pwc-archive/papers-with-abstracts huggingface dataset:
Ensemble learning with 3D convolutional neural networks for connectome-based prediction
Authors: Meenakshi Khosla, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu
Date: 2018-09-11 00:00:00
Tasks: BIG-bench Machine Learning, Ensemble Learning

The specificty and sensitivity of resting state functional MRI (rs-fMRI) measurements depend on pre-processing choices, such as the parcellation scheme used to define regions of interest (ROIs). In this study, we critically evaluate the effect of brain parcellations on machine learning models applied to rs-fMRI data. Our experiments reveal a remarkable trend: On average, models with stochastic parcellations consistently perform as well as models with widely used atlases at the same spatial scale. We thus propose an ensemble learning strategy to combine the predictions from models trained on connectivity data extracted using different (e.g., stochastic) parcellations. We further present an implementation of our ensemble learning strategy with a novel 3D Convolutional Neural Network (CNN) approach. The proposed CNN approach takes advantage of the full-resolution 3D spatial structure of rs-fMRI data and fits non-linear predictive models. Our ensemble CNN framework overcomes the limitations of traditional machine learning models for connectomes that often rely on region-based summary statistics and/or linear models. We showcase our approach on a classification (autism patients versus healthy controls) and a regression problem (prediction of subject's age), and report promising results.
Texture and Steerability based Image Authentication
Authors: S.B.G. Tilak Babu, Ch. Srinivasa Rao
Date: 2018-01-18 00:00:00
Proceeding: Conference 2018 1

Copy-Move Forgery Detection (CMFD) method is
useful for identifying copy and pasted portions in an image. CMFD
has demand in forensic investigation, legal evidence and in many
other fields. In this paper, the gists of different newly arrived
methodologies in current literature are discussed. Some existing
methodologies can be able to localize the forged region and some are
not. An efficient method for localization of copy move forgery is
proposed in this work for identifying forgery. In the proposed
methodology, CMFD is achieved by giving suspected image to
Steerable Pyramid Transform (SPT), Local Binary Pattern (LBP) is
applied on each oriented subband obtained from SPT to extract
feature set, then it is used to trained Support Vector Machine (SVM)
to classify images into forged or not. Then localization process is
carried out on forged images. Results of proposed methodology are
showing robustness even though the forged image has undergone
some post processing attacks viz., rotation, flip, JPEG compression.
Image forgery detection for high resolution images using SIFT and RANSAC algorithm
Authors: S.B.G. Tilak Babu, Ch. Srinivasa Rao
Date: 2018-03-22 00:00:00
Proceeding: Conference 2018 3
Tasks: Image Forgery Detection, Vocal Bursts Intensity Prediction

Cloning (copy-move forgery) is a malicious
tampering attack with digital images where a part of image is
copied and pasted within the image to conceal the important
details of image without any obvious traces of manipulation. This
type of tampering attacks leaves a big question of authenticity of
images to the forensics. Many techniques are proposed in the past
few years after powerful software’s are developed to manipulate
the image. The proposed scheme is involved with both the block
based and feature point extraction based techniques to extract
the forged regions more accurately. The proposed algorithm
mainly involves in matching the tentacles of same features
extracted from each block by computing the dot product between
the unit vectors. Random Sample Consensus (RANSAC)
algorithm is used to extract the matched regions. The
experimental result of the algorithm which is proposed indicates
that, it can extract more accurate results compared with existing
forgery detection methods.
SPECMAR: Fast Heart Rate Estimation from PPG Signal using a Modified Spectral Subtraction Scheme with Composite Motion Artifacts Reference Generation
Authors: Mohammad Tariqul Islam, Sk. Tanvir Ahmed, Celia Shahnaz, Shaikh Anowarul Fattah
Date: 2018-10-15 00:00:00
Tasks: Heart rate estimation

