pwc-archive/papers-with-abstracts huggingface dataset:
Efficient and Robust Registration on the 3D Special Euclidean Group
Authors: Uttaran Bhattacharya, Venu Madhav Govindu
Date: 2019-04-11 00:00:00
Proceeding: ICCV 2019 10
Tasks: Motion Estimation

We present an accurate, robust and fast method for registration of 3D scans. Our motion estimation optimizes a robust cost function on the intrinsic representation of rigid motions, i.e., the Special Euclidean group $\mathbb{SE}(3)$. We exploit the geometric properties of Lie groups as well as the robustness afforded by an iteratively reweighted least squares optimization. We also generalize our approach to a joint multiview method that simultaneously solves for the registration of a set of scans. We demonstrate the efficacy of our approach by thorough experimental validation. Our approach significantly outperforms the state-of-the-art robust 3D registration method based on a line process in terms of both speed and accuracy. We also show that this line process method is a special case of our principled geometric solution. Finally, we also present scenarios where global registration based on feature correspondences fails but multiview ICP based on our robust motion estimation is successful.
Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach
Authors: Proteek Chandan Roy, Vishnu Naresh Boddeti
Date: 2019-04-11 00:00:00
Proceeding: CVPR 2019 6

Image recognition systems have demonstrated tremendous progress over the past
few decades thanks, in part, to our ability of learning compact and robust
representations of images. As we witness the wide spread adoption of these
systems, it is imperative to consider the problem of unintended leakage of
information from an image representation, which might compromise the privacy of
the data owner. This paper investigates the problem of learning an image
representation that minimizes such leakage of user information. We formulate
the problem as an adversarial non-zero sum game of finding a good embedding
function with two competing goals: to retain as much task dependent
discriminative image information as possible, while simultaneously minimizing
the amount of information, as measured by entropy, about other sensitive
attributes of the user. We analyze the stability and convergence dynamics of
the proposed formulation using tools from non-linear systems theory and compare
to that of the corresponding adversarial zero-sum game formulation that
optimizes likelihood as a measure of information content. Numerical experiments
on UCI, Extended Yale B, CIFAR-10 and CIFAR-100 datasets indicate that our
proposed approach is able to learn image representations that exhibit high task
performance while mitigating leakage of predefined sensitive information.
Generalizing Monocular 3D Human Pose Estimation in the Wild
Authors: Luyang Wang, Yan Chen, Zhenhua Guo, Keyuan Qian, Mude Lin, Hongsheng Li, Jimmy S. Ren
Date: 2019-04-11 00:00:00
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Monocular 3D Human Pose Estimation, Pose Estimation

The availability of the large-scale labeled 3D poses in the Human3.6M dataset
plays an important role in advancing the algorithms for 3D human pose
estimation from a still image. We observe that recent innovation in this area
mainly focuses on new techniques that explicitly address the generalization
issue when using this dataset, because this database is constructed in a highly
controlled environment with limited human subjects and background variations.
Despite such efforts, we can show that the results of the current methods are
still error-prone especially when tested against the images taken in-the-wild.
In this paper, we aim to tackle this problem from a different perspective. We
propose a principled approach to generate high quality 3D pose ground truth
given any in-the-wild image with a person inside. We achieve this by first
devising a novel stereo inspired neural network to directly map any 2D pose to
high quality 3D counterpart. We then perform a carefully designed geometric
searching scheme to further refine the joints. Based on this scheme, we build a
large-scale dataset with 400,000 in-the-wild images and their corresponding 3D
pose ground truth. This enables the training of a high quality neural network
model, without specialized training scheme and auxiliary loss function, which
performs favorably against the state-of-the-art 3D pose estimation methods. We
also evaluate the generalization ability of our model both quantitatively and
qualitatively. Results show that our approach convincingly outperforms the
previous methods. We make our dataset and code publicly available.
Restricted Isometry Property under High Correlations
Authors: Shiva Prasad Kasiviswanathan, Mark Rudelson
Date: 2019-04-11 00:00:00
Tasks: compressed sensing, Dimensionality Reduction, Vocal Bursts Intensity Prediction

