pwc-archive/papers-with-abstracts huggingface dataset:
Learning non-maximum suppression
Authors: Jan Hosang, Rodrigo Benenson, Bernt Schiele
Date: 2017-05-08 00:00:00
Proceeding: CVPR 2017 7
Tasks: Clustering, Human Detection, Object, object-detection, Object Detection, Occlusion Handling

Object detectors have hugely profited from moving towards an end-to-end
learning paradigm: proposals, features, and the classifier becoming one neural
network improved results two-fold on general object detection. One
indispensable component is non-maximum suppression (NMS), a post-processing
algorithm responsible for merging all detections that belong to the same
object. The de facto standard NMS algorithm is still fully hand-crafted,
suspiciously simple, and -- being based on greedy clustering with a fixed
distance threshold -- forces a trade-off between recall and precision. We
propose a new network architecture designed to perform NMS, using only boxes
and their score. We report experiments for person detection on PETS and for
general object categories on the COCO dataset. Our approach shows promise
providing improved localization and occlusion handling.
Semiparametric spectral modeling of the Drosophila connectome
Authors: Carey E. Priebe, Youngser Park, Minh Tang, Avanti Athreya, Vince Lyzinski, Joshua T. Vogelstein, Yichen Qin, Ben Cocanougher, Katharina Eichler, Marta Zlatic, Albert Cardona
Date: 2017-05-09 00:00:00
Tasks: Position, Stochastic Block Model

We present semiparametric spectral modeling of the complete larval Drosophila
mushroom body connectome. Motivated by a thorough exploratory data analysis of
the network via Gaussian mixture modeling (GMM) in the adjacency spectral
embedding (ASE) representation space, we introduce the latent structure model
(LSM) for network modeling and inference. LSM is a generalization of the
stochastic block model (SBM) and a special case of the random dot product graph
(RDPG) latent position model, and is amenable to semiparametric GMM in the ASE
representation space. The resulting connectome code derived via semiparametric
GMM composed with ASE captures latent connectome structure and elucidates
biologically relevant neuronal properties.
Thresholding Bandits with Augmented UCB
Authors: Subhojyoti Mukherjee, K. P. Naveen, Nandan Sudarsanam, Balaraman Ravindran
Date: 2017-04-07 00:00:00

In this paper we propose the Augmented-UCB (AugUCB) algorithm for a fixed-budget version of the thresholding bandit problem (TBP), where the objective is to identify a set of arms whose quality is above a threshold. A key feature of AugUCB is that it uses both mean and variance estimates to eliminate arms that have been sufficiently explored; to the best of our knowledge this is the first algorithm to employ such an approach for the considered TBP. Theoretically, we obtain an upper bound on the loss (probability of mis-classification) incurred by AugUCB. Although UCBEV in literature provides a better guarantee, it is important to emphasize that UCBEV has access to problem complexity (whose computation requires arms' mean and variances), and hence is not realistic in practice; this is in contrast to AugUCB whose implementation does not require any such complexity inputs. We conduct extensive simulation experiments to validate the performance of AugUCB. Through our simulation work, we establish that AugUCB, owing to its utilization of variance estimates, performs significantly better than the state-of-the-art APT, CSAR and other non variance-based algorithms.
Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation
Authors: Iiris Sundin, Tomi Peltola, Muntasir Mamun Majumder, Pedram Daee, Marta Soare, Homayun Afrabandpey, Caroline Heckman, Samuel Kaski, Pekka Marttinen
Date: 2017-05-09 00:00:00
Tasks: Experimental Design, Sensitivity

Predicting the efficacy of a drug for a given individual, using
high-dimensional genomic measurements, is at the core of precision medicine.
However, identifying features on which to base the predictions remains a
challenge, especially when the sample size is small. Incorporating expert
knowledge offers a promising alternative to improve a prediction model, but
collecting such knowledge is laborious to the expert if the number of candidate
features is very large. We introduce a probabilistic model that can incorporate
expert feedback about the impact of genomic measurements on the sensitivity of
a cancer cell for a given drug. We also present two methods to intelligently
collect this feedback from the expert, using experimental design and
multi-armed bandit models. In a multiple myeloma blood cancer data set (n=51),
expert knowledge decreased the prediction error by 8%. Furthermore, the
intelligent approaches can be used to reduce the workload of feedback
collection to less than 30% on average compared to a naive approach.
Learning Sentence Representation with Guidance of Human Attention
Authors: Shaonan Wang, Jiajun Zhang, Cheng-qing Zong
Date: 2016-09-29 00:00:00
Tasks: POS, Sentence

