pwc-archive/papers-with-abstracts huggingface dataset:
A Multi-Objective Learning to re-Rank Approach to Optimize Online Marketplaces for Multiple Stakeholders
Authors: Phong Nguyen, John Dines, Jan Krasnodebski
Date: 2017-08-02 00:00:00
Tasks: Recommendation Systems

Multi-objective recommender systems address the difficult task of
recommending items that are relevant to multiple, possibly conflicting,
criteria. However these systems are most often designed to address the
objective of one single stakeholder, typically, in online commerce, the
consumers whose input and purchasing decisions ultimately determine the success
of the recommendation systems. In this work, we address the multi-objective,
multi-stakeholder, recommendation problem involving one or more objective(s)
per stakeholder. In addition to the consumer stakeholder, we also consider two
other stakeholders; the suppliers who provide the goods and services for sale
and the intermediary who is responsible for helping connect consumers to
suppliers via its recommendation algorithms. We analyze the multi-objective,
multi-stakeholder, problem from the point of view of the online marketplace
intermediary whose objective is to maximize its commission through its
recommender system. We define a multi-objective problem relating all our three
stakeholders which we solve with a novel learning-to-re-rank approach that
makes use of a novel regularization function based on the Kendall tau
correlation metric and its kernel version; given an initial ranking of item
recommendations built for the consumer, we aim to re-rank it such that the new
ranking is also optimized for the secondary objectives while staying close to
the initial ranking. We evaluate our approach on a real-world dataset of hotel
recommendations provided by Expedia where we show the effectiveness of our
approach against a business-rules oriented baseline model.
Asymptotic Optimality in Stochastic Optimization
Authors: John Duchi, Feng Ruan
Date: 2016-12-16 00:00:00
Tasks: Stochastic Optimization

We study local complexity measures for stochastic convex optimization
problems, providing a local minimax theory analogous to that of H\'{a}jek and
Le Cam for classical statistical problems. We give complementary optimality
results, developing fully online methods that adaptively achieve optimal
convergence guarantees. Our results provide function-specific lower bounds and
convergence results that make precise a correspondence between statistical
difficulty and the geometric notion of tilt-stability from optimization. As
part of this development, we show how variants of Nesterov's dual averaging---a
stochastic gradient-based procedure---guarantee finite time identification of
constraints in optimization problems, while stochastic gradient procedures
fail. Additionally, we highlight a gap between problems with linear and
nonlinear constraints: standard stochastic-gradient-based procedures are
suboptimal even for the simplest nonlinear constraints, necessitating the
development of asymptotically optimal Riemannian stochastic gradient methods.
Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds
Authors: Hamid Hamraz, Marco A. Contreras, Jun Zhang
Date: 2017-02-17 00:00:00

Airborne laser scanning (LiDAR) point clouds over large forested areas can be
processed to segment individual trees and subsequently extract tree-level
information. Existing segmentation procedures typically detect more than 90% of
overstory trees, yet they barely detect 60% of understory trees because of the
occlusion effect of higher canopy layers. Although understory trees provide
limited financial value, they are an essential component of ecosystem
functioning by offering habitat for numerous wildlife species and influencing
stand development. Here we model the occlusion effect in terms of point
density. We estimate the fractions of points representing different canopy
layers (one overstory and multiple understory) and also pinpoint the required
density for reasonable tree segmentation (where accuracy plateaus). We show
that at a density of ~170 pt/m-sqr understory trees can likely be segmented as
accurately as overstory trees. Given the advancements of LiDAR sensor
technology, point clouds will affordably reach this required density. Using
modern computational approaches for big data, the denser point clouds can
efficiently be processed to ultimately allow accurate remote quantification of
forest resources. The methodology can also be adopted for other similar remote
sensing or advanced imaging applications such as geological subsurface
modelling or biomedical tissue analysis.
Latent common manifold learning with alternating diffusion: analysis and applications
Authors: Ronen Talmon, Hau-Tieng Wu
Date: 2016-01-30 00:00:00