The task of heart rate estimation using photoplethysmographic (PPG) signal is
challenging due to the presence of various motion artifacts in the recorded
signals. In this paper, a fast algorithm for heart rate estimation based on
modified SPEctral subtraction scheme utilizing Composite Motion Artifacts
Reference generation (SPECMAR) is proposed using two-channel PPG and three-axis
accelerometer signals. First, the preliminary noise reduction is obtained by
filtering unwanted frequency components from the recorded signals. Next, a
composite motion artifacts reference generation method is developed to be
employed in the proposed SPECMAR algorithm for motion artifacts reduction. The
heart rate is then computed from the noise and motion artifacts reduced PPG
signal. Finally, a heart rate tracking algorithm is proposed considering
neighboring estimates. The performance of the SPECMAR algorithm has been tested
on publicly available PPG database. The average heart rate estimation error is
found to be 2.09 BPM on 23 recordings. The Pearson correlation is 0.9907. Due
to low computational complexity, the method is faster than the comparing
methods. The low estimation error, smooth and fast heart rate tracking makes
SPECMAR an ideal choice to be implemented in wearable devices.
Reservoir observers: Model-free inference of unmeasured variables in chaotic systems
Authors: Zhixin Lu, Jaideep Pathak, Brian Hunt, Michelle Girvan, Roger Brockett, and Edward Ott
Date: 2017-04-05 00:00:00
Proceeding: Chaos 27, 041102 (2017) 2017 4
Tasks: Time Series Analysis

Deducing the state of a dynamical system as a function of time from a limited number of concurrent system state measurements is an important problem of great practical utility. A scheme that accomplishes this is called an “observer.” We consider the case in which a model of the system is unavailable or insufficiently accurate, but “training” time series data of the desired state variables are available for a short period of time, and a limited number of other system variables are continually measured. We propose a solution to this problem using networks of neuron-like units known as “reservoir computers.” The measurements that are continually available are input to the network, which is trained with the limited-time data to output estimates of the desired state variables. We demonstrate our method, which we call a “reservoir observer,” using the Rössler system, the Lorenz system, and the spatiotemporally chaotic Kuramoto–Sivashinsky equation. Subject to the condition of observability (i.e., whether it is in principle possible, by any means, to infer the desired unmeasured variables from the measured variables), we show that the reservoir observer can be a very effective and versatile tool for robustly reconstructing unmeasured dynamical system variables.
Knowing the state of a dynamical system as it evolves in time is important for a variety of applications. This paper proposes a general-purpose method for inferring unmeasured state variables from a limited set of ongoing measurements. Our method is intended for situations in which mathematical models of system dynamics are unavailable or are insufficiently accurate to perform the desired inference. We use the machine-learning technique called “reservoir computing,” with which we construct a system-independent means of processing the measurements. A key point is the extent to which this approach is “universal.” That is, our examples show that the same reservoir can be trained to infer the state of different systems. It is the training that relates to a specific system, not the “hardware.” The reservoir hardware plays a similar role to an animal's brain, which retrains itself as the system represented by its body and environment changes.
Statistical shape analysis of simplified neuronal trees
Authors: Adam Duncan, Eric Klassen, and Anuj Srivastava
Date: 2018-11-03 00:00:00
Proceeding: journal 2018 11
Tasks: Clustering

Neuron morphology plays a central role in characterizing cognitive health and functionality of brain structures. The problem of quantifying neuron shapes and capturing statistical variability of shapes is difficult because neurons differ both in geometry and in topology. This paper develops a mathematical representation of neuronal trees, restricting to the trees that consist of: (1) a main branch viewed as a parameterized curve in 
R
3
R3
, and (2) some number of secondary branches—also parameterized curves in 
R
3
R3
—which emanate from the main branch at arbitrary points. It imposes a metric on the representation space, in order to compare neuronal shapes, and to obtain optimal deformations (geodesics) across arbitrary trees. The key idea is to impose certain equivalence relations that allow trees with different geometries and topologies to be compared efficiently. The combinatorial problem of matching side branches across trees is reduced to a linear assignment with well-known efficient solutions. This framework is then applied to comparing, clustering, and classifying neurons using fully automated algorithms. The framework is illustrated on three datasets of neuron reconstructions, specifically showing geodesics paths and cross-validated classification between experimental groups.
gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo
Authors: Nestor Gonzalez Lopez, Yue Leire Erro Nuin, Elias Barba Moral, Lander Usategui San Juan, Alejandro Solano Rueda, Víctor Mayoral Vilches, Risto Kojcev
Date: 2019-03-14 00:00:00
Tasks: Benchmarking, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Reinforcement Learning

