pwc-archive/papers-with-abstracts huggingface dataset:
PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation
Authors: Danfei Xu, Dragomir Anguelov, Ashesh Jain
Date: 2017-11-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Object Detection, 6D Pose Estimation using RGB, object-detection, Object Detection, Sensor Fusion

We present PointFusion, a generic 3D object detection method that leverages
both image and 3D point cloud information. Unlike existing methods that either
use multi-stage pipelines or hold sensor and dataset-specific assumptions,
PointFusion is conceptually simple and application-agnostic. The image data and
the raw point cloud data are independently processed by a CNN and a PointNet
architecture, respectively. The resulting outputs are then combined by a novel
fusion network, which predicts multiple 3D box hypotheses and their
confidences, using the input 3D points as spatial anchors. We evaluate
PointFusion on two distinctive datasets: the KITTI dataset that features
driving scenes captured with a lidar-camera setup, and the SUN-RGBD dataset
that captures indoor environments with RGB-D cameras. Our model is the first
one that is able to perform better or on-par with the state-of-the-art on these
diverse datasets without any dataset-specific model tuning.
Sparse Photometric 3D Face Reconstruction Guided by Morphable Models
Authors: Xuan Cao, Zhang Chen, Anpei Chen, Xin Chen, Cen Wang, Jingyi Yu
Date: 2017-11-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Face Reconstruction, Face Reconstruction, Position, Semantic Segmentation

We present a novel 3D face reconstruction technique that leverages sparse
photometric stereo (PS) and latest advances on face registration/modeling from
a single image. We observe that 3D morphable faces approach provides a
reasonable geometry proxy for light position calibration. Specifically, we
develop a robust optimization technique that can calibrate per-pixel lighting
direction and illumination at a very high precision without assuming uniform
surface albedos. Next, we apply semantic segmentation on input images and the
geometry proxy to refine hairy vs. bare skin regions using tailored filters.
Experiments on synthetic and real data show that by using a very small set of
images, our technique is able to reconstruct fine geometric details such as
wrinkles, eyebrows, whelks, pores, etc, comparable to and sometimes surpassing
movie quality productions.
Defining the Pose of any 3D Rigid Object and an Associated Distance
Authors: Romain Brégier, Frédéric Devernay, Laetitia Leyrit, James Crowley
Date: 2016-12-14 00:00:00
Tasks: Object, Pose Estimation, valid

The pose of a rigid object is usually regarded as a rigid transformation,
described by a translation and a rotation. However, equating the pose space
with the space of rigid transformations is in general abusive, as it does not
account for objects with proper symmetries -- which are common among man-made
objects.In this article, we define pose as a distinguishable static state of an
object, and equate a pose with a set of rigid transformations. Based solely on
geometric considerations, we propose a frame-invariant metric on the space of
possible poses, valid for any physical rigid object, and requiring no arbitrary
tuning. This distance can be evaluated efficiently using a representation of
poses within an Euclidean space of at most 12 dimensions depending on the
object's symmetries. This makes it possible to efficiently perform neighborhood
queries such as radius searches or k-nearest neighbor searches within a large
set of poses using off-the-shelf methods. Pose averaging considering this
metric can similarly be performed easily, using a projection function from the
Euclidean space onto the pose space. The practical value of those theoretical
developments is illustrated with an application of pose estimation of instances
of a 3D rigid object given an input depth map, via a Mean Shift procedure.
Single Image Super-Resolution based on Wiener Filter in Similarity Domain
Authors: Cristóvão Cruz, Rakesh Mehta, Vladimir Katkovnik, Karen Egiazarian
Date: 2017-04-13 00:00:00
Tasks: Collaborative Filtering, Image Super-Resolution, Super-Resolution

