pwc-archive/papers-with-abstracts huggingface dataset:
SAR Image Colorization: Converting Single-Polarization to Fully Polarimetric Using Deep Neural Networks
Authors: Qian Song, Feng Xu, Ya-Qiu Jin
Date: 2017-07-22 00:00:00
Tasks: Colorization, Image Colorization

A deep neural networks based method is proposed to convert single
polarization grayscale SAR image to fully polarimetric. It consists of two
components: a feature extractor network to extract hierarchical multi-scale
spatial features of grayscale SAR image, followed by a feature translator
network to map spatial feature to polarimetric feature with which the
polarimetric covariance matrix of each pixel can be reconstructed. Both
qualitative and quantitative experiments with real fully polarimetric data are
conducted to show the efficacy of the proposed method. The reconstructed
full-pol SAR image agrees well with the true full-pol image. Existing PolSAR
applications such as model-based decomposition and unsupervised classification
can be applied directly to the reconstructed full-pol SAR images. This
framework can be easily extended to reconstruction of full-pol data from
compact-pol data. The experiment results also show that the proposed method
could be potentially used for interference removal on the cross-polarization
channel.
Exercise Motion Classification from Large-Scale Wearable Sensor Data Using Convolutional Neural Networks
Authors: Terry Taewoong Um, Vahid Babakeshizadeh, Dana Kulić
Date: 2016-10-22 00:00:00
Tasks: General Classification, Time Series, Time Series Analysis

The ability to accurately identify human activities is essential for
developing automatic rehabilitation and sports training systems. In this paper,
large-scale exercise motion data obtained from a forearm-worn wearable sensor
are classified with a convolutional neural network (CNN). Time-series data
consisting of accelerometer and orientation measurements are formatted as
images, allowing the CNN to automatically extract discriminative features. A
comparative study on the effects of image formatting and different CNN
architectures is also presented. The best performing configuration classifies
50 gym exercises with 92.1% accuracy.
Vertex Nomination Via Seeded Graph Matching
Authors: Heather G. Patsolic, Youngser Park, Vince Lyzinski, Carey E. Priebe
Date: 2017-05-01 00:00:00
Tasks: Graph Matching

Consider two networks on overlapping, non-identical vertex sets. Given vertices of interest in the first network, we seek to identify the corresponding vertices, if any exist, in the second network. While in moderately sized networks graph matching methods can be applied directly to recover the missing correspondences, herein we present a principled methodology appropriate for situations in which the networks are too large for brute-force graph matching. Our methodology identifies vertices in a local neighborhood of the vertices of interest in the first network that have verifiable corresponding vertices in the second network. Leveraging these known correspondences, referred to as seeds, we match the induced subgraphs in each network generated by the neighborhoods of these verified seeds, and rank the vertices of the second network in terms of the most likely matches to the original vertices of interest. We demonstrate the applicability of our methodology through simulations and real data examples.
Inspiring Computer Vision System Solutions
Authors: Julian Zilly, Amit Boyarski, Micael Carvalho, Amir Atapour Abarghouei, Konstantinos Amplianitis, Aleksandr Krasnov, Massimiliano Mancini, Hernán Gonzalez, Riccardo Spezialetti, Carlos Sampedro Pérez, Hao Li
Date: 2017-07-22 00:00:00

The "digital Michelangelo project" was a seminal computer vision project in
the early 2000's that pushed the capabilities of acquisition systems and
involved multiple people from diverse fields, many of whom are now leaders in
industry and academia. Reviewing this project with modern eyes provides us with
the opportunity to reflect on several issues, relevant now as then to the field
of computer vision and research in general, that go beyond the technical
aspects of the work.
  This article was written in the context of a reading group competition at the
week-long International Computer Vision Summer School 2017 (ICVSS) on Sicily,
Italy. To deepen the participants understanding of computer vision and to
foster a sense of community, various reading groups were tasked to highlight
important lessons which may be learned from provided literature, going beyond
the contents of the paper. This report is the winning entry of this guided
discourse (Fig. 1). The authors closely examined the origins, fruits and most
importantly lessons about research in general which may be distilled from the
"digital Michelangelo project". Discussions leading to this report were held
within the group as well as with Hao Li, the group mentor.
Towards Decoding as Continuous Optimization in Neural Machine Translation
Authors: Cong Duy Vu Hoang, Gholamreza Haffari, Trevor Cohn
Date: 2017-01-11 00:00:00
Tasks: Machine Translation, NMT, Reranking, Translation