Recently, much progress has been made in learning general-purpose sentence
representations that can be used across domains. However, most of the existing
models typically treat each word in a sentence equally. In contrast, extensive
studies have proven that human read sentences efficiently by making a sequence
of fixation and saccades. This motivates us to improve sentence representations
by assigning different weights to the vectors of the component words, which can
be treated as an attention mechanism on single sentences. To that end, we
propose two novel attention models, in which the attention weights are derived
using significant predictors of human reading time, i.e., Surprisal, POS tags
and CCG supertags. The extensive experiments demonstrate that the proposed
methods significantly improve upon the state-of-the-art sentence representation
models.
Evidence for the size principle in semantic and perceptual domains
Authors: Joshua C. Peterson, Thomas L. Griffiths
Date: 2017-05-09 00:00:00

Shepard's Universal Law of Generalization offered a compelling case for the
first physics-like law in cognitive science that should hold for all
intelligent agents in the universe. Shepard's account is based on a rational
Bayesian model of generalization, providing an answer to the question of why
such a law should emerge. Extending this account to explain how humans use
multiple examples to make better generalizations requires an additional
assumption, called the size principle: hypotheses that pick out fewer objects
should make a larger contribution to generalization. The degree to which this
principle warrants similarly law-like status is far from conclusive. Typically,
evaluating this principle has not been straightforward, requiring additional
assumptions. We present a new method for evaluating the size principle that is
more direct, and apply this method to a diverse array of datasets. Our results
provide support for the broad applicability of the size principle.
Paving the Roadway for Safety of Automated Vehicles: An Empirical Study on Testing Challenges
Authors: Alessia Knauss, Jan Schröder, Christian Berger, Henrik Eriksson
Date: 2017-05-09 00:00:00

The technology in the area of automated vehicles is gaining speed and
promises many advantages. However, with the recent introduction of
conditionally automated driving, we have also seen accidents. Test protocols
for both, conditionally automated (e.g., on highways) and automated vehicles do
not exist yet and leave researchers and practitioners with different
challenges. For instance, current test procedures do not suffice for fully
automated vehicles, which are supposed to be completely in charge for the
driving task and have no driver as a back up. This paper presents current
challenges of testing the functionality and safety of automated vehicles
derived from conducting focus groups and interviews with 26 participants from
five countries having a background related to testing automotive safety-related
topics.We provide an overview of the state-of-practice of testing active safety
features as well as challenges that needs to be addressed in the future to
ensure safety for automated vehicles. The major challenges identified through
the interviews and focus groups, enriched by literature on this topic are
related to 1) virtual testing and simulation, 2) safety, reliability, and
quality, 3) sensors and sensor models, 4) required scenario complexity and
amount of test cases, and 5) handover of responsibility between the driver and
the vehicle.
Diving Performance Assessment by means of Video Processing
Authors: Stefano Frassinelli, Alessandro Niccolai, Riccardo E. Zich
Date: 2017-05-09 00:00:00

The aim of this paper is to present a procedure for video analysis applied in
an innovative way to diving performance assessment. Sport performance analysis
is a trend that is growing exponentially for all level athletes. The technique
here shown is based on two important requirements: flexibility and low cost.
These two requirements lead to many problems in the video processing that have
been faced and solved in this paper.
ArtTrack: Articulated Multi-person Tracking in the Wild
Authors: Eldar Insafutdinov, Mykhaylo Andriluka, Leonid Pishchulin, Siyu Tang, Evgeny Levinkov, Bjoern Andres, Bernt Schiele
Date: 2016-12-05 00:00:00
Proceeding: CVPR 2017 7
Tasks: Keypoint Detection, Multi-Person Pose Estimation, Pose Estimation