The analysis of data sets arising from multiple sensors has drawn significant
research attention over the years. Traditional methods, including kernel-based
methods, are typically incapable of capturing nonlinear geometric structures.
We introduce a latent common manifold model underlying multiple sensor
observations for the purpose of multimodal data fusion. A method based on
alternating diffusion is presented and analyzed; we provide theoretical
analysis of the method under the latent common manifold model. To exemplify the
power of the proposed framework, experimental results in several applications
are reported.
Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation
Authors: Ruichi Yu, Ang Li, Vlad I. Morariu, Larry S. Davis
Date: 2017-07-28 00:00:00
Proceeding: ICCV 2017 10
Tasks: Knowledge Distillation, Relationship Detection, Visual Relationship Detection

Understanding visual relationships involves identifying the subject, the
object, and a predicate relating them. We leverage the strong correlations
between the predicate and the (subj,obj) pair (both semantically and spatially)
to predict the predicates conditioned on the subjects and the objects. Modeling
the three entities jointly more accurately reflects their relationships, but
complicates learning since the semantic space of visual relationships is huge
and the training data is limited, especially for the long-tail relationships
that have few instances. To overcome this, we use knowledge of linguistic
statistics to regularize visual model learning. We obtain linguistic knowledge
by mining from both training annotations (internal knowledge) and publicly
available text, e.g., Wikipedia (external knowledge), computing the conditional
probability distribution of a predicate given a (subj,obj) pair. Then, we
distill the knowledge into a deep model to achieve better generalization. Our
experimental results on the Visual Relationship Detection (VRD) and Visual
Genome datasets suggest that with this linguistic knowledge distillation, our
model outperforms the state-of-the-art methods significantly, especially when
predicting unseen relationships (e.g., recall improved from 8.45% to 19.17% on
VRD zero-shot testing set).
Drone-based Object Counting by Spatially Regularized Regional Proposal Network
Authors: Meng-Ru Hsieh, Yen-Liang Lin, Winston H. Hsu
Date: 2017-07-19 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object Counting, Region Proposal

Existing counting methods often adopt regression-based approaches and cannot
precisely localize the target objects, which hinders the further analysis
(e.g., high-level understanding and fine-grained classification). In addition,
most of prior work mainly focus on counting objects in static environments with
fixed cameras. Motivated by the advent of unmanned flying vehicles (i.e.,
drones), we are interested in detecting and counting objects in such dynamic
environments. We propose Layout Proposal Networks (LPNs) and spatial kernels to
simultaneously count and localize target objects (e.g., cars) in videos
recorded by the drone. Different from the conventional region proposal methods,
we leverage the spatial layout information (e.g., cars often park regularly)
and introduce these spatially regularized constraints into our network to
improve the localization accuracy. To evaluate our counting method, we present
a new large-scale car parking lot dataset (CARPK) that contains nearly 90,000
cars captured from different parking lots. To the best of our knowledge, it is
the first and the largest drone view dataset that supports object counting, and
provides the bounding box annotations.
AMAT: Medial Axis Transform for Natural Images
Authors: Stavros Tsogkas, Sven Dickinson
Date: 2017-03-24 00:00:00
Proceeding: ICCV 2017 10
Tasks: Clustering

We introduce Appearance-MAT (AMAT), a generalization of the medial axis
transform for natural images, that is framed as a weighted geometric set cover
problem. We make the following contributions: i) we extend previous medial
point detection methods for color images, by associating each medial point with
a local scale; ii) inspired by the invertibility property of the binary MAT, we
also associate each medial point with a local encoding that allows us to invert
the AMAT, reconstructing the input image; iii) we describe a clustering scheme
that takes advantage of the additional scale and appearance information to
group individual points into medial branches, providing a shape decomposition
of the underlying image regions. In our experiments, we show state-of-the-art
performance in medial point detection on Berkeley Medial AXes (BMAX500), a new
dataset of medial axes based on the BSDS500 database, and good generalization
on the SK506 and WH-SYMMAX datasets. We also measure the quality of
reconstructed images from BMAX500, obtained by inverting their computed AMAT.
Our approach delivers significantly better reconstruction quality with respect
to three baselines, using just 10% of the image pixels. Our code and
annotations are available at https://github.com/tsogkas/amat .
Hamiltonian Monte Carlo with Energy Conserving Subsampling
Authors: Khue-Dung Dang, Matias Quiroz, Robert Kohn, Minh-Ngoc Tran, Mattias Villani
Date: 2017-08-02 00:00:00