Matrices satisfying the Restricted Isometry Property (RIP) play an important role in the areas of compressed sensing and statistical learning. RIP matrices with optimal parameters are mainly obtained via probabilistic arguments, as explicit constructions seem hard. It is therefore interesting to ask whether a fixed matrix can be incorporated into a construction of restricted isometries. In this paper, we construct a new broad ensemble of random matrices with dependent entries that satisfy the restricted isometry property. Our construction starts with a fixed (deterministic) matrix $X$ satisfying some simple stable rank condition, and we show that the matrix $XR$, where $R$ is a random matrix drawn from various popular probabilistic models (including, subgaussian, sparse, low-randomness, satisfying convex concentration property), satisfies the RIP with high probability. These theorems have various applications in signal recovery, random matrix theory, dimensionality reduction, etc. Additionally, motivated by an application for understanding the effectiveness of word vector embeddings popular in natural language processing and machine learning applications, we investigate the RIP of the matrix $XR^{(l)}$ where $R^{(l)}$ is formed by taking all possible (disregarding order) $l$-way entrywise products of the columns of a random matrix $R$.
CNN-Based Deep Architecture for Reinforced Concrete Delamination Segmentation Through Thermography
Authors: Chongsheng Cheng, Zhexiong Shang, Zhigang Shen
Date: 2019-04-11 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Delamination assessment of the bridge deck plays a vital role for bridge
health monitoring. Thermography as one of the nondestructive technologies for
delamination detection has the advantage of efficient data acquisition. But
there are challenges on the interpretation of data for accurate delamination
shape profiling. Due to the environmental variation and the irregular presence
of delamination size and depth, conventional processing methods based on
temperature contrast fall short in accurate segmentation of delamination.
Inspired by the recent development of deep learning architecture for image
segmentation, the Convolutional Neural Network (CNN) based framework was
investigated for the applicability of delamination segmentation under
variations in temperature contrast and shape diffusion. The models were
developed based on Dense Convolutional Network (DenseNet) and trained on
thermal images collected for mimicked delamination in concrete slabs with
different depths under experimental setup. The results suggested satisfactory
performance of accurate profiling the delamination shapes.
Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?
Authors: Sorami Hisamoto, Matt Post, Kevin Duh
Date: 2019-04-11 00:00:00
Proceeding: TACL 2020 1
Tasks: Machine Translation, Translation, Video Captioning

Data privacy is an important issue for "machine learning as a service" providers. We focus on the problem of membership inference attacks: given a data sample and black-box access to a model's API, determine whether the sample existed in the model's training data. Our contribution is an investigation of this problem in the context of sequence-to-sequence models, which are important in applications such as machine translation and video captioning. We define the membership inference problem for sequence generation, provide an open dataset based on state-of-the-art machine translation models, and report initial results on whether these models leak private information against several kinds of membership inference attacks.
Non-locally Encoder-Decoder Convolutional Network for Whole Brain QSM Inversion
Authors: Juan Liu, Kevin M. Koch
Date: 2019-04-11 00:00:00
Tasks: Decoder, Translation

Quantitative Susceptibility Mapping (QSM) reconstruction is a challenging
inverse problem driven by ill conditioning of its field-to -susceptibility
transformation. State-of-art QSM reconstruction methods either suffer from
image artifacts or long computation times, which limits QSM clinical
translation efforts. To overcome these limitations, a non-locally
encoder-decoder gated convolutional neural network is trained to infer whole
brain susceptibility map, using the local field and brain mask as the inputs.
The performance of the proposed method is evaluated relative to synthetic data,
a publicly available challenge dataset, and clinical datasets. The proposed
approach can outperform existing methods on quantitative metrics and visual
assessment of image sharpness and streaking artifacts. The estimated
susceptibility maps can preserve conspicuity of fine features and suppress
streaking artifacts. The demonstrated methods have potential value in advancing
QSM clinical research and aiding in the translation of QSM to clinical
operations.
Deep Neural Network Ensembles
Authors: Sean Tao
Date: 2019-04-11 00:00:00
Tasks: Decision Making