In this paper we propose an approach for articulated tracking of multiple
people in unconstrained videos. Our starting point is a model that resembles
existing architectures for single-frame pose estimation but is substantially
faster. We achieve this in two ways: (1) by simplifying and sparsifying the
body-part relationship graph and leveraging recent methods for faster
inference, and (2) by offloading a substantial share of computation onto a
feed-forward convolutional architecture that is able to detect and associate
body joints of the same person even in clutter. We use this model to generate
proposals for body joint locations and formulate articulated tracking as
spatio-temporal grouping of such proposals. This allows to jointly solve the
association problem for all people in the scene by propagating evidence from
strong detections through time and enforcing constraints that each proposal can
be assigned to one person only. We report results on a public MPII Human Pose
benchmark and on a new MPII Video Pose dataset of image sequences with multiple
people. We demonstrate that our model achieves state-of-the-art results while
using only a fraction of time and is able to leverage temporal information to
improve state-of-the-art for crowded scenes.
Basic protocols in quantum reinforcement learning with superconducting circuits
Authors: Lucas Lamata
Date: 2017-01-18 00:00:00
Tasks: BIG-bench Machine Learning, Quantum Machine Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Superconducting circuit technologies have recently achieved quantum protocols
involving closed feedback loops. Quantum artificial intelligence and quantum
machine learning are emerging fields inside quantum technologies which may
enable quantum devices to acquire information from the outer world and improve
themselves via a learning process. Here we propose the implementation of basic
protocols in quantum reinforcement learning, with superconducting circuits
employing feedback-loop control. We introduce diverse scenarios for
proof-of-principle experiments with state-of-the-art superconducting circuit
technologies and analyze their feasibility in presence of imperfections. The
field of quantum artificial intelligence implemented with superconducting
circuits paves the way for enhanced quantum control and quantum computation
protocols.
A Systematic Review of Hindi Prosody
Authors: Somnath Roy
Date: 2017-05-09 00:00:00
Tasks: Sentence

Prosody describes both form and function of a sentence using the
suprasegmental features of speech. Prosody phenomena are explored in the domain
of higher phonological constituents such as word, phonological phrase and
intonational phrase. The study of prosody at the word level is called word
prosody and above word level is called sentence prosody. Word Prosody describes
stress pattern by comparing the prosodic features of its constituent syllables.
Sentence Prosody involves the study on phrasing pattern and intonatonal pattern
of a language. The aim of this study is to summarize the existing works on
Hindi prosody carried out in different domain of language and speech
processing. The review is presented in a systematic fashion so that it could be
a useful resource for one who wants to build on the existing works.
Convolutional Dictionary Learning via Local Processing
Authors: Vardan Papyan, Yaniv Romano, Jeremias Sulam, Michael Elad
Date: 2017-05-09 00:00:00
Proceeding: ICCV 2017 10
Tasks: Dictionary Learning, Image Inpainting, Relation

