pwc-archive/papers-with-abstracts huggingface dataset:
ChromaTag: A Colored Marker and Fast Detection Algorithm
Authors: Joseph DeGol, Timothy Bretl, Derek Hoiem
Date: 2017-08-09 00:00:00
Proceeding: ICCV 2017 10
Tasks: Robot Navigation, TAG

Current fiducial marker detection algorithms rely on marker IDs for false
positive rejection. Time is wasted on potential detections that will eventually
be rejected as false positives. We introduce ChromaTag, a fiducial marker and
detection algorithm designed to use opponent colors to limit and quickly reject
initial false detections and grayscale for precise localization. Through
experiments, we show that ChromaTag is significantly faster than current
fiducial markers while achieving similar or better detection accuracy. We also
show how tag size and viewing direction effect detection accuracy. Our
contribution is significant because fiducial markers are often used in
real-time applications (e.g. marker assisted robot navigation) where heavy
computation is required by other parts of the system.
Tikhonov Regularization for Long Short-Term Memory Networks
Authors: Andrei Turkin
Date: 2017-08-09 00:00:00

It is a well-known fact that adding noise to the input data often improves
network performance. While the dropout technique may be a cause of memory loss,
when it is applied to recurrent connections, Tikhonov regularization, which can
be regarded as the training with additive noise, avoids this issue naturally,
though it implies regularizer derivation for different architectures. In case
of feedforward neural networks this is straightforward, while for networks with
recurrent connections and complicated layers it leads to some difficulties. In
this paper, a Tikhonov regularizer is derived for Long-Short Term Memory (LSTM)
networks. Although it is independent of time for simplicity, it considers
interaction between weights of the LSTM unit, which in theory makes it possible
to regularize the unit with complicated dependences by using only one parameter
that measures the input data perturbation. The regularizer that is proposed in
this paper has three parameters: one to control the regularization process, and
other two to maintain computation stability while the network is being trained.
The theory developed in this paper can be applied to get such regularizers for
different recurrent neural networks with Hadamard products and Lipschitz
continuous functions.
Hierarchically-Attentive RNN for Album Summarization and Storytelling
Authors: Licheng Yu, Mohit Bansal, Tamara L. Berg
Date: 2017-08-09 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Retrieval, Visual Storytelling

We address the problem of end-to-end visual storytelling. Given a photo
album, our model first selects the most representative (summary) photos, and
then composes a natural language story for the album. For this task, we make
use of the Visual Storytelling dataset and a model composed of three
hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album
photos, select representative (summary) photos, and compose the story.
Automatic and human evaluations show our model achieves better performance on
selection, generation, and retrieval than baselines.
Random Binary Trees for Approximate Nearest Neighbour Search in Binary Space
Authors: Michal Komorowski, Tomasz Trzcinski
Date: 2017-08-09 00:00:00
Tasks: Retrieval

Approximate nearest neighbour (ANN) search is one of the most important
problems in computer science fields such as data mining or computer vision. In
this paper, we focus on ANN for high-dimensional binary vectors and we propose
a simple yet powerful search method that uses Random Binary Search Trees
(RBST). We apply our method to a dataset of 1.25M binary local feature
descriptors obtained from a real-life image-based localisation system provided
by Google as a part of Project Tango. An extensive evaluation of our method
against the state-of-the-art variations of Locality Sensitive Hashing (LSH),
namely Uniform LSH and Multi-probe LSH, shows the superiority of our method in
terms of retrieval precision with performance boost of over 20%
Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning
Authors: Tae-Hyun Oh, Kyungdon Joo, Neel Joshi, Baoyuan Wang, In So Kweon, Sing Bing Kang
Date: 2017-08-09 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object Recognition, Semantic Segmentation

Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In
these media, dynamic and still elements are juxtaposed to create an artistic
and narrative experience. Creating a high-quality, aesthetically pleasing
cinemagraph requires isolating objects in a semantically meaningful way and
then selecting good start times and looping periods for those objects to
minimize visual artifacts (such a tearing). To achieve this, we present a new
technique that uses object recognition and semantic segmentation as part of an
optimization method to automatically create cinemagraphs from videos that are
both visually appealing and semantically meaningful. Given a scene with
multiple objects, there are many cinemagraphs one could create. Our method
evaluates these multiple candidates and presents the best one, as determined by
a model trained to predict human preferences in a collaborative way. We
demonstrate the effectiveness of our approach with multiple results and a user
study.
SUBIC: A supervised, structured binary code for image search
Authors: Himalaya Jain, Joaquin Zepeda, Patrick Pérez, Rémi Gribonval
Date: 2017-08-09 00:00:00
Proceeding: ICCV 2017 10
Tasks: Deep Hashing, Image Retrieval, Quantization, Retrieval