Current deep neural networks suffer from two problems; first, they are hard to interpret, and second, they suffer from overfitting. There have been many attempts to define interpretability in neural networks, but they typically lack causality or generality. A myriad of regularization techniques have been developed to prevent overfitting, and this has driven deep learning to become the hot topic it is today; however, while most regularization techniques are justified empirically and even intuitively, there is not much underlying theory. This paper argues that to extract the features used in neural networks to make decisions, it's important to look at the paths between clusters existing in the hidden spaces of neural networks. These features are of particular interest because they reflect the true decision making process of the neural network. This analysis is then furthered to present an ensemble algorithm for arbitrary neural networks which has guarantees for test accuracy. Finally, a discussion detailing the aforementioned guarantees is introduced and the implications to neural networks, including an intuitive explanation for all current regularization methods, are presented. The ensemble algorithm has generated state-of-the-art results for Wide-ResNets on CIFAR-10 (top 5 for all models) and has improved test accuracy for all models it has been applied to.
Predicting Progression of Age-related Macular Degeneration from Fundus Images using Deep Learning
Authors: Boris Babenko, Siva Balasubramanian, Katy E. Blumer, Greg S. Corrado, Lily Peng, Dale R. Webster, Naama Hammel, Avinash V. Varadarajan
Date: 2019-04-10 00:00:00
Tasks: Sensitivity, Specificity

Background: Patients with neovascular age-related macular degeneration (AMD)
can avoid vision loss via certain therapy. However, methods to predict the
progression to neovascular age-related macular degeneration (nvAMD) are
lacking. Purpose: To develop and validate a deep learning (DL) algorithm to
predict 1-year progression of eyes with no, early, or intermediate AMD to
nvAMD, using color fundus photographs (CFP). Design: Development and validation
of a DL algorithm. Methods: We trained a DL algorithm to predict 1-year
progression to nvAMD, and used 10-fold cross-validation to evaluate this
approach on two groups of eyes in the Age-Related Eye Disease Study (AREDS):
none/early/intermediate AMD, and intermediate AMD (iAMD) only. We compared the
DL algorithm to the manually graded 4-category and 9-step scales in the AREDS
dataset. Main outcome measures: Performance of the DL algorithm was evaluated
using the sensitivity at 80% specificity for progression to nvAMD. Results: The
DL algorithm's sensitivity for predicting progression to nvAMD from
none/early/iAMD (78+/-6%) was higher than manual grades from the 9-step scale
(67+/-8%) or the 4-category scale (48+/-3%). For predicting progression
specifically from iAMD, the DL algorithm's sensitivity (57+/-6%) was also
higher compared to the 9-step grades (36+/-8%) and the 4-category grades
(20+/-0%). Conclusions: Our DL algorithm performed better in predicting
progression to nvAMD than manual grades. Future investigations are required to
test the application of this DL algorithm in a real-world clinical setting.
Learning to Generate Synthetic Data via Compositing
Authors: Shashank Tripathi, Siddhartha Chandra, Amit Agrawal, Ambrish Tyagi, James M. Rehg, Visesh Chari
Date: 2019-04-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Data Augmentation, Human Detection, object-detection, Object Detection, Synthetic Data Generation