Convolutional Sparse Coding (CSC) is an increasingly popular model in the
signal and image processing communities, tackling some of the limitations of
traditional patch-based sparse representations. Although several works have
addressed the dictionary learning problem under this model, these relied on an
ADMM formulation in the Fourier domain, losing the sense of locality and the
relation to the traditional patch-based sparse pursuit. A recent work suggested
a novel theoretical analysis of this global model, providing guarantees that
rely on a localized sparsity measure. Herein, we extend this local-global
relation by showing how one can efficiently solve the convolutional sparse
pursuit problem and train the filters involved, while operating locally on
image patches. Our approach provides an intuitive algorithm that can leverage
standard techniques from the sparse representations field. The proposed method
is fast to train, simple to implement, and flexible enough that it can be
easily deployed in a variety of applications. We demonstrate the proposed
training scheme for image inpainting and image separation, while achieving
state-of-the-art results.
A Benchmark Dataset and Saliency-guided Stacked Autoencoders for Video-based Salient Object Detection
Authors: Jia Li, Changqun Xia, Xiaowu Chen
Date: 2016-11-01 00:00:00
Tasks: Benchmarking, Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Image-based salient object detection (SOD) has been extensively studied in
the past decades. However, video-based SOD is much less explored since there
lack large-scale video datasets within which salient objects are unambiguously
defined and annotated. Toward this end, this paper proposes a video-based SOD
dataset that consists of 200 videos (64 minutes). In constructing the dataset,
we manually annotate all objects and regions over 7,650 uniformly sampled
keyframes and collect the eye-tracking data of 23 subjects that free-view all
videos. From the user data, we find salient objects in video can be defined as
objects that consistently pop-out throughout the video, and objects with such
attributes can be unambiguously annotated by combining manually annotated
object/region masks with eye-tracking data of multiple subjects. To the best of
our knowledge, it is currently the largest dataset for video-based salient
object detection.
  Based on this dataset, this paper proposes an unsupervised baseline approach
for video-based SOD by using saliency-guided stacked autoencoders. In the
proposed approach, multiple spatiotemporal saliency cues are first extracted at
pixel, superpixel and object levels. With these saliency cues, stacked
autoencoders are unsupervisedly constructed which automatically infer a
saliency score for each pixel by progressively encoding the high-dimensional
saliency cues gathered from the pixel and its spatiotemporal neighbors.
Experimental results show that the proposed unsupervised approach outperforms
30 state-of-the-art models on the proposed dataset, including 19 image-based &
classic (unsupervised or non-deep learning), 6 image-based & deep learning, and
5 video-based & unsupervised. Moreover, benchmarking results show that the
proposed dataset is very challenging and has the potential to boost the
development of video-based SOD.
Efficient Structure from Motion for Oblique UAV Images Based on Maximal Spanning Tree Expansions
Authors: San Jiang, Wanshou Jiang
Date: 2017-05-09 00:00:00

The primary contribution of this paper is an efficient Structure from Motion
(SfM) solution for oblique unmanned aerial vehicle (UAV) images. First, an
algorithm, considering spatial relationship constrains between image
footprints, is designed for match pair selection with assistant of UAV flight
control data and oblique camera mounting angles. Second, a topological
connection network (TCN), represented by an undirected weighted graph, is
constructed from initial match pairs, which encodes overlap area and
intersection angle into edge weights. Then, an algorithm, termed MST-Expansion,
is proposed to extract the match graph from the TCN where the TCN is firstly
simplified by a maximum spanning tree (MST). By further analysis of local
structure in the MST, expansion operations are performed on the nodes of the
MST for match graph enhancement, which is achieved by introducing critical
connections in two expansion directions. Finally, guided by the match graph, an
efficient SfM solution is proposed, and its validation is verified through
comprehensive analysis and comparison using three UAV datasets captured with
different oblique multi-camera systems. Experiment results demonstrate that the
efficiency of image matching is improved with a speedup ratio ranging from 19
to 35, and competitive orientation accuracy is achieved from both relative
bundle adjustment (BA) without GCPs (Ground Control Points) and absolute BA
with GCPs. At the same time, images in the three datasets are successfully
oriented. For orientation of oblique UAV images, the proposed method can be a
more efficient solution.
Solving a Path Planning Problem in a Partially Known Environment using a Swarm Algorithm
Authors: Esh Vckay, Mansimar Aneja, Dipti Deodhare
Date: 2017-05-09 00:00:00

This paper proposes a path planning strategy for an Autonomous Ground Vehicle (AGV) navigating in a partially known environment. Global path planning is performed by first using a spatial database of the region to be traversed containing selected attributes such as height data and soil information from a suitable spatial database. The database is processed using a biomimetic swarm algorithm that is inspired by the nest building strategies followed by termites. Local path planning is performed online utilizing information regarding contingencies that affect the safe navigation of the AGV from various sensors. The simulation discussed has been implemented on the open source Player-Stage-Gazebo platform.
Emotional Metaheuristics For in-situ Foraging Using Sensor Constrained Robot Swarms
Authors: Esh Vckay, Debasish Ghose
Date: 2017-05-09 00:00:00