This paper presents an upgraded, real world application oriented version of
gym-gazebo, the Robot Operating System (ROS) and Gazebo based Reinforcement
Learning (RL) toolkit, which complies with OpenAI Gym. The content discusses
the new ROS 2 based software architecture and summarizes the results obtained
using Proximal Policy Optimization (PPO). Ultimately, the output of this work
presents a benchmarking system for robotics that allows different techniques
and algorithms to be compared using the same virtual conditions. We have
evaluated environments with different levels of complexity of the Modular
Articulated Robotic Arm (MARA), reaching accuracies in the millimeter scale.
The converged results show the feasibility and usefulness of the gym-gazebo 2
toolkit, its potential and applicability in industrial use cases, using modular
robots.
SimulCap : Single-View Human Performance Capture with Cloth Simulation
Authors: Tao Yu, Zerong Zheng, Yuan Zhong, Jianhui Zhao, Qionghai Dai, Gerard Pons-Moll, Yebin Liu
Date: 2019-03-15 00:00:00
Proceeding: CVPR 2019 6

This paper proposes a new method for live free-viewpoint human performance
capture with dynamic details (e.g., cloth wrinkles) using a single RGBD camera.
Our main contributions are: (i) a multi-layer representation of garments and
body, and (ii) a physics-based performance capture procedure. We first digitize
the performer using multi-layer surface representation, which includes the
undressed body surface and separate clothing meshes. For performance capture,
we perform skeleton tracking, cloth simulation, and iterative depth fitting
sequentially for the incoming frame. By incorporating cloth simulation into the
performance capture pipeline, we can simulate plausible cloth dynamics and
cloth-body interactions even in the occluded regions, which was not possible in
previous capture methods. Moreover, by formulating depth fitting as a physical
process, our system produces cloth tracking results consistent with the depth
observation while still maintaining physical constraints. Results and
evaluations show the effectiveness of our method. Our method also enables new
types of applications such as cloth retargeting, free-viewpoint video rendering
and animations.
Unsupervised Person Re-identification by Soft Multilabel Learning
Authors: Hong-Xing Yu, Wei-Shi Zheng, An-Cong Wu, Xiaowei Guo, Shaogang Gong, Jian-Huang Lai
Date: 2019-03-15 00:00:00
Proceeding: CVPR 2019 6
Tasks: Person Re-Identification, Unsupervised Person Re-Identification

Although unsupervised person re-identification (RE-ID) has drawn increasing
research attentions due to its potential to address the scalability problem of
supervised RE-ID models, it is very challenging to learn discriminative
information in the absence of pairwise labels across disjoint camera views. To
overcome this problem, we propose a deep model for the soft multilabel learning
for unsupervised RE-ID. The idea is to learn a soft multilabel (real-valued
label likelihood vector) for each unlabeled person by comparing (and
representing) the unlabeled person with a set of known reference persons from
an auxiliary domain. We propose the soft multilabel-guided hard negative mining
to learn a discriminative embedding for the unlabeled target domain by
exploring the similarity consistency of the visual features and the soft
multilabels of unlabeled target pairs. Since most target pairs are cross-view
pairs, we develop the cross-view consistent soft multilabel learning to achieve
the learning goal that the soft multilabels are consistently good across
different camera views. To enable effecient soft multilabel learning, we
introduce the reference agent learning to represent each reference person by a
reference agent in a joint embedding. We evaluate our unified deep model on
Market-1501 and DukeMTMC-reID. Our model outperforms the state-of-the-art
unsupervised RE-ID methods by clear margins. Code is available at
https://github.com/KovenYu/MAR.
SceneCode: Monocular Dense Semantic Reconstruction using Learned Encoded Scene Representations
Authors: Shuaifeng Zhi, Michael Bloesch, Stefan Leutenegger, Andrew J. Davison
Date: 2019-03-15 00:00:00
Proceeding: CVPR 2019 6