We present a task-aware approach to synthetic data generation. Our framework employs a trainable synthesizer network that is optimized to produce meaningful training samples by assessing the strengths and weaknesses of a `target' network. The synthesizer and target networks are trained in an adversarial manner wherein each network is updated with a goal to outdo the other. Additionally, we ensure the synthesizer generates realistic data by pairing it with a discriminator trained on real-world images. Further, to make the target classifier invariant to blending artefacts, we introduce these artefacts to background regions of the training images so the target does not over-fit to them. We demonstrate the efficacy of our approach by applying it to different target networks including a classification network on AffNIST, and two object detection networks (SSD, Faster-RCNN) on different datasets. On the AffNIST benchmark, our approach is able to surpass the baseline results with just half the training examples. On the VOC person detection benchmark, we show improvements of up to 2.7% as a result of our data augmentation. Similarly on the GMU detection benchmark, we report a performance boost of 3.5% in mAP over the baseline method, outperforming the previous state of the art approaches by up to 7.5% on specific categories.
Least Squares Auto-Tuning
Authors: Shane Barratt, Stephen Boyd
Date: 2019-04-10 00:00:00

Least squares is by far the simplest and most commonly applied computational
method in many fields. In almost all applications, the least squares objective
is rarely the true objective. We account for this discrepancy by parametrizing
the least squares problem and automatically adjusting these parameters using an
optimization algorithm. We apply our method, which we call least squares
auto-tuning, to data fitting.
Instance Segmentation based Semantic Matting for Compositing Applications
Authors: Guanqing Hu, James J. Clark
Date: 2019-04-10 00:00:00
Tasks: Image Matting, Instance Segmentation, Semantic Image Matting, Semantic Segmentation

Image compositing is a key step in film making and image editing that aims to
segment a foreground object and combine it with a new background. Automatic
image compositing can be done easily in a studio using chroma-keying when the
background is pure blue or green. However, image compositing in natural scenes
with complex backgrounds remains a tedious task, requiring experienced artists
to hand-segment. In order to achieve automatic compositing in natural scenes,
we propose a fully automated method that integrates instance segmentation and
image matting processes to generate high-quality semantic mattes that can be
used for image editing task. Our approach can be seen both as a refinement of
existing instance segmentation algorithms and as a fully automated semantic
image matting method. It extends automatic image compositing techniques such as
chroma-keying to scenes with complex natural backgrounds without the need for
any kind of user interaction. The output of our approach can be considered as
both refined instance segmentations and alpha mattes with semantic meanings. We
provide experimental results which show improved performance results as
compared to existing approaches.
Energy-Based Continuous Inverse Optimal Control
Authors: Yifei Xu, Jianwen Xie, Tianyang Zhao, Chris Baker, Yibiao Zhao, Ying Nian Wu
Date: 2019-04-10 00:00:00
Tasks: Autonomous Driving, continuous-control, Continuous Control, Trajectory Prediction

The problem of continuous inverse optimal control (over finite time horizon) is to learn the unknown cost function over the sequence of continuous control variables from expert demonstrations. In this article, we study this fundamental problem in the framework of energy-based model, where the observed expert trajectories are assumed to be random samples from a probability density function defined as the exponential of the negative cost function up to a normalizing constant. The parameters of the cost function are learned by maximum likelihood via an "analysis by synthesis" scheme, which iterates (1) synthesis step: sample the synthesized trajectories from the current probability density using the Langevin dynamics via back-propagation through time, and (2) analysis step: update the model parameters based on the statistical difference between the synthesized trajectories and the observed trajectories. Given the fact that an efficient optimization algorithm is usually available for an optimal control problem, we also consider a convenient approximation of the above learning method, where we replace the sampling in the synthesis step by optimization. Moreover, to make the sampling or optimization more efficient, we propose to train the energy-based model simultaneously with a top-down trajectory generator via cooperative learning, where the trajectory generator is used to fast initialize the synthesis step of the energy-based model. We demonstrate the proposed methods on autonomous driving tasks, and show that they can learn suitable cost functions for optimal control.
Analyzing Dynamical Brain Functional Connectivity As Trajectories on Space of Covariance Matrices
Authors: Mengyu Dai, Zhengwu Zhang, Anuj Srivastava
Date: 2019-04-10 00:00:00
Tasks: Clustering, Dimensionality Reduction, Functional Connectivity, General Classification, Time Series, Time Series Analysis