We propose a novel decoding approach for neural machine translation (NMT)
based on continuous optimisation. We convert decoding - basically a discrete
optimization problem - into a continuous optimization problem. The resulting
constrained continuous optimisation problem is then tackled using
gradient-based methods. Our powerful decoding framework enables decoding
intractable models such as the intersection of left-to-right and right-to-left
(bidirectional) as well as source-to-target and target-to-source (bilingual)
NMT models. Our empirical results show that our decoding framework is
effective, and leads to substantial improvements in translations generated from
the intersected models where the typical greedy or beam search is not feasible.
We also compare our framework against reranking, and analyse its advantages and
disadvantages.
MoodSwipe: A Soft Keyboard that Suggests Messages Based on User-Specified Emotions
Authors: Chieh-Yang Huang, Tristan Labetoulle, Ting-Hao Kenneth Huang, Yi-Pei Chen, Hung-Chen Chen, Vallari Srivastava, Lun-Wei Ku
Date: 2017-07-22 00:00:00
Tasks: Emotion Classification, General Classification

We present MoodSwipe, a soft keyboard that suggests text messages given the
user-specified emotions utilizing the real dialog data. The aim of MoodSwipe is
to create a convenient user interface to enjoy the technology of emotion
classification and text suggestion, and at the same time to collect labeled
data automatically for developing more advanced technologies. While users
select the MoodSwipe keyboard, they can type as usual but sense the emotion
conveyed by their text and receive suggestions for their message as a benefit.
In MoodSwipe, the detected emotions serve as the medium for suggested texts,
where viewing the latter is the incentive to correcting the former. We conduct
several experiments to show the superiority of the emotion classification
models trained on the dialog data, and further to verify good emotion cues are
important context for text suggestion.
Dual Principal Component Pursuit
Authors: Manolis C. Tsakiris, Rene Vidal
Date: 2015-10-15 00:00:00

We consider the problem of learning a linear subspace from data corrupted by outliers. Classical approaches are typically designed for the case in which the subspace dimension is small relative to the ambient dimension. Our approach works with a dual representation of the subspace and hence aims to find its orthogonal complement; as such, it is particularly suitable for subspaces whose dimension is close to the ambient dimension (subspaces of high relative dimension). We pose the problem of computing normal vectors to the inlier subspace as a non-convex $\ell_1$ minimization problem on the sphere, which we call Dual Principal Component Pursuit (DPCP) problem. We provide theoretical guarantees under which every global solution to DPCP is a vector in the orthogonal complement of the inlier subspace. Moreover, we relax the non-convex DPCP problem to a recursion of linear programs whose solutions are shown to converge in a finite number of steps to a vector orthogonal to the subspace. In particular, when the inlier subspace is a hyperplane, the solutions to the recursion of linear programs converge to the global minimum of the non-convex DPCP problem in a finite number of steps. We also propose algorithms based on alternating minimization and iteratively re-weighted least squares, which are suitable for dealing with large-scale data. Experiments on synthetic data show that the proposed methods are able to handle more outliers and higher relative dimensions than current state-of-the-art methods, while experiments in the context of the three-view geometry problem in computer vision suggest that the proposed methods can be a useful or even superior alternative to traditional RANSAC-based approaches for computer vision and other applications.
An Event-based Fast Movement Detection Algorithm for a Positioning Robot Using POWERLINK Communication
Authors: Juan Barrios-Avilés, Taras Iakymchuk, Jorge Samaniego, Alfredo Rosado-Muñoz
Date: 2017-07-22 00:00:00