Hamiltonian Monte Carlo (HMC) samples efficiently from high-dimensional
posterior distributions with proposed parameter draws obtained by iterating on
a discretized version of the Hamiltonian dynamics. The iterations make HMC
computationally costly, especially in problems with large datasets, since it is
necessary to compute posterior densities and their derivatives with respect to
the parameters. Naively computing the Hamiltonian dynamics on a subset of the
data causes HMC to lose its key ability to generate distant parameter proposals
with high acceptance probability. The key insight in our article is that
efficient subsampling HMC for the parameters is possible if both the dynamics
and the acceptance probability are computed from the same data subsample in
each complete HMC iteration. We show that this is possible to do in a
principled way in a HMC-within-Gibbs framework where the subsample is updated
using a pseudo marginal MH step and the parameters are then updated using an
HMC step, based on the current subsample. We show that our subsampling methods
are fast and compare favorably to two popular sampling algorithms that utilize
gradient estimates from data subsampling. We also explore the current
limitations of subsampling HMC algorithms by varying the quality of the
variance reducing control variates used in the estimators of the posterior
density and its gradients.
Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs
Authors: Vishwanath A. Sindagi, Vishal M. Patel
Date: 2017-08-02 00:00:00
Proceeding: ICCV 2017 10
Tasks: Crowd Counting, Vocal Bursts Intensity Prediction

We present a novel method called Contextual Pyramid CNN (CP-CNN) for
generating high-quality crowd density and count estimation by explicitly
incorporating global and local contextual information of crowd images. The
proposed CP-CNN consists of four modules: Global Context Estimator (GCE), Local
Context Estimator (LCE), Density Map Estimator (DME) and a Fusion-CNN (F-CNN).
GCE is a VGG-16 based CNN that encodes global context and it is trained to
classify input images into different density classes, whereas LCE is another
CNN that encodes local context information and it is trained to perform
patch-wise classification of input images into different density classes. DME
is a multi-column architecture-based CNN that aims to generate high-dimensional
feature maps from the input image which are fused with the contextual
information estimated by GCE and LCE using F-CNN. To generate high resolution
and high-quality density maps, F-CNN uses a set of convolutional and
fractionally-strided convolutional layers and it is trained along with the DME
in an end-to-end fashion using a combination of adversarial loss and
pixel-level Euclidean loss. Extensive experiments on highly challenging
datasets show that the proposed method achieves significant improvements over
the state-of-the-art methods.
Semantic Instance Labeling Leveraging Hierarchical Segmentation
Authors: Steven Hickson, Irfan Essa, Henrik Christensen
Date: 2017-08-02 00:00:00
Tasks: Segmentation, Superpixels

Most of the approaches for indoor RGBD semantic la- beling focus on using
pixels or superpixels to train a classi- fier. In this paper, we implement a
higher level segmentation using a hierarchy of superpixels to obtain a better
segmen- tation for training our classifier. By focusing on meaningful segments
that conform more directly to objects, regardless of size, we train a random
forest of decision trees as a clas- sifier using simple features such as the 3D
size, LAB color histogram, width, height, and shape as specified by a his-
togram of surface normals. We test our method on the NYU V2 depth dataset, a
challenging dataset of cluttered indoor environments. Our experiments using the
NYU V2 depth dataset show that our method achieves state of the art re- sults
on both a general semantic labeling introduced by the dataset (floor,
structure, furniture, and objects) and a more object specific semantic
labeling. We show that training a classifier on a segmentation from a hierarchy
of super pixels yields better results than training directly on super pixels,
patches, or pixels as in previous work.
Predicting Human Activities Using Stochastic Grammar
Authors: Siyuan Qi, Siyuan Huang, Ping Wei, Song-Chun Zhu
Date: 2017-08-02 00:00:00
Proceeding: ICCV 2017 10
Tasks: Activity Prediction

