pwc-archive/papers-with-abstracts huggingface dataset:
DENSER Cities: A System for Dense Efficient Reconstructions of Cities
Authors: Michael Tanner, Pedro Pinies, Lina Maria Paz, Paul Newman
Date: 2016-04-13 00:00:00

This paper is about the efficient generation of dense, colored models of
city-scale environments from range data and in particular, stereo cameras.
Better maps make for better understanding; better understanding leads to better
robots, but this comes at a cost. The computational and memory requirements of
large dense models can be prohibitive. We provide the theory and the system
needed to create city-scale dense reconstructions. To do so, we apply a
regularizer over a compressed 3D data structure while dealing with the complex
boundary conditions this induces during the data-fusion stage. We show that
only with these considerations can we swiftly create neat, large, "well
behaved" reconstructions. We evaluate our system using the KITTI dataset and
provide statistics for the metric errors in all surfaces created compared to
those measured with 3D laser. Our regularizer reduces the median error by 40%
in 3.4 km of dense reconstructions with a median accuracy of 6 cm. For
subjective analysis, we provide a qualitative review of 6.1 km of our dense
reconstructions in an attached video. These are the largest dense
reconstructions from a single passive camera we are aware of in the literature.
A Novel Method to Study Bottom-up Visual Saliency and its Neural Mechanism
Authors: Cheng Chen, Xilin Zhang, Yizhou Wang, Fang Fang
Date: 2016-04-13 00:00:00

In this study, we propose a novel method to measure bottom-up saliency maps
of natural images. In order to eliminate the influence of top-down signals,
backward masking is used to make stimuli (natural images) subjectively
invisible to subjects, however, the bottom-up saliency can still orient the
subjects attention. To measure this orientation/attention effect, we adopt the
cueing effect paradigm by deploying discrimination tasks at each location of an
image, and measure the discrimination performance variation across the image as
the attentional effect of the bottom-up saliency. Such attentional effects are
combined to construct a final bottomup saliency map. Based on the proposed
method, we introduce a new bottom-up saliency map dataset of natural images to
benchmark computational models. We compare several state-of-the-art saliency
models on the dataset. Moreover, the proposed paradigm is applied to
investigate the neural basis of the bottom-up visual saliency map by analyzing
psychophysical and fMRI experimental results. Our findings suggest that the
bottom-up saliency maps of natural images are constructed in V1. It provides a
strong scientific evidence to resolve the long standing dispute in neuroscience
about where the bottom-up saliency map is constructed in human brain.
Quantifying uncertainties on excursion sets under a Gaussian random field prior
Authors: Dario Azzimonti, Julien Bect, Clément Chevalier, David Ginsbourger
Date: 2015-01-15 00:00:00

We focus on the problem of estimating and quantifying uncertainties on the
excursion set of a function under a limited evaluation budget. We adopt a
Bayesian approach where the objective function is assumed to be a realization
of a Gaussian random field. In this setting, the posterior distribution on the
objective function gives rise to a posterior distribution on excursion sets.
Several approaches exist to summarize the distribution of such sets based on
random closed set theory. While the recently proposed Vorob'ev approach
exploits analytical formulae, further notions of variability require Monte
Carlo estimators relying on Gaussian random field conditional simulations. In
the present work we propose a method to choose Monte Carlo simulation points
and obtain quasi-realizations of the conditional field at fine designs through
affine predictors. The points are chosen optimally in the sense that they
minimize the posterior expected distance in measure between the excursion set
and its reconstruction. The proposed method reduces the computational costs due
to Monte Carlo simulations and enables the computation of quasi-realizations on
fine designs in large dimensions. We apply this reconstruction approach to
obtain realizations of an excursion set on a fine grid which allow us to give a
new measure of uncertainty based on the distance transform of the excursion
set. Finally we present a safety engineering test case where the simulation
method is employed to compute a Monte Carlo estimate of a contour line.
Bayesian inference in hierarchical models by combining independent posteriors
Authors: Ritabrata Dutta, Paul Blomstedt, Samuel Kaski
Date: 2016-03-30 00:00:00
Tasks: Bayesian Inference