Human brain functional connectivity (FC) is often measured as the similarity of functional MRI responses across brain regions when a brain is either resting or performing a task. This paper aims to statistically analyze the dynamic nature of FC by representing the collective time-series data, over a set of brain regions, as a trajectory on the space of covariance matrices, or symmetric-positive definite matrices (SPDMs). We use a recently developed metric on the space of SPDMs for quantifying differences across FC observations, and for clustering and classification of FC trajectories. To facilitate large scale and high-dimensional data analysis, we propose a novel, metric-based dimensionality reduction technique to reduce data from large SPDMs to small SPDMs. We illustrate this comprehensive framework using data from the Human Connectome Project (HCP) database for multiple subjects and tasks, with task classification rates that match or outperform state-of-the-art techniques.
Predicting Future Pedestrian Motion in Video Sequences using Crowd Simulation
Authors: Cliceres dal Bianco, Soraia Raupp Musse
Date: 2019-04-10 00:00:00
Tasks: Motion Estimation

While human and group analysis have become an important area in last decades,
some current and relevant applications involve to estimate future motion of
pedestrians in real video sequences. This paper presents a method to provide
motion estimation of real pedestrians in next seconds, using crowd simulation.
Our method is based on Physics and heuristics and use BioCrowds as crowd
simulation methodology to estimate future positions of people in video
sequences. Results show that our method for estimation works well even for
complex videos where events can happen. The maximum achieved average error is
$2.72$cm when estimating the future motion of 32 pedestrians with more than 2
seconds in advance. This paper discusses this and other results.
BAOD: Budget-Aware Object Detection
Authors: Alejandro Pardo, Mengmeng Xu, Ali Thabet, Pablo Arbelaez, Bernard Ghanem
Date: 2019-04-10 00:00:00
Tasks: Active Learning, Object, object-detection, Object Detection

We study the problem of object detection from a novel perspective in which annotation budget constraints are taken into consideration, appropriately coined Budget Aware Object Detection (BAOD). When provided with a fixed budget, we propose a strategy for building a diverse and informative dataset that can be used to optimally train a robust detector. We investigate both optimization and learning-based methods to sample which images to annotate and what type of annotation (strongly or weakly supervised) to annotate them with. We adopt a hybrid supervised learning framework to train the object detector from both these types of annotation. We conduct a comprehensive empirical study showing that a handcrafted optimization method outperforms other selection techniques including random sampling, uncertainty sampling and active learning. By combining an optimal image/annotation selection scheme with hybrid supervised learning to solve the BAOD problem, we show that one can achieve the performance of a strongly supervised detector on PASCAL-VOC 2007 while saving 12.8% of its original annotation budget. Furthermore, when $100\%$ of the budget is used, it surpasses this performance by 2.0 mAP percentage points.
Generating Animations from Screenplays
Authors: Yeyao Zhang, Eleftheria Tsipidi, Sasha Schriber, Mubbasir Kapadia, Markus Gross, Ashutosh Modi
Date: 2019-04-10 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Sentence, Text Simplification

Automatically generating animation from natural language text finds
application in a number of areas e.g. movie script writing, instructional
videos, and public safety. However, translating natural language text into
animation is a challenging task. Existing text-to-animation systems can handle
only very simple sentences, which limits their applications. In this paper, we
develop a text-to-animation system which is capable of handling complex
sentences. We achieve this by introducing a text simplification step into the
process. Building on an existing animation generation system for screenwriting,
we create a robust NLP pipeline to extract information from screenplays and map
them to the system's knowledge base. We develop a set of linguistic
transformation rules that simplify complex sentences. Information extracted
from the simplified sentences is used to generate a rough storyboard and video
depicting the text. Our sentence simplification module outperforms existing
systems in terms of BLEU and SARI metrics.We further evaluated our system via a
user study: 68 % participants believe that our system generates reasonable
animation from input screenplays.