This work develops a tracking system based on an event-based camera. A
bioinspired filtering algorithm to reduce noise and transmitted data while
keeping the main features at the scene is implemented in FPGA which also serves
as a network node. POWERLINK IEEE 61158 industrial network is used to
communicate the FPGA with a controller connected to a self-developed two axis
servo-controlled robot. The FPGA includes the network protocol to integrate the
event-based camera as any other existing network node. The inverse kinematics
for the robot is included in the controller. In addition, another network node
is used to control pneumatic valves blowing the ball at different speed and
trajectories. To complete the system and provide a comparison, a traditional
frame-based camera is also connected to the controller. The imaging data for
the tracking system are obtained either from the event-based or frame-based
camera. Results show that the robot can accurately follow the ball using fast
image recognition, with the intrinsic advantages of the event-based system
(size, price, power). This works shows how the development of new equipment and
algorithms can be efficiently integrated in an industrial system, merging
commercial industrial equipment with the new devices so that new technologies
can rapidly enter into the industrial field.
Native Language Identification on Text and Speech
Authors: Marcos Zampieri, Alina Maria Ciobanu, Liviu P. Dinu
Date: 2017-07-22 00:00:00
Proceeding: WS 2017 9
Tasks: Language Identification, Native Language Identification

This paper presents an ensemble system combining the output of multiple SVM
classifiers to native language identification (NLI). The system was submitted
to the NLI Shared Task 2017 fusion track which featured students essays and
spoken responses in form of audio transcriptions and iVectors by non-native
English speakers of eleven native languages. Our system competed in the
challenge under the team name ZCD and was based on an ensemble of SVM
classifiers trained on character n-grams achieving 83.58% accuracy and ranking
3rd in the shared task.
Emotion Recognition by Body Movement Representation on the Manifold of Symmetric Positive Definite Matrices
Authors: Mohamed Daoudi, Stefano Berretti, Pietro Pala, Yvonne Delevoye, Alberto del Bimbo
Date: 2017-07-22 00:00:00
Tasks: Emotion Classification, Emotion Recognition, General Classification

Emotion recognition is attracting great interest for its potential
application in a multitude of real-life situations. Much of the Computer Vision
research in this field has focused on relating emotions to facial expressions,
with investigations rarely including more than upper body. In this work, we
propose a new scenario, for which emotional states are related to 3D dynamics
of the whole body motion. To address the complexity of human body movement, we
used covariance descriptors of the sequence of the 3D skeleton joints, and
represented them in the non-linear Riemannian manifold of Symmetric Positive
Definite matrices. In doing so, we exploited geodesic distances and geometric
means on the manifold to perform emotion classification. Using sequences of
spontaneous walking under the five primary emotional states, we report a method
that succeeded in classifying the different emotions, with comparable
performance to those observed in a human-based force-choice classification
task.
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning
Authors: Dipendra Misra, John Langford, Yoav Artzi
Date: 2017-04-28 00:00:00
Proceeding: EMNLP 2017 9
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose to directly map raw visual observations and text input to actions
for instruction execution. While existing approaches assume access to
structured environment representations or use a pipeline of separately trained
models, we learn a single model to jointly reason about linguistic and visual
input. We use reinforcement learning in a contextual bandit setting to train a
neural network agent. To guide the agent's exploration, we use reward shaping
with different forms of supervision. Our approach does not require intermediate
representations, planning procedures, or training different models. We evaluate
in a simulated environment, and show significant improvements over supervised
learning and common reinforcement learning variants.
Coarse-to-Fine Lifted MAP Inference in Computer Vision
Authors: Haroun Habeeb, Ankit Anand, Mausam, Parag Singla
Date: 2017-07-22 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