For large-scale visual search, highly compressed yet meaningful
representations of images are essential. Structured vector quantizers based on
product quantization and its variants are usually employed to achieve such
compression while minimizing the loss of accuracy. Yet, unlike binary hashing
schemes, these unsupervised methods have not yet benefited from the
supervision, end-to-end learning and novel architectures ushered in by the deep
learning revolution. We hence propose herein a novel method to make deep
convolutional neural networks produce supervised, compact, structured binary
codes for visual search. Our method makes use of a novel block-softmax
non-linearity and of batch-based entropy losses that together induce structure
in the learned encodings. We show that our method outperforms state-of-the-art
compact representations based on deep hashing or structured quantization in
single and cross-domain category retrieval, instance retrieval and
classification. We make our code and models publicly available online.
Item Recommendation with Continuous Experience Evolution of Users using Brownian Motion
Authors: Subhabrata Mukherjee, Stephan Guennemann, Gerhard Weikum
Date: 2017-05-07 00:00:00
Tasks: Language Modeling, Language Modelling, Recommendation Systems

Online review communities are dynamic as users join and leave, adopt new
vocabulary, and adapt to evolving trends. Recent work has shown that
recommender systems benefit from explicit consideration of user experience.
However, prior work assumes a fixed number of discrete experience levels,
whereas in reality users gain experience and mature continuously over time.
This paper presents a new model that captures the continuous evolution of user
experience, and the resulting language model in reviews and other posts. Our
model is unsupervised and combines principles of Geometric Brownian Motion,
Brownian Motion, and Latent Dirichlet Allocation to trace a smooth temporal
progression of user experience and language model respectively. We develop
practical algorithms for estimating the model parameters from data and for
inference with our model (e.g., to recommend items). Extensive experiments with
five real-world datasets show that our model not only fits data better than
discrete-model baselines, but also outperforms state-of-the-art methods for
predicting item ratings.
Understanding and Mapping Natural Beauty
Authors: Scott Workman, Richard Souvenir, Nathan Jacobs
Date: 2016-12-09 00:00:00
Proceeding: ICCV 2017 10

While natural beauty is often considered a subjective property of images, in
this paper, we take an objective approach and provide methods for quantifying
and predicting the scenicness of an image. Using a dataset containing hundreds
of thousands of outdoor images captured throughout Great Britain with
crowdsourced ratings of natural beauty, we propose an approach to predict
scenicness which explicitly accounts for the variance of human ratings. We
demonstrate that quantitative measures of scenicness can benefit semantic image
understanding, content-aware image processing, and a novel application of
cross-view mapping, where the sparsity of ground-level images can be addressed
by incorporating unlabeled overhead images in the training and prediction
steps. For each application, our methods for scenicness prediction result in
quantitative and qualitative improvements over baseline approaches.
Material Editing Using a Physically Based Rendering Network
Authors: Guilin Liu, Duygu Ceylan, Ersin Yumer, Jimei Yang, Jyh-Ming Lien
Date: 2017-08-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Generation

The ability to edit materials of objects in images is desirable by many
content creators. However, this is an extremely challenging task as it requires
to disentangle intrinsic physical properties of an image. We propose an
end-to-end network architecture that replicates the forward image formation
process to accomplish this task. Specifically, given a single image, the
network first predicts intrinsic properties, i.e. shape, illumination, and
material, which are then provided to a rendering layer. This layer performs
in-network image synthesis, thereby enabling the network to understand the
physics behind the image formation process. The proposed rendering layer is
fully differentiable, supports both diffuse and specular materials, and thus
can be applicable in a variety of problem settings. We demonstrate a rich set
of visually plausible material editing examples and provide an extensive
comparative study.
KeyXtract Twitter Model - An Essential Keywords Extraction Model for Twitter Designed using NLP Tools
Authors: Tharindu Weerasooriya, Nandula Perera, S. R. Liyanage
Date: 2017-08-09 00:00:00
Tasks: Lemmatization, model, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), POS, Sentence