Hierarchical models are versatile tools for joint modeling of data sets
arising from different, but related, sources. Fully Bayesian inference may,
however, become computationally prohibitive if the source-specific data models
are complex, or if the number of sources is very large. To facilitate
computation, we propose an approach, where inference is first made
independently for the parameters of each data set, whereupon the obtained
posterior samples are used as observed data in a substitute hierarchical model,
based on a scaled likelihood function. Compared to direct inference in a full
hierarchical model, the approach has the advantage of being able to speed up
convergence by breaking down the initial large inference problem into smaller
individual subproblems with better convergence properties. Moreover it enables
parallel processing of the possibly complex inferences of the source-specific
parameters, which may otherwise create a computational bottleneck if processed
jointly as part of a hierarchical model. The approach is illustrated with both
simulated and real data.
RGBD Datasets: Past, Present and Future
Authors: Michael Firman
Date: 2016-04-04 00:00:00
Tasks: Gesture Recognition, Object, Object Tracking, Pose Estimation

Since the launch of the Microsoft Kinect, scores of RGBD datasets have been
released. These have propelled advances in areas from reconstruction to gesture
recognition. In this paper we explore the field, reviewing datasets across
eight categories: semantics, object pose estimation, camera tracking, scene
reconstruction, object tracking, human actions, faces and identification. By
extracting relevant information in each category we help researchers to find
appropriate data for their needs, and we consider which datasets have succeeded
in driving computer vision forward and why.
  Finally, we examine the future of RGBD datasets. We identify key areas which
are currently underexplored, and suggest that future directions may include
synthetic data and dense reconstructions of static and dynamic scenes.
Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks
Authors: Junyuan Xie, Ross Girshick, Ali Farhadi
Date: 2016-04-13 00:00:00
Tasks: Depth Estimation

As 3D movie viewing becomes mainstream and Virtual Reality (VR) market
emerges, the demand for 3D contents is growing rapidly. Producing 3D videos,
however, remains challenging. In this paper we propose to use deep neural
networks for automatically converting 2D videos and images to stereoscopic 3D
format. In contrast to previous automatic 2D-to-3D conversion algorithms, which
have separate stages and need ground truth depth map as supervision, our
approach is trained end-to-end directly on stereo pairs extracted from 3D
movies. This novel training scheme makes it possible to exploit orders of
magnitude more data and significantly increases performance. Indeed, Deep3D
outperforms baselines in both quantitative and human subject evaluations.
Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex
Authors: Qianli Liao, Tomaso Poggio
Date: 2016-04-13 00:00:00

We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the primate visual cortex. We begin with the observation that a special type of shallow RNN is exactly equivalent to a very deep ResNet with weight sharing among the layers. A direct implementation of such a RNN, although having orders of magnitude fewer parameters, leads to a performance similar to the corresponding ResNet. We propose 1) a generalization of both RNN and ResNet architectures and 2) the conjecture that a class of moderately deep RNNs is a biologically-plausible model of the ventral stream in visual cortex. We demonstrate the effectiveness of the architectures by testing them on the CIFAR-10 and ImageNet dataset.
ProNet: Learning to Propose Object-specific Boxes for Cascaded Neural Networks
Authors: Chen Sun, Manohar Paluri, Ronan Collobert, Ram Nevatia, Lubomir Bourdev
Date: 2015-11-12 00:00:00
Proceeding: CVPR 2016 6
Tasks: Classification, General Classification, Object, Weakly Supervised Object Detection