Single image super resolution (SISR) is an ill-posed problem aiming at
estimating a plausible high resolution (HR) image from a single low resolution
(LR) image. Current state-of-the-art SISR methods are patch-based. They use
either external data or internal self-similarity to learn a prior for a HR
image. External data based methods utilize large number of patches from the
training data, while self-similarity based approaches leverage one or more
similar patches from the input image. In this paper we propose a
self-similarity based approach that is able to use large groups of similar
patches extracted from the input image to solve the SISR problem. We introduce
a novel prior leading to collaborative filtering of patch groups in 1D
similarity domain and couple it with an iterative back-projection framework.
The performance of the proposed algorithm is evaluated on a number of SISR
benchmark datasets. Without using any external data, the proposed approach
outperforms the current non-CNN based methods on the tested datasets for
various scaling factors. On certain datasets, the gain is over 1 dB, when
compared to the recent method A+. For high sampling rate (x4) the proposed
method performs similarly to very recent state-of-the-art deep convolutional
network based approaches.
Semantic Technology-Assisted Review (STAR) Document analysis and monitoring using random vectors
Authors: Jean-François Delpech
Date: 2017-11-28 00:00:00

The review and analysis of large collections of documents and the periodic
monitoring of new additions thereto has greatly benefited from new developments
in computer software. This paper demonstrates how using random vectors to
construct a low-dimensional Euclidean space embedding words and documents
enables fast and accurate computation of semantic similarities between them.
With this technique of Semantic Technology-Assisted Review (STAR), documents
can be selected, compared, classified, summarized and evaluated very quickly
with minimal expert involvement and high-quality results.
Curriculum Q-Learning for Visual Vocabulary Acquisition
Authors: Ahmed H. Zaidi, Russell Moore, Ted Briscoe
Date: 2017-11-29 00:00:00
Tasks: Q-Learning, Reinforcement Learning

The structure of curriculum plays a vital role in our learning process, both
as children and adults. Presenting material in ascending order of difficulty
that also exploits prior knowledge can have a significant impact on the rate of
learning. However, the notion of difficulty and prior knowledge differs from
person to person. Motivated by the need for a personalised curriculum, we
present a novel method of curriculum learning for vocabulary words in the form
of visual prompts. We employ a reinforcement learning model grounded in
pedagogical theories that emulates the actions of a tutor. We simulate three
students with different levels of vocabulary knowledge in order to evaluate the
how well our model adapts to the environment. The results of the simulation
reveal that through interaction, the model is able to identify the areas of
weakness, as well as push students to the edge of their ZPD. We hypothesise
that these methods can also be effective in training agents to learn language
representations in a simulated environment where it has previously been shown
that order of words and prior knowledge play an important role in the efficacy
of language learning.
Clickstream analysis for crowd-based object segmentation with confidence
Authors: Eric Heim, Alexander Seitel, Jonas Andrulis, Fabian Isensee, Christian Stock, Tobias Ross, Lena Maier-Hein
Date: 2016-11-25 00:00:00
Tasks: Image Segmentation, Object, Segmentation, Semantic Segmentation

With the rapidly increasing interest in machine learning based solutions for
automatic image annotation, the availability of reference annotations for
algorithm training is one of the major bottlenecks in the field. Crowdsourcing
has evolved as a valuable option for low-cost and large-scale data annotation;
however, quality control remains a major issue which needs to be addressed. To
our knowledge, we are the first to analyze the annotation process to improve
crowd-sourced image segmentation. Our method involves training a regressor to
estimate the quality of a segmentation from the annotator's clickstream data.
The quality estimation can be used to identify spam and weight individual
annotations by their (estimated) quality when merging multiple segmentations of
one image. Using a total of 29,000 crowd annotations performed on publicly
available data of different object classes, we show that (1) our method is
highly accurate in estimating the segmentation quality based on clickstream
data, (2) outperforms state-of-the-art methods for merging multiple
annotations. As the regressor does not need to be trained on the object class
that it is applied to it can be regarded as a low-cost option for quality
control and confidence analysis in the context of crowd-based image annotation.
Compression for Smooth Shape Analysis
Authors: V. Estellers, F. R. Schmidt, D. Cremers
Date: 2017-11-29 00:00:00