Since a tweet is limited to 140 characters, it is ambiguous and difficult for
traditional Natural Language Processing (NLP) tools to analyse. This research
presents KeyXtract which enhances the machine learning based Stanford CoreNLP
Part-of-Speech (POS) tagger with the Twitter model to extract essential
keywords from a tweet. The system was developed using rule-based parsers and
two corpora. The data for the research was obtained from a Twitter profile of a
telecommunication company. The system development consisted of two stages. At
the initial stage, a domain specific corpus was compiled after analysing the
tweets. The POS tagger extracted the Noun Phrases and Verb Phrases while the
parsers removed noise and extracted any other keywords missed by the POS
tagger. The system was evaluated using the Turing Test. After it was tested and
compared against Stanford CoreNLP, the second stage of the system was developed
addressing the shortcomings of the first stage. It was enhanced using Named
Entity Recognition and Lemmatization. The second stage was also tested using
the Turing test and its pass rate increased from 50.00% to 83.33%. The
performance of the final system output was measured using the F1 score.
Stanford CoreNLP with the Twitter model had an average F1 of 0.69 while the
improved system had a F1 of 0.77. The accuracy of the system could be improved
by using a complete domain specific corpus. Since the system used linguistic
features of a sentence, it could be applied to other NLP tools.
Canonical Correlation Forests
Authors: Tom Rainforth, Frank Wood
Date: 2015-07-20 00:00:00
Tasks: General Classification, regression

We introduce canonical correlation forests (CCFs), a new decision tree
ensemble method for classification and regression. Individual canonical
correlation trees are binary decision trees with hyperplane splits based on
local canonical correlation coefficients calculated during training. Unlike
axis-aligned alternatives, the decision surfaces of CCFs are not restricted to
the coordinate system of the inputs features and therefore more naturally
represent data with correlated inputs. CCFs naturally accommodate multiple
outputs, provide a similar computational complexity to random forests, and
inherit their impressive robustness to the choice of input parameters. As part
of the CCF training algorithm, we also introduce projection bootstrapping, a
novel alternative to bagging for oblique decision tree ensembles which
maintains use of the full dataset in selecting split points, often leading to
improvements in predictive accuracy. Our experiments show that, even without
parameter tuning, CCFs out-perform axis-aligned random forests and other
state-of-the-art tree ensemble methods on both classification and regression
problems, delivering both improved predictive accuracy and faster training
times. We further show that they outperform all of the 179 classifiers
considered in a recent extensive survey.
How Do People Differ? A Social Media Approach
Authors: Vincent Wong, Yaneer Bar-Yam
Date: 2017-08-09 00:00:00
Tasks: Dimensionality Reduction

Research from a variety of fields including psychology and linguistics have
found correlations and patterns in personal attributes and behavior, but
efforts to understand the broader heterogeneity in human behavior have not yet
integrated these approaches and perspectives with a cohesive methodology. Here
we extract patterns in behavior and relate those patterns together in a
high-dimensional picture. We use dimension reduction to analyze word usage in
text data from the online discussion platform Reddit. We find that pronouns can
be used to characterize the space of the two most prominent dimensions that
capture the greatest differences in word usage, even though pronouns were not
included in the determination of those dimensions. These patterns overlap with
patterns of topics of discussion to reveal relationships between pronouns and
topics that can describe the user population. This analysis corroborates
findings from past research that have identified word use differences across
populations and synthesizes them relative to one another. We believe this is a
step toward understanding how differences between people are related to each
other.
An evaluation of large-scale methods for image instance and class discovery
Authors: Matthijs Douze, Hervé Jégou, Jeff Johnson
Date: 2017-08-09 00:00:00
Tasks: Clustering, GPU, Instance Search

This paper aims at discovering meaningful subsets of related images from
large image collections without annotations. We search groups of images related
at different levels of semantic, i.e., either instances or visual classes.
While k-means is usually considered as the gold standard for this task, we
evaluate and show the interest of diffusion methods that have been neglected by
the state of the art, such as the Markov Clustering algorithm.
  We report results on the ImageNet and the Paris500k instance dataset, both
enlarged with images from YFCC100M. We evaluate our methods with a labelling
cost that reflects how much effort a human would require to correct the
generated clusters.
  Our analysis highlights several properties. First, when powered with an
efficient GPU implementation, the cost of the discovery process is small
compared to computing the image descriptors, even for collections as large as
100 million images. Second, we show that descriptions selected for instance
search improve the discovery of object classes. Third, the Markov Clustering
technique consistently outperforms other methods; to our knowledge it has never
been considered in this large scale scenario.
Privacy Preserving Face Retrieval in the Cloud for Mobile Users
Authors: Xin Jin, Shiming Ge, Chenggen Song
Date: 2017-08-09 00:00:00
Tasks: Privacy Preserving, Retrieval