This paper aims to classify and locate objects accurately and efficiently,
without using bounding box annotations. It is challenging as objects in the
wild could appear at arbitrary locations and in different scales. In this
paper, we propose a novel classification architecture ProNet based on
convolutional neural networks. It uses computationally efficient neural
networks to propose image regions that are likely to contain objects, and
applies more powerful but slower networks on the proposed regions. The basic
building block is a multi-scale fully-convolutional network which assigns
object confidence scores to boxes at different locations and scales. We show
that such networks can be trained effectively using image-level annotations,
and can be connected into cascades or trees for efficient object
classification. ProNet outperforms previous state-of-the-art significantly on
PASCAL VOC 2012 and MS COCO datasets for object classification and point-based
localization.
Dissecting a Social Botnet: Growth, Content and Influence in Twitter
Authors: Norah Abokhodair, Daisy Yoo, David W. McDonald
Date: 2016-04-13 00:00:00

Social botnets have become an important phenomenon on social media. There are
many ways in which social bots can disrupt or influence online discourse, such
as, spam hashtags, scam twitter users, and astroturfing. In this paper we
considered one specific social botnet in Twitter to understand how it grows
over time, how the content of tweets by the social botnet differ from regular
users in the same dataset, and lastly, how the social botnet may have
influenced the relevant discussions. Our analysis is based on a qualitative
coding for approximately 3000 tweets in Arabic and English from the Syrian
social bot that was active for 35 weeks on Twitter before it was shutdown. We
find that the growth, behavior and content of this particular botnet did not
specifically align with common conceptions of botnets. Further we identify
interesting aspects of the botnet that distinguish it from regular users.
Community Detection with Node Attributes and its Generalization
Authors: Yuan Li
Date: 2016-04-12 00:00:00
Tasks: Community Detection

Community detection algorithms are fundamental tools to understand
organizational principles in social networks. With the increasing power of
social media platforms, when detecting communities there are two possi- ble
sources of information one can use: the structure of social network and node
attributes. However structure of social networks and node attributes are often
interpreted separately in the research of community detection. When these two
sources are interpreted simultaneously, one common as- sumption shared by
previous studies is that nodes attributes are correlated with communities. In
this paper, we present a model that is capable of combining topology
information and nodes attributes information with- out assuming correlation.
This new model can recover communities with higher accuracy even when node
attributes and communities are uncorre- lated. We derive the detectability
threshold for this model and use Belief Propagation (BP) to make inference.
This algorithm is optimal in the sense that it can recover community all the
way down to the threshold. This new model is also with the potential to handle
edge content and dynamic settings.
Plan Explicability and Predictability for Robot Task Planning
Authors: Yu Zhang, Sarath Sreedharan, Anagha Kulkarni, Tathagata Chakraborti, Hankz Hankui Zhuo, Subbarao Kambhampati
Date: 2015-11-25 00:00:00
Tasks: Motion Planning, Robot Task Planning, Task Planning

Intelligent robots and machines are becoming pervasive in human populated
environments. A desirable capability of these agents is to respond to
goal-oriented commands by autonomously constructing task plans. However, such
autonomy can add significant cognitive load and potentially introduce safety
risks to humans when agents behave unexpectedly. Hence, for such agents to be
helpful, one important requirement is for them to synthesize plans that can be
easily understood by humans. While there exists previous work that studied
socially acceptable robots that interact with humans in "natural ways", and
work that investigated legible motion planning, there lacks a general solution
for high level task planning. To address this issue, we introduce the notions
of plan {\it explicability} and {\it predictability}. To compute these
measures, first, we postulate that humans understand agent plans by associating
abstract tasks with agent actions, which can be considered as a labeling
process. We learn the labeling scheme of humans for agent plans from training
examples using conditional random fields (CRFs). Then, we use the learned model
to label a new plan to compute its explicability and predictability. These
measures can be used by agents to proactively choose or directly synthesize
plans that are more explicable and predictable to humans. We provide
evaluations on a synthetic domain and with human subjects using physical robots
to show the effectiveness of our approach
Enhanced Low-Rank Matrix Approximation
Authors: Ankit Parekh, Ivan W. Selesnick
Date: 2015-11-06 00:00:00
Tasks: Denoising, Image Denoising

