 2018 6
Tasks: General Classification, Object, object-detection, Object Detection

Although Faster R-CNN and its variants have shown promising performance in
object detection, they only exploit simple first-order representation of object
proposals for final classification and regression. Recent classification
methods demonstrate that the integration of high-order statistics into deep
convolutional neural networks can achieve impressive improvement, but their
goal is to model whole images by discarding location information so that they
cannot be directly adopted to object detection. In this paper, we make an
attempt to exploit high-order statistics in object detection, aiming at
generating more discriminative representations for proposals to enhance the
performance of detectors. To this end, we propose a novel Multi-scale
Location-aware Kernel Representation (MLKP) to capture high-order statistics of
deep features in proposals. Our MLKP can be efficiently computed on a modified
multi-scale feature map using a low-dimensional polynomial kernel
approximation.Moreover, different from existing orderless global
representations based on high-order statistics, our proposed MLKP is location
retentive and sensitive so that it can be flexibly adopted to object detection.
Through integrating into Faster R-CNN schema, the proposed MLKP achieves very
competitive performance with state-of-the-art methods, and improves Faster
R-CNN by 4.9% (mAP), 4.7% (mAP) and 5.0% (AP at IOU=[0.5:0.05:0.95]) on PASCAL
VOC 2007, VOC 2012 and MS COCO benchmarks, respectively. Code is available at:
https://github.com/Hwang64/MLKP.
High-quality nonparallel voice conversion based on cycle-consistent adversarial network
Authors: Fuming Fang, Junichi Yamagishi, Isao Echizen, Jaime Lorenzo-Trueba
Date: 2018-04-02 00:00:00
Tasks: Generative Adversarial Network, Image-to-Image Translation, Speech Synthesis, Translation, Vocal Bursts Intensity Prediction, Voice Conversion

Although voice conversion (VC) algorithms have achieved remarkable success
along with the development of machine learning, superior performance is still
difficult to achieve when using nonparallel data. In this paper, we propose
using a cycle-consistent adversarial network (CycleGAN) for nonparallel
data-based VC training. A CycleGAN is a generative adversarial network (GAN)
originally developed for unpaired image-to-image translation. A subjective
evaluation of inter-gender conversion demonstrated that the proposed method
significantly outperformed a method based on the Merlin open source neural
network speech synthesis system (a parallel VC system adapted for our setup)
and a GAN-based parallel VC system. This is the first research to show that the
performance of a nonparallel VC method can exceed that of state-of-the-art
parallel VC methods.
Face Alignment in Full Pose Range: A 3D Total Solution
Authors: Xiangyu Zhu, Xiaoming Liu, Zhen Lei, Stan Z. Li
Date: 2018-04-02 00:00:00
Tasks: 3D Pose Estimation, Depth Image Estimation, Face Alignment, Face Model, Face Reconstruction, Pose Estimation

Face alignment, which fits a face model to an image and extracts the semantic
meanings of facial pixels, has been an important topic in the computer vision
community. However, most algorithms are designed for faces in small to medium
poses (yaw angle is smaller than 45 degrees), which lack the ability to align
faces in large poses up to 90 degrees. The challenges are three-fold. Firstly,
the commonly used landmark face model assumes that all the landmarks are
visible and is therefore not suitable for large poses. Secondly, the face
appearance varies more drastically across large poses, from the frontal view to
the profile view. Thirdly, labelling landmarks in large poses is extremely
challenging since the invisible landmarks have to be guessed. In this paper, we
propose to tackle these three challenges in an new alignment framework termed
3D Dense Face Alignment (3DDFA), in which a dense 3D Morphable Model (3DMM) is
fitted to the image via Cascaded Convolutional Neural Networks. We also utilize
3D information to synthesize face images in profile views to provide abundant
samples for training. Experiments on the challenging AFLW database show that
the proposed approach achieves significant improvements over the
state-of-the-art methods.
Application of Grey Numbers to Assessment Processes
Authors: Michael Gr. Voskoglou, Yiannis Theodorou
Date: 2018-04-02 00:00:00

The theory of grey systems plays an important role in science,engineering and
in the everyday life in general for handling approximate data. In the present
paper grey numbers are used as a tool for assessing with linguistic expressions
the mean performance of a group of objects participating in a certain activity.
Two applications to student and football player assessment are also presented
illustrating our results.
A Study of Student Learning Skills Using Fuzzy Relation Equations
Authors: Michael Gr. Voskoglou
Date: 2018-04-02 00:00:00
Tasks: Relation

Fuzzy relation equations (FRE)are associated with the composition of binary
fuzzy relations. In the present work FRE are used as a tool for studying the
process of learning a new subject matter by a student class. A classroom
application and other csuitable examples connected to the student learning of
the derivative are also presented illustrating our results and useful
conclusions are obtained.
End-to-End Learning of Motion Representation for Video Understanding
Authors: Lijie Fan, Wenbing Huang, Chuang Gan, Stefano Ermon, Boqing Gong, Junzhou Huang
Date: 2018-04-02 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Recognition, Optical Flow Estimation, Video Understanding

Despite the recent success of end-to-end learned representations,
hand-crafted optical flow features are still widely used in video analysis
tasks. To fill this gap, we propose TVNet, a novel end-to-end trainable neural
network, to learn optical-flow-like features from data. TVNet subsumes a
specific optical flow solver, the TV-L1 method, and is initialized by unfolding
its optimization iterations as neural layers. TVNet can therefore be used
directly without any extra learning. Moreover, it can be naturally concatenated
with other task-specific networks to formulate an end-to-end architecture, thus
making our method more efficient than current multi-stage approaches by
avoiding the need to pre-compute and store features on disk. Finally, the
parameters of the TVNet can be further fine-tuned by end-to-end training. This
enables TVNet to learn richer and task-specific patterns beyond exact optical
flow. Extensive experiments on two action recognition benchmarks verify the
effectiveness of the proposed approach. Our TVNet achieves better accuracies
than all compared methods, while being competitive with the fastest counterpart
in terms of features extraction time.