Most 3D shape analysis methods use triangular meshes to discretize both the
shape and functions on it as piecewise linear functions. With this
representation, shape analysis requires fine meshes to represent smooth shapes
and geometric operators like normals, curvatures, or Laplace-Beltrami
eigenfunctions at large computational and memory costs.
  We avoid this bottleneck with a compression technique that represents a
smooth shape as subdivision surfaces and exploits the subdivision scheme to
parametrize smooth functions on that shape with a few control parameters. This
compression does not affect the accuracy of the Laplace-Beltrami operator and
its eigenfunctions and allow us to compute shape descriptors and shape
matchings at an accuracy comparable to triangular meshes but a fraction of the
computational cost.
  Our framework can also compress surfaces represented by point clouds to do
shape analysis of 3D scanning data.
RAIL: Risk-Averse Imitation Learning
Authors: Anirban Santara, Abhishek Naik, Balaraman Ravindran, Dipankar Das, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
Date: 2017-07-20 00:00:00
Tasks: Autonomous Driving, continuous-control, Continuous Control, Imitation Learning

Imitation learning algorithms learn viable policies by imitating an expert's
behavior when reward signals are not available. Generative Adversarial
Imitation Learning (GAIL) is a state-of-the-art algorithm for learning policies
when the expert's behavior is available as a fixed set of trajectories. We
evaluate in terms of the expert's cost function and observe that the
distribution of trajectory-costs is often more heavy-tailed for GAIL-agents
than the expert at a number of benchmark continuous-control tasks. Thus,
high-cost trajectories, corresponding to tail-end events of catastrophic
failure, are more likely to be encountered by the GAIL-agents than the expert.
This makes the reliability of GAIL-agents questionable when it comes to
deployment in risk-sensitive applications like robotic surgery and autonomous
driving. In this work, we aim to minimize the occurrence of tail-end events by
minimizing tail risk within the GAIL framework. We quantify tail risk by the
Conditional-Value-at-Risk (CVaR) of trajectories and develop the Risk-Averse
Imitation Learning (RAIL) algorithm. We observe that the policies learned with
RAIL show lower tail-end risk than those of vanilla GAIL. Thus the proposed
RAIL algorithm appears as a potent alternative to GAIL for improved reliability
in risk-sensitive applications.
Structured Variational Inference for Coupled Gaussian Processes
Authors: Vincent Adam
Date: 2017-11-03 00:00:00
Tasks: Gaussian Processes, Variational Inference

Sparse variational approximations allow for principled and scalable inference
in Gaussian Process (GP) models. In settings where several GPs are part of the
generative model, theses GPs are a posteriori coupled. For many applications
such as regression where predictive accuracy is the quantity of interest, this
coupling is not crucial. Howewer if one is interested in posterior uncertainty,
it cannot be ignored. A key element of variational inference schemes is the
choice of the approximate posterior parameterization. When the number of latent
variables is large, mean field (MF) methods provide fast and accurate posterior
means while more structured posterior lead to inference algorithm of greater
computational complexity. Here, we extend previous sparse GP approximations and
propose a novel parameterization of variational posteriors in the multi-GP
setting allowing for fast and scalable inference capturing posterior
dependencies.
DeepSkeleton: Skeleton Map for 3D Human Pose Regression
Authors: Qingfu Wan, Wei zhang, xiangyang xue
Date: 2017-11-29 00:00:00
Tasks: 2D Human Pose Estimation, 3D Human Pose Estimation, Pose Estimation, regression

Despite recent success on 2D human pose estimation, 3D human pose estimation
still remains an open problem. A key challenge is the ill-posed depth ambiguity
nature. This paper presents a novel intermediate feature representation named
skeleton map for regression. It distills structural context from irrelavant
properties of RGB image e.g. illumination and texture. It is simple, clean and
can be easily generated via deconvolution network. For the first time, we show
that training regression network from skeleton map alone is capable of meeting
the performance of state-of-theart 3D human pose estimation works. We further
exploit the power of multiple 3D hypothesis generation to obtain reasonbale 3D
pose in consistent with 2D pose detection. The effectiveness of our approach is
validated on challenging in-the-wild dataset MPII and indoor dataset Human3.6M.
Saliency Weighted Convolutional Features for Instance Search
Authors: Eva Mohedano, Kevin McGuinness, Xavier Giro-i-Nieto, Noel E. O'Connor
Date: 2017-11-29 00:00:00
Tasks: Instance Search, Retrieval