There is a vast body of theoretical research on lifted inference in
probabilistic graphical models (PGMs). However, few demonstrations exist where
lifting is applied in conjunction with top of the line applied algorithms. We
pursue the applicability of lifted inference for computer vision (CV), with the
insight that a globally optimal (MAP) labeling will likely have the same label
for two symmetric pixels. The success of our approach lies in efficiently
handling a distinct unary potential on every node (pixel), typical of CV
applications. This allows us to lift the large class of algorithms that model a
CV problem via PGM inference. We propose a generic template for coarse-to-fine
(C2F) inference in CV, which progressively refines an initial coarsely lifted
PGM for varying quality-time trade-offs. We demonstrate the performance of C2F
inference by developing lifted versions of two near state-of-the-art CV
algorithms for stereo vision and interactive image segmentation. We find that,
against flat algorithms, the lifted versions have a much superior anytime
performance, without any loss in final solution quality.
Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting
Authors: Li Sun, Gerardo Aragon-Camarasa, Simon Rogers, Rustam Stolkin, J. Paul Siebert
Date: 2017-07-22 00:00:00

This paper proposes a single-shot approach for recognising clothing
categories from 2.5D features. We propose two visual features, BSP (B-Spline
Patch) and TSD (Topology Spatial Distances) for this task. The local BSP
features are encoded by LLC (Locality-constrained Linear Coding) and fused with
three different global features. Our visual feature is robust to deformable
shapes and our approach is able to recognise the category of unknown clothing
in unconstrained and random configurations. We integrated the category
recognition pipeline with a stereo vision system, clothing instance detection,
and dual-arm manipulators to achieve an autonomous sorting system. To verify
the performance of our proposed method, we build a high-resolution RGBD
clothing dataset of 50 clothing items of 5 categories sampled in random
configurations (a total of 2,100 clothing samples). Experimental results show
that our approach is able to reach 83.2\% accuracy while classifying clothing
items which were previously unseen during training. This advances beyond the
previous state-of-the-art by 36.2\%. Finally, we evaluate the proposed approach
in an autonomous robot sorting system, in which the robot recognises a clothing
item from an unconstrained pile, grasps it, and sorts it into a box according
to its category. Our proposed sorting system achieves reasonable sorting
success rates with single-shot perception.
Black-Box Data-efficient Policy Search for Robotics
Authors: Konstantinos Chatzilygeroudis, Roberto Rama, Rituraj Kaushik, Dorian Goepp, Vassilis Vassiliades, Jean-Baptiste Mouret
Date: 2017-03-21 00:00:00
Tasks: Continuous Control, Reinforcement Learning, Reinforcement Learning (RL)

The most data-efficient algorithms for reinforcement learning (RL) in
robotics are based on uncertain dynamical models: after each episode, they
first learn a dynamical model of the robot, then they use an optimization
algorithm to find a policy that maximizes the expected return given the model
and its uncertainties. It is often believed that this optimization can be
tractable only if analytical, gradient-based algorithms are used; however,
these algorithms require using specific families of reward functions and
policies, which greatly limits the flexibility of the overall approach. In this
paper, we introduce a novel model-based RL algorithm, called Black-DROPS
(Black-box Data-efficient RObot Policy Search) that: (1) does not impose any
constraint on the reward function or the policy (they are treated as
black-boxes), (2) is as data-efficient as the state-of-the-art algorithm for
data-efficient RL in robotics, and (3) is as fast (or faster) than analytical
approaches when several cores are available. The key idea is to replace the
gradient-based optimization algorithm with a parallel, black-box algorithm that
takes into account the model uncertainties. We demonstrate the performance of
our new algorithm on two standard control benchmark problems (in simulation)
and a low-cost robotic manipulator (with a real robot).
Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception Module
Authors: Wuzhen Shi, Feng Jiang, Debin Zhao
Date: 2017-07-22 00:00:00
Tasks: Image Restoration, Image Super-Resolution, Super-Resolution