This letter proposes to estimate low-rank matrices by formulating a convex
optimization problem with non-convex regularization. We employ parameterized
non-convex penalty functions to estimate the non-zero singular values more
accurately than the nuclear norm. A closed-form solution for the global optimum
of the proposed objective function (sum of data fidelity and the non-convex
regularizer) is also derived. The solution reduces to singular value
thresholding method as a special case. The proposed method is demonstrated for
image denoising.
Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels
Authors: Ishan Misra, C. Lawrence Zitnick, Margaret Mitchell, Ross Girshick
Date: 2015-12-22 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Captioning, image-classification, Image Classification

When human annotators are given a choice about what to label in an image,
they apply their own subjective judgments on what to ignore and what to
mention. We refer to these noisy "human-centric" annotations as exhibiting
human reporting bias. Examples of such annotations include image tags and
keywords found on photo sharing sites, or in datasets containing image
captions. In this paper, we use these noisy annotations for learning visually
correct image classifiers. Such annotations do not use consistent vocabulary,
and miss a significant amount of the information present in an image; however,
we demonstrate that the noise in these annotations exhibits structure and can
be modeled. We propose an algorithm to decouple the human reporting bias from
the correct visually grounded labels. Our results are highly interpretable for
reporting "what's in the image" versus "what's worth saying." We demonstrate
the algorithm's efficacy along a variety of metrics and datasets, including MS
COCO and Yahoo Flickr 100M. We show significant improvements over traditional
algorithms for both image classification and image captioning, doubling the
performance of existing methods in some cases.
Training Region-based Object Detectors with Online Hard Example Mining
Authors: Abhinav Shrivastava, Abhinav Gupta, Ross Girshick
Date: 2016-04-12 00:00:00
Proceeding: CVPR 2016 6
Tasks: object-detection, Object Detection

The field of object detection has made significant advances riding on the
wave of region-based ConvNets, but their training procedure still includes many
heuristics and hyperparameters that are costly to tune. We present a simple yet
surprisingly effective online hard example mining (OHEM) algorithm for training
region-based ConvNet detectors. Our motivation is the same as it has always
been -- detection datasets contain an overwhelming number of easy examples and
a small number of hard examples. Automatic selection of these hard examples can
make training more effective and efficient. OHEM is a simple and intuitive
algorithm that eliminates several heuristics and hyperparameters in common use.
But more importantly, it yields consistent and significant boosts in detection
performance on benchmarks like PASCAL VOC 2007 and 2012. Its effectiveness
increases as datasets become larger and more difficult, as demonstrated by the
results on the MS COCO dataset. Moreover, combined with complementary advances
in the field, OHEM leads to state-of-the-art results of 78.9% and 76.3% mAP on
PASCAL VOC 2007 and 2012 respectively.
Cross-stitch Networks for Multi-task Learning
Authors: Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, Martial Hebert
Date: 2016-04-12 00:00:00
Proceeding: CVPR 2016 6
Tasks: Multi-Task Learning, Semantic Segmentation

Multi-task learning in Convolutional Networks has displayed remarkable
success in the field of recognition. This success can be largely attributed to
learning shared representations from multiple supervisory tasks. However,
existing multi-task approaches rely on enumerating multiple network
architectures specific to the tasks at hand, that do not generalize. In this
paper, we propose a principled approach to learn shared representations in
ConvNets using multi-task learning. Specifically, we propose a new sharing
unit: "cross-stitch" unit. These units combine the activations from multiple
networks and can be trained end-to-end. A network with cross-stitch units can
learn an optimal combination of shared and task-specific representations. Our
proposed method generalizes across multiple tasks and shows dramatically
improved performance over baseline methods for categories with few training
examples.
Spatiotemporal Articulated Models for Dynamic SLAM
Authors: Suren Kumar, Vikas Dhiman, Madan Ravi Ganesh, Jason J. Corso
Date: 2016-04-12 00:00:00
Tasks: Simultaneous Localization and Mapping