This work explores attention models to weight the contribution of local
convolutional representations for the instance search task. We present a
retrieval framework based on bags of local convolutional features (BLCF) that
benefits from saliency weighting to build an efficient image representation.
The use of human visual attention models (saliency) allows significant
improvements in retrieval performance without the need to conduct region
analysis or spatial verification, and without requiring any feature fine
tuning. We investigate the impact of different saliency models, finding that
higher performance on saliency benchmarks does not necessarily equate to
improved performance when used in instance search tasks. The proposed approach
outperforms the state-of-the-art on the challenging INSTRE benchmark by a large
margin, and provides similar performance on the Oxford and Paris benchmarks
compared to more complex methods that use off-the-shelf representations. The
source code used in this project is available at
https://imatge-upc.github.io/salbow/
Blind estimation of white Gaussian noise variance in highly textured images
Authors: Mykola Ponomarenko, Nikolay Gapon, Viacheslav Voronin, Karen Egiazarian
Date: 2017-11-29 00:00:00

In the paper, a new method of blind estimation of noise variance in a single
highly textured image is proposed. An input image is divided into 8x8 blocks
and discrete cosine transform (DCT) is performed for each block. A part of 64
DCT coefficients with lowest energy calculated through all blocks is selected
for further analysis. For the DCT coefficients, a robust estimate of noise
variance is calculated. Corresponding to the obtained estimate, a part of
blocks having very large values of local variance calculated only for the
selected DCT coefficients are excluded from the further analysis. These two
steps (estimation of noise variance and exclusion of blocks) are iteratively
repeated three times. For the verification of the proposed method, a new
noise-free test image database TAMPERE17 consisting of many highly textured
images is designed. It is shown for this database and different values of noise
variance from the set {25, 49, 100, 225}, that the proposed method provides
approximately two times lower estimation root mean square error than other
methods.
Reinforcement Learning To Adapt Speech Enhancement to Instantaneous Input Signal Quality
Authors: Rasool Fakoor, Xiaodong He, Ivan Tashev, Shuayb Zarar
Date: 2017-11-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Speech Enhancement

Today, the optimal performance of existing noise-suppression algorithms, both
data-driven and those based on classic statistical methods, is range bound to
specific levels of instantaneous input signal-to-noise ratios. In this paper,
we present a new approach to improve the adaptivity of such algorithms enabling
them to perform robustly across a wide range of input signal and noise types.
Our methodology is based on the dynamic control of algorithmic parameters via
reinforcement learning. Specifically, we model the noise-suppression module as
a black box, requiring no knowledge of the algorithmic mechanics except a
simple feedback from the output. We utilize this feedback as the reward signal
for a reinforcement-learning agent that learns a policy to adapt the
algorithmic parameters for every incoming audio frame (16 ms of data). Our
preliminary results show that such a control mechanism can substantially
increase the overall performance of the underlying noise-suppression algorithm;
42% and 16% improvements in output SNR and MSE, respectively, when compared to
no adaptivity.
Efficient exploration with Double Uncertain Value Networks
Authors: Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker
Date: 2017-11-29 00:00:00
Tasks: Efficient Exploration, Reinforcement Learning, Thompson Sampling

This paper studies directed exploration for reinforcement learning agents by
tracking uncertainty about the value of each available action. We identify two
sources of uncertainty that are relevant for exploration. The first originates
from limited data (parametric uncertainty), while the second originates from
the distribution of the returns (return uncertainty). We identify methods to
learn these distributions with deep neural networks, where we estimate
parametric uncertainty with Bayesian drop-out, while return uncertainty is
propagated through the Bellman equation as a Gaussian distribution. Then, we
identify that both can be jointly estimated in one network, which we call the
Double Uncertain Value Network. The policy is directly derived from the learned
distributions based on Thompson sampling. Experimental results show that both
types of uncertainty may vastly improve learning in domains with a strong
exploration challenge.
Neural SLAM: Learning to Explore with External Memory
Authors: Jingwei Zhang, Lei Tai, Ming Liu, Joschka Boedecker, Wolfram Burgard
Date: 2017-06-29 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Simultaneous Localization and Mapping