Traditional works have shown that patches in a natural image tend to
redundantly recur many times inside the image, both within the same scale, as
well as across different scales. Make full use of these multi-scale information
can improve the image restoration performance. However, the current proposed
deep learning based restoration methods do not take the multi-scale information
into account. In this paper, we propose a dilated convolution based inception
module to learn multi-scale information and design a deep network for single
image super-resolution. Different dilated convolution learns different scale
feature, then the inception module concatenates all these features to fuse
multi-scale information. In order to increase the reception field of our
network to catch more contextual information, we cascade multiple inception
modules to constitute a deep network to conduct single image super-resolution.
With the novel dilated convolution based inception module, the proposed
end-to-end single image super-resolution network can take advantage of
multi-scale information to improve image super-resolution performance.
Experimental results show that our proposed method outperforms many
state-of-the-art single image super-resolution methods.
Deep Networks for Compressed Image Sensing
Authors: Wuzhen Shi, Feng Jiang, Shengping Zhang, Debin Zhao
Date: 2017-07-22 00:00:00
Tasks: compressed sensing, Image Compression

The compressed sensing (CS) theory has been successfully applied to image
compression in the past few years as most image signals are sparse in a certain
domain. Several CS reconstruction models have been recently proposed and
obtained superior performance. However, there still exist two important
challenges within the CS theory. The first one is how to design a sampling
mechanism to achieve an optimal sampling efficiency, and the second one is how
to perform the reconstruction to get the highest quality to achieve an optimal
signal recovery. In this paper, we try to deal with these two problems with a
deep network. First of all, we train a sampling matrix via the network training
instead of using a traditional manually designed one, which is much appropriate
for our deep network based reconstruct process. Then, we propose a deep network
to recover the image, which imitates traditional compressed sensing
reconstruction processes. Experimental results demonstrate that our deep
networks based CS reconstruction method offers a very significant quality
improvement compared against state of the art ones.
The NLTK FrameNet API: Designing for Discoverability with a Rich Linguistic Resource
Authors: Nathan Schneider, Chuck Wooters
Date: 2017-03-21 00:00:00
Proceeding: EMNLP 2017 9

A new Python API, integrated within the NLTK suite, offers access to the
FrameNet 1.7 lexical database. The lexicon (structured in terms of frames) as
well as annotated sentences can be processed programatically, or browsed with
human-readable displays via the interactive Python prompt.
PatchShuffle Regularization
Authors: Guoliang Kang, Xuanyi Dong, Liang Zheng, Yi Yang
Date: 2017-07-22 00:00:00
Tasks: General Classification

This paper focuses on regularizing the training of the convolutional neural
network (CNN). We propose a new regularization approach named ``PatchShuffle``
that can be adopted in any classification-oriented CNN models. It is easy to
implement: in each mini-batch, images or feature maps are randomly chosen to
undergo a transformation such that pixels within each local patch are shuffled.
Through generating images and feature maps with interior orderless patches,
PatchShuffle creates rich local variations, reduces the risk of network
overfitting, and can be viewed as a beneficial supplement to various kinds of
training regularization techniques, such as weight decay, model ensemble and
dropout. Experiments on four representative classification datasets show that
PatchShuffle improves the generalization ability of CNN especially when the
data is scarce. Moreover, we empirically illustrate that CNN models trained
with PatchShuffle are more robust to noise and local changes in an image.
Generalized Rank Pooling for Activity Recognition
Authors: Anoop Cherian, Basura Fernando, Mehrtash Harandi, Stephen Gould
Date: 2017-04-07 00:00:00
Proceeding: CVPR 2017 7
Tasks: Action Recognition, Activity Recognition, Riemannian optimization, Temporal Action Localization

Most popular deep models for action recognition split video sequences into
short sub-sequences consisting of a few frames; frame-based features are then
pooled for recognizing the activity. Usually, this pooling step discards the
temporal order of the frames, which could otherwise be used for better
recognition. Towards this end, we propose a novel pooling method, generalized
rank pooling (GRP), that takes as input, features from the intermediate layers
of a CNN that is trained on tiny sub-sequences, and produces as output the
parameters of a subspace which (i) provides a low-rank approximation to the
features and (ii) preserves their temporal order. We propose to use these
parameters as a compact representation for the video sequence, which is then
used in a classification setup. We formulate an objective for computing this
subspace as a Riemannian optimization problem on the Grassmann manifold, and
propose an efficient conjugate gradient scheme for solving it. Experiments on
several activity recognition datasets show that our scheme leads to
state-of-the-art performance.