We propose an online spatiotemporal articulation model estimation framework
that estimates both articulated structure as well as a temporal prediction
model solely using passive observations. The resulting model can predict future
mo- tions of an articulated object with high confidence because of the spatial
and temporal structure. We demonstrate the effectiveness of the predictive
model by incorporating it within a standard simultaneous localization and
mapping (SLAM) pipeline for mapping and robot localization in previously
unexplored dynamic environments. Our method is able to localize the robot and
map a dynamic scene by explaining the observed motion in the world. We
demonstrate the effectiveness of the proposed framework for both simulated and
real-world dynamic environments.
Assessing forensic evidence by computing belief functions
Authors: Timber Kerkvliet, Ronald Meester
Date: 2015-12-02 00:00:00

We first discuss certain problems with the classical probabilistic approach
for assessing forensic evidence, in particular its inability to distinguish
between lack of belief and disbelief, and its inability to model complete
ignorance within a given population. We then discuss Shafer belief functions, a
generalization of probability distributions, which can deal with both these
objections. We use a calculus of belief functions which does not use the much
criticized Dempster rule of combination, but only the very natural
Dempster-Shafer conditioning. We then apply this calculus to some classical
forensic problems like the various island problems and the problem of parental
identification. If we impose no prior knowledge apart from assuming that the
culprit or parent belongs to a given population (something which is possible in
our setting), then our answers differ from the classical ones when uniform or
other priors are imposed. We can actually retrieve the classical answers by
imposing the relevant priors, so our setup can and should be interpreted as a
generalization of the classical methodology, allowing more flexibility. We show
how our calculus can be used to develop an analogue of Bayes' rule, with belief
functions instead of classical probabilities. We also discuss consequences of
our theory for legal practice.
DTM: Deformable Template Matching
Authors: Hyungtae Lee, Heesung Kwon, Ryan M. Robinson, William D. Nothwang
Date: 2016-04-12 00:00:00
Tasks: Object Recognition, Template Matching

A novel template matching algorithm that can incorporate the concept of
deformable parts, is presented in this paper. Unlike the deformable part model
(DPM) employed in object recognition, the proposed template-matching approach
called Deformable Template Matching (DTM) does not require a training step.
Instead, deformation is achieved by a set of predefined basic rules (e.g. the
left sub-patch cannot pass across the right patch). Experimental evaluation of
this new method using the PASCAL VOC 07 dataset demonstrated substantial
performance improvement over conventional template matching algorithms.
Additionally, to confirm the applicability of DTM, the concept is applied to
the generation of a rotation-invariant SIFT descriptor. Experimental evaluation
employing deformable matching of SIFT features shows an increased number of
matching features compared to a conventional SIFT matching.
Fast Object Localization Using a CNN Feature Map Based Multi-Scale Search
Authors: Hyungtae Lee, Heesung Kwon, Archith J. Bency, William D. Nothwang
Date: 2016-04-12 00:00:00
Tasks: Object Localization

Object localization is an important task in computer vision but requires a
large amount of computational power due mainly to an exhaustive multiscale
search on the input image. In this paper, we describe a near real-time
multiscale search on a deep CNN feature map that does not use region proposals.
The proposed approach effectively exploits local semantic information preserved
in the feature map of the outermost convolutional layer. A multi-scale search
is performed on the feature map by processing all the sub-regions of different
sizes using separate expert units of fully connected layers. Each expert unit
receives as input local semantic features only from the corresponding
sub-regions of a specific geometric shape. Therefore, it contains more nearly
optimal parameters tailored to the corresponding shape. This multi-scale and
multi-aspect ratio scanning strategy can effectively localize a potential
object of an arbitrary size. The proposed approach is fast and able to localize
objects of interest with a frame rate of 4 fps while providing improved
detection performance over the state-of-the art on the PASCAL VOC 12 and MSCOCO
data sets.