We present a new social animal inspired emotional swarm intelligence technique. This technique is used to solve a variant of the popular collective robots problem called foraging. We show with a simulation study how simple interaction rules based on sensations like hunger and loneliness can lead to globally coherent emergent behavior which allows sensor constrained robots to solve the given problem
Joint Multi-Leaf Segmentation, Alignment and Tracking from Fluorescence Plant Videos
Authors: Xi Yin, Xiaoming Liu, Jin Chen, David M. Kramer
Date: 2015-05-02 00:00:00

This paper proposes a novel framework for fluorescence plant video
processing. The plant research community is interested in the leaf-level
photosynthetic analysis within a plant. A prerequisite for such analysis is to
segment all leaves, estimate their structures, and track them over time. We
identify this as a joint multi-leaf segmentation, alignment, and tracking
problem. First, leaf segmentation and alignment are applied on the last frame
of a plant video to find a number of well-aligned leaf candidates. Second, leaf
tracking is applied on the remaining frames with leaf candidate transformation
from the previous frame. We form two optimization problems with shared terms in
their objective functions for leaf alignment and tracking respectively. A
quantitative evaluation framework is formulated to evaluate the performance of
our algorithm with four metrics. Two models are learned to predict the
alignment accuracy and detect tracking failure respectively in order to provide
guidance for subsequent plant biology analysis. The limitation of our algorithm
is also studied. Experimental results show the effectiveness, efficiency, and
robustness of the proposed method.
Automated Low-cost Terrestrial Laser Scanner for Measuring Diameters at Breast Height and Heights of Forest Trees
Authors: Pei Wang, Guochao Bu, Ronghao Li, Rui Zhao
Date: 2017-02-08 00:00:00
Tasks: Position

Terrestrial laser scanner is a kind of fast, high-precision data acquisition
device, which had been more and more applied to the research areas of forest
inventory. In this study, a kind of automated low-cost terrestrial laser
scanner was designed and implemented based on a two-dimensional laser radar
sensor SICK LMS-511 and a stepper motor. The new scanner was named as BEE,
which can scan the forest trees in three dimension. The BEE scanner and its
supporting software are specifically designed for forest inventory. The
experiments have been performed by using the BEE scanner in an artificial
ginkgo forest which was located in Haidian district of Beijing. Four square
plots were selected to do the experiments. The BEE scanner scanned in the four
plots and acquired the single scan data respectively. The DBH, tree height and
tree position of trees in the four plots were estimated and analyzed. For
comparison, the manual measured data was also collected in the four plots. The
tree stem detection rate for all four plots was 92.75%; the root mean square
error of the DBH estimation was 1.27cm; the root mean square error of the tree
height estimation was 0.24m; the tree position estimation was in line with the
actual position. Experimental results show that the BEE scanner can efficiently
estimate the structure parameters of forest trees and has a good potential in
practical application of forest inventory.
Counting Everyday Objects in Everyday Scenes
Authors: Prithvijit Chattopadhyay, Ramakrishna Vedantam, Ramprasaath R. Selvaraju, Dhruv Batra, Devi Parikh
Date: 2016-04-12 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, Object Counting, object-detection, Object Detection, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We are interested in counting the number of instances of object classes in
natural, everyday images. Previous counting approaches tackle the problem in
restricted domains such as counting pedestrians in surveillance videos. Counts
can also be estimated from outputs of other vision tasks like object detection.
In this work, we build dedicated models for counting designed to tackle the
large variance in counts, appearances, and scales of objects found in natural
scenes. Our approach is inspired by the phenomenon of subitizing - the ability
of humans to make quick assessments of counts given a perceptual signal, for
small count values. Given a natural scene, we employ a divide and conquer
strategy while incorporating context across the scene to adapt the subitizing
idea to counting. Our approach offers consistent improvements over numerous
baseline approaches for counting on the PASCAL VOC 2007 and COCO datasets.
Subsequently, we study how counting can be used to improve object detection. We
then show a proof of concept application of our counting methods to the task of
Visual Question Answering, by studying the `how many?' questions in the VQA and
COCO-QA datasets.