Systems which incrementally create 3D semantic maps from image sequences must
store and update representations of both geometry and semantic entities.
However, while there has been much work on the correct formulation for
geometrical estimation, state-of-the-art systems usually rely on simple
semantic representations which store and update independent label estimates for
each surface element (depth pixels, surfels, or voxels). Spatial correlation is
discarded, and fused label maps are incoherent and noisy.
  We introduce a new compact and optimisable semantic representation by
training a variational auto-encoder that is conditioned on a colour image.
Using this learned latent space, we can tackle semantic label fusion by jointly
optimising the low-dimenional codes associated with each of a set of
overlapping images, producing consistent fused label maps which preserve
spatial correlation. We also show how this approach can be used within a
monocular keyframe based semantic mapping system where a similar code approach
is used for geometry. The probabilistic formulation allows a flexible
formulation where we can jointly estimate motion, geometry and semantics in a
unified optimisation.
MFAS: Multimodal Fusion Architecture Search
Authors: Juan-Manuel Pérez-Rúa, Valentin Vielzeuf, Stéphane Pateux, Moez Baccouche, Frédéric Jurie
Date: 2019-03-15 00:00:00
Tasks: Action Recognition, General Classification, Neural Architecture Search, Temporal Action Localization

We tackle the problem of finding good architectures for multimodal
classification problems. We propose a novel and generic search space that spans
a large number of possible fusion architectures. In order to find an optimal
architecture for a given dataset in the proposed search space, we leverage an
efficient sequential model-based exploration approach that is tailored for the
problem. We demonstrate the value of posing multimodal fusion as a neural
architecture search problem by extensive experimentation on a toy dataset and
two other real multimodal datasets. We discover fusion architectures that
exhibit state-of-the-art performance for problems with different domain and
dataset size, including the NTU RGB+D dataset, the largest multi-modal action
recognition dataset available.
High Frame Rate Video Reconstruction based on an Event Camera
Authors: Liyuan Pan, Richard Hartley, Cedric Scheerlinck, Miaomiao Liu, Xin Yu, Yuchao Dai
Date: 2019-03-12 00:00:00
Tasks: Video Generation, Video Reconstruction, Vocal Bursts Intensity Prediction

Event-based cameras measure intensity changes (called `events') with microsecond accuracy under high-speed motion and challenging lighting conditions. With the `active pixel sensor' (APS), the `Dynamic and Active-pixel Vision Sensor' (DAVIS) allows the simultaneous output of intensity frames and events. However, the output images are captured at a relatively low frame rate and often suffer from motion blur. A blurred image can be regarded as the integral of a sequence of latent images, while events indicate changes between the latent images. Thus, we are able to model the blur-generation process by associating event data to a latent sharp image. Based on the abundant event data alongside a low frame rate, easily blurred images, we propose a simple yet effective approach to reconstruct high-quality and high frame rate sharp videos. Starting with a single blurred frame and its event data from DAVIS, we propose the Event-based Double Integral (EDI) model and solve it by adding regularization terms. Then, we extend it to multiple Event-based Double Integral (mEDI) model to get more smooth results based on multiple images and their events. Furthermore, we provide a new and more efficient solver to minimize the proposed energy model. By optimizing the energy function, we achieve significant improvements in removing blur and the reconstruction of a high temporal resolution video. The video generation is based on solving a simple non-convex optimization problem in a single scalar variable. Experimental results on both synthetic and real datasets demonstrate the superiority of our mEDI model and optimization method compared to the state-of-the-art.
Inserting Videos into Videos
Authors: Donghoon Lee, Tomas Pfister, Ming-Hsuan Yang
Date: 2019-03-15 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object, Object Tracking, Person Re-Identification