This paper presents a novel method to predict future human activities from
partially observed RGB-D videos. Human activity prediction is generally
difficult due to its non-Markovian property and the rich context between human
and environments.
  We use a stochastic grammar model to capture the compositional structure of
events, integrating human actions, objects, and their affordances. We represent
the event by a spatial-temporal And-Or graph (ST-AOG). The ST-AOG is composed
of a temporal stochastic grammar defined on sub-activities, and spatial graphs
representing sub-activities that consist of human actions, objects, and their
affordances. Future sub-activities are predicted using the temporal grammar and
Earley parsing algorithm. The corresponding action, object, and affordance
labels are then inferred accordingly. Extensive experiments are conducted to
show the effectiveness of our model on both semantic event parsing and future
activity prediction.
An Energy Minimization Approach to 3D Non-Rigid Deformable Surface Estimation Using RGBD Data
Authors: Bryan Willimon, Steven Hickson, Ian Walker, Stan Birchfield
Date: 2017-08-02 00:00:00
Tasks: Object

We propose an algorithm that uses energy mini- mization to estimate the
current configuration of a non-rigid object. Our approach utilizes an RGBD
image to calculate corresponding SURF features, depth, and boundary informa-
tion. We do not use predetermined features, thus enabling our system to operate
on unmodified objects. Our approach relies on a 3D nonlinear energy
minimization framework to solve for the configuration using a semi-implicit
scheme. Results show various scenarios of dynamic posters and shirts in
different configurations to illustrate the performance of the method. In
particular, we show that our method is able to estimate the configuration of a
textureless nonrigid object with no correspondences available.
PROBE-GK: Predictive Robust Estimation using Generalized Kernels
Authors: Valentin Peretroukhin, William Vega-Brown, Nicholas Roy, Jonathan Kelly
Date: 2017-08-01 00:00:00
Tasks: Bayesian Inference

Many algorithms in computer vision and robotics make strong assumptions about
uncertainty, and rely on the validity of these assumptions to produce accurate
and consistent state estimates. In practice, dynamic environments may degrade
sensor performance in predictable ways that cannot be captured with static
uncertainty parameters. In this paper, we employ fast nonparametric Bayesian
inference techniques to more accurately model sensor uncertainty. By setting a
prior on observation uncertainty, we derive a predictive robust estimator, and
show how our model can be learned from sample images, both with and without
knowledge of the motion used to generate the data. We validate our approach
through Monte Carlo simulations, and report significant improvements in
localization accuracy relative to a fixed noise model in several settings,
including on synthetic data, the KITTI dataset, and our own experimental
platform.
Associative Domain Adaptation
Authors: Philip Haeusser, Thomas Frerix, Alexander Mordvintsev, Daniel Cremers
Date: 2017-08-02 00:00:00
Proceeding: ICCV 2017 10
Tasks: Domain Adaptation, General Classification

We propose associative domain adaptation, a novel technique for end-to-end
domain adaptation with neural networks, the task of inferring class labels for
an unlabeled target domain based on the statistical properties of a labeled
source domain. Our training scheme follows the paradigm that in order to
effectively derive class labels for the target domain, a network should produce
statistically domain invariant embeddings, while minimizing the classification
error on the labeled source domain. We accomplish this by reinforcing
associations between source and target data directly in embedding space. Our
method can easily be added to any existing classification network with no
structural and almost no computational overhead. We demonstrate the
effectiveness of our approach on various benchmarks and achieve
state-of-the-art results across the board with a generic convolutional neural
network architecture not specifically tuned to the respective tasks. Finally,
we show that the proposed association loss produces embeddings that are more
effective for domain adaptation compared to methods employing maximum mean
discrepancy as a similarity measure in embedding space.
PROBE: Predictive Robust Estimation for Visual-Inertial Navigation
Authors: Valentin Peretroukhin, Lee Clement, Matthew Giamou, Jonathan Kelly
Date: 2017-08-01 00:00:00