We present an approach for agents to learn representations of a global map from sensor data, to aid their exploration in new environments. To achieve this, we embed procedures mimicking that of traditional Simultaneous Localization and Mapping (SLAM) into the soft attention based addressing of external memory architectures, in which the external memory acts as an internal representation of the environment. This structure encourages the evolution of SLAM-like behaviors inside a completely differentiable deep neural network. We show that this approach can help reinforcement learning agents to successfully explore new environments where long-term memory is essential. We validate our approach in both challenging grid-world environments and preliminary Gazebo experiments. A video of our experiments can be found at: https://goo.gl/G2Vu5y.
Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing
Authors: Mogens Graf Plessen
Date: 2017-11-29 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, Model-based Reinforcement Learning, Model Predictive Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Within the context of autonomous driving a model-based reinforcement learning
algorithm is proposed for the design of neural network-parameterized
controllers. Classical model-based control methods, which include sampling- and
lattice-based algorithms and model predictive control, suffer from the
trade-off between model complexity and computational burden required for the
online solution of expensive optimization or search problems at every short
sampling time. To circumvent this trade-off, a 2-step procedure is motivated:
first learning of a controller during offline training based on an arbitrarily
complicated mathematical system model, before online fast feedforward
evaluation of the trained controller. The contribution of this paper is the
proposition of a simple gradient-free and model-based algorithm for deep
reinforcement learning using task separation with hill climbing (TSHC). In
particular, (i) simultaneous training on separate deterministic tasks with the
purpose of encoding many motion primitives in a neural network, and (ii) the
employment of maximally sparse rewards in combination with virtual velocity
constraints (VVCs) in setpoint proximity are advocated.
Introduction to Tensor Decompositions and their Applications in Machine Learning
Authors: Stephan Rabanser, Oleksandr Shchur, Stephan Günnemann
Date: 2017-11-29 00:00:00
Tasks: BIG-bench Machine Learning, Tensor Decomposition

Tensors are multidimensional arrays of numerical values and therefore
generalize matrices to multiple dimensions. While tensors first emerged in the
psychometrics community in the $20^{\text{th}}$ century, they have since then
spread to numerous other disciplines, including machine learning. Tensors and
their decompositions are especially beneficial in unsupervised learning
settings, but are gaining popularity in other sub-disciplines like temporal and
multi-relational data analysis, too.
  The scope of this paper is to give a broad overview of tensors, their
decompositions, and how they are used in machine learning. As part of this, we
are going to introduce basic tensor concepts, discuss why tensors can be
considered more rigid than matrices with respect to the uniqueness of their
decomposition, explain the most important factorization algorithms and their
properties, provide concrete examples of tensor decomposition applications in
machine learning, conduct a case study on tensor-based estimation of mixture
models, talk about the current state of research, and provide references to
available software libraries.
Leveraging Conversation Structure on Social Media to Identify Potentially Influential Users
Authors: Dario De Nart, Dante Degl'Innocenti, Marco Pavan
Date: 2017-11-29 00:00:00
Tasks: Abstract Argumentation, BIG-bench Machine Learning

Social networks have a community providing feedback on comments that allows
to identify opinion leaders and users whose positions are unwelcome. Other
platforms are not backed by such tools. Having a picture of the community's
reactions to a published content is a non trivial problem. In this work we
propose a novel approach using Abstract Argumentation Frameworks and machine
learning to describe interactions between users. Our experiments provide
evidence that modelling the flow of a conversation with the primitives of AAF
can support the identification of users who produce consistently appreciated
content without modelling such content.