In this paper, we introduce a new problem of manipulating a given video by
inserting other videos into it. Our main task is, given an object video and a
scene video, to insert the object video at a user-specified location in the
scene video so that the resulting video looks realistic. We aim to handle
different object motions and complex backgrounds without expensive segmentation
annotations. As it is difficult to collect training pairs for this problem, we
synthesize fake training pairs that can provide helpful supervisory signals
when training a neural network with unpaired real data. The proposed network
architecture can take both real and fake pairs as input and perform both
supervised and unsupervised training in an adversarial learning scheme. To
synthesize a realistic video, the network renders each frame based on the
current input and previous frames. Within this framework, we observe that
injecting noise into previous frames while generating the current frame
stabilizes training. We conduct experiments on real-world videos in object
tracking and person re-identification benchmark datasets. Experimental results
demonstrate that the proposed algorithm is able to synthesize long sequences of
realistic videos with a given object video inserted.
Selective Kernel Networks
Authors: Xiang Li, Wenhai Wang, Xiaolin Hu, Jian Yang
Date: 2019-03-15 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Classification

In standard Convolutional Neural Networks (CNNs), the receptive fields of
artificial neurons in each layer are designed to share the same size. It is
well-known in the neuroscience community that the receptive field size of
visual cortical neurons are modulated by the stimulus, which has been rarely
considered in constructing CNNs. We propose a dynamic selection mechanism in
CNNs that allows each neuron to adaptively adjust its receptive field size
based on multiple scales of input information. A building block called
Selective Kernel (SK) unit is designed, in which multiple branches with
different kernel sizes are fused using softmax attention that is guided by the
information in these branches. Different attentions on these branches yield
different sizes of the effective receptive fields of neurons in the fusion
layer. Multiple SK units are stacked to a deep network termed Selective Kernel
Networks (SKNets). On the ImageNet and CIFAR benchmarks, we empirically show
that SKNet outperforms the existing state-of-the-art architectures with lower
model complexity. Detailed analyses show that the neurons in SKNet can capture
target objects with different scales, which verifies the capability of neurons
for adaptively adjusting their receptive field sizes according to the input.
The code and models are available at https://github.com/implus/SKNet.
Learning Robust Representations by Projecting Superficial Statistics Out
Authors: Haohan Wang, Zexue He, Zachary C. Lipton, Eric P. Xing
Date: 2019-03-02 00:00:00
Proceeding: ICLR 2019 5
Tasks: Domain Generalization

Despite impressive performance as evaluated on i.i.d. holdout data, deep
neural networks depend heavily on superficial statistics of the training data
and are liable to break under distribution shift. For example, subtle changes
to the background or texture of an image can break a seemingly powerful
classifier. Building on previous work on domain generalization, we hope to
produce a classifier that will generalize to previously unseen domains, even
when domain identifiers are not available during training. This setting is
challenging because the model may extract many distribution-specific
(superficial) signals together with distribution-agnostic (semantic) signals.
To overcome this challenge, we incorporate the gray-level co-occurrence matrix
(GLCM) to extract patterns that our prior knowledge suggests are superficial:
they are sensitive to the texture but unable to capture the gestalt of an
image. Then we introduce two techniques for improving our networks'
out-of-sample performance. The first method is built on the reverse gradient
method that pushes our model to learn representations from which the GLCM
representation is not predictable. The second method is built on the
independence introduced by projecting the model's representation onto the
subspace orthogonal to GLCM representation's. We test our method on the battery
of standard domain generalization data sets and, interestingly, achieve
comparable or better performance as compared to other domain generalization
methods that explicitly require samples from the target distribution for
training.
Formality Style Transfer with Hybrid Textual Annotations
Authors: Ruochen Xu, Tao Ge, Furu Wei
Date: 2019-03-15 00:00:00
Tasks: Formality Style Transfer, Sentence, Style Transfer, Text Style Transfer, Unsupervised Text Style Transfer

Formality style transformation is the task of modifying the formality of a
given sentence without changing its content. Its challenge is the lack of
large-scale sentence-aligned parallel data. In this paper, we propose an
omnivorous model that takes parallel data and formality-classified data jointly
to alleviate the data sparsity issue. We empirically demonstrate the
effectiveness of our approach by achieving the state-of-art performance on a
recently proposed benchmark dataset of formality transfer. Furthermore, our
model can be readily adapted to other unsupervised text style transfer tasks
like unsupervised sentiment transfer and achieve competitive results on three
widely recognized benchmarks.