Navigation in unknown, chaotic environments continues to present a significant challenge for the robotics community. Lighting changes, self-similar textures, motion blur, and moving objects are all considerable stumbling blocks for state-of-the-art vision-based navigation algorithms. In this paper we present a novel technique for improving localization accuracy within a visual-inertial navigation system (VINS). We make use of training data to learn a model for the quality of visual features with respect to localization error in a given environment. This model maps each visual observation from a predefined prediction space of visual-inertial predictors onto a scalar weight, which is then used to scale the observation covariance matrix. In this way, our model can adjust the influence of each observation according to its quality. We discuss our choice of predictors and report substantial reductions in localization error on 4 km of data from the KITTI dataset, as well as on experimental datasets consisting of 700 m of indoor and outdoor driving on a small ground rover equipped with a Skybotix VI-Sensor.
Combining Keystroke Dynamics and Face Recognition for User Verification
Authors: Abhinav Gupta, Agrim Khanna, Anmol Jagetia, Devansh Sharma, Sanchit Alekh, Vaibhav Choudhary
Date: 2017-08-02 00:00:00
Tasks: Face Recognition

The massive explosion and ubiquity of computing devices and the outreach of
the web have been the most defining events of the century so far. As more and
more people gain access to the internet, traditional know-something and
have-something authentication methods such as PINs and passwords are proving to
be insufficient for prohibiting unauthorized access to increasingly personal
data on the web. Therefore, the need of the hour is a user-verification system
that is not only more reliable and secure, but also unobtrusive and
minimalistic. Keystroke Dynamics is a novel Biometric Technique; it is not only
unobtrusive, but also transparent and inexpensive. The fusion of keystroke
dynamics and Face Recognition engenders the most desirable characteristics of a
verification system. Our implementation uses Hidden Markov Models (HMM) for
modelling the Keystroke Dynamics, with the help of two widely used Feature
Vectors: Keypress Latency and Keypress Duration. On the other hand, Face
Recognition makes use of the traditional Eigenfaces approach.The results show
that the system has a high precision, with a False Acceptance Rate of 5.4% and
a False Rejection Rate of 9.2%. Moreover, it is also future-proof, as the
hardware requirements, i.e. camera and keyboard (physical or on-screen), have
become an indispensable part of modern computing.
Learning to Generate Chairs, Tables and Cars with Convolutional Networks
Authors: Alexey Dosovitskiy, Jost Tobias Springenberg, Maxim Tatarchenko, Thomas Brox
Date: 2014-11-21 00:00:00
Tasks: Object

We train generative 'up-convolutional' neural networks which are able to
generate images of objects given object style, viewpoint, and color. We train
the networks on rendered 3D models of chairs, tables, and cars. Our experiments
show that the networks do not merely learn all images by heart, but rather find
a meaningful representation of 3D models allowing them to assess the similarity
of different models, interpolate between given views to generate the missing
ones, extrapolate views, and invent new objects not present in the training set
by recombining training instances, or even two different object classes.
Moreover, we show that such generative networks can be used to find
correspondences between different objects from the dataset, outperforming
existing approaches on this task.
Audio Super Resolution using Neural Networks
Authors: Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon
Date: 2017-08-02 00:00:00
Tasks: Audio Generation, Audio Super-Resolution, Super-Resolution, text-to-speech, Text to Speech

We introduce a new audio processing technique that increases the sampling
rate of signals such as speech or music using deep convolutional neural
networks. Our model is trained on pairs of low and high-quality audio examples;
at test-time, it predicts missing samples within a low-resolution signal in an
interpolation process similar to image super-resolution. Our method is simple
and does not involve specialized audio processing techniques; in our
experiments, it outperforms baselines on standard speech and music benchmarks
at upscaling ratios of 2x, 4x, and 6x. The method has practical applications in
telephony, compression, and text-to-speech generation; it demonstrates the
effectiveness of feed-forward convolutional architectures on an audio
generation task.