Recently, cloud storage and processing have been widely adopted. Mobile users
in one family or one team may automatically backup their photos to the same
shared cloud storage space. The powerful face detector trained and provided by
a 3rd party may be used to retrieve the photo collection which contains a
specific group of persons from the cloud storage server. However, the privacy
of the mobile users may be leaked to the cloud server providers. In the
meanwhile, the copyright of the face detector should be protected. Thus, in
this paper, we propose a protocol of privacy preserving face retrieval in the
cloud for mobile users, which protects the user photos and the face detector
simultaneously. The cloud server only provides the resources of storage and
computing and can not learn anything of the user photos and the face detector.
We test our protocol inside several families and classes. The experimental
results reveal that our protocol can successfully retrieve the proper photos
from the cloud server and protect the user photos and the face detector.
Simulated Annealing with Levy Distribution for Fast Matrix Factorization-Based Collaborative Filtering
Authors: Mostafa A. Shehata, Mohammad Nassef, Amr A. Badr
Date: 2017-08-09 00:00:00
Tasks: Collaborative Filtering

Matrix factorization is one of the best approaches for collaborative
filtering, because of its high accuracy in presenting users and items latent
factors. The main disadvantages of matrix factorization are its complexity, and
being very hard to be parallelized, specially with very large matrices. In this
paper, we introduce a new method for collaborative filtering based on Matrix
Factorization by combining simulated annealing with levy distribution. By using
this method, good solutions are achieved in acceptable time with low
computations, compared to other methods like stochastic gradient descent,
alternating least squares, and weighted non-negative matrix factorization.
CoupleNet: Coupling Global Structure with Local Parts for Object Detection
Authors: Yousong Zhu, Chaoyang Zhao, Jinqiao Wang, Xu Zhao, Yi Wu, Hanqing Lu
Date: 2017-08-09 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, object-detection, Object Detection, Position, Region Proposal

The region-based Convolutional Neural Network (CNN) detectors such as Faster
R-CNN or R-FCN have already shown promising results for object detection by
combining the region proposal subnetwork and the classification subnetwork
together. Although R-FCN has achieved higher detection speed while keeping the
detection performance, the global structure information is ignored by the
position-sensitive score maps. To fully explore the local and global
properties, in this paper, we propose a novel fully convolutional network,
named as CoupleNet, to couple the global structure with local parts for object
detection. Specifically, the object proposals obtained by the Region Proposal
Network (RPN) are fed into the the coupling module which consists of two
branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to
capture the local part information of the object, while the other employs the
RoI pooling to encode the global and context information. Next, we design
different coupling strategies and normalization ways to make full use of the
complementary advantages between the global and local branches. Extensive
experiments demonstrate the effectiveness of our approach. We achieve
state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7%
on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly
available.
WebVision Database: Visual Learning and Understanding from Web Data
Authors: Wen Li, Li-Min Wang, Wei Li, Eirikur Agustsson, Luc van Gool
Date: 2017-08-09 00:00:00
Tasks: Domain Adaptation

In this paper, we present a study on learning visual recognition models from
large scale noisy web data. We build a new database called WebVision, which
contains more than $2.4$ million web images crawled from the Internet by using
queries generated from the 1,000 semantic concepts of the benchmark ILSVRC 2012
dataset. Meta information along with those web images (e.g., title,
description, tags, etc.) are also crawled. A validation set and test set
containing human annotated images are also provided to facilitate algorithmic
development. Based on our new database, we obtain a few interesting
observations: 1) the noisy web images are sufficient for training a good deep
CNN model for visual recognition; 2) the model learnt from our WebVision
database exhibits comparable or even better generalization ability than the one
trained from the ILSVRC 2012 dataset when being transferred to new datasets and
tasks; 3) a domain adaptation issue (a.k.a., dataset bias) is observed, which
means the dataset can be used as the largest benchmark dataset for visual
domain adaptation. Our new WebVision database and relevant studies in this work
would benefit the advance of learning state-of-the-art visual models with
minimum supervision based on web data.
BubbleView: an interface for crowdsourcing image importance maps and tracking visual attention
Authors: Nam Wook Kim, Zoya Bylinskii, Michelle A. Borkin, Krzysztof Z. Gajos, Aude Oliva, Fredo Durand, Hanspeter Pfister
Date: 2017-02-16 00:00:00

In this paper, we present BubbleView, an alternative methodology for eye
tracking using discrete mouse clicks to measure which information people
consciously choose to examine. BubbleView is a mouse-contingent, moving-window
interface in which participants are presented with a series of blurred images
and click to reveal "bubbles" - small, circular areas of the image at original
resolution, similar to having a confined area of focus like the eye fovea.
Across 10 experiments with 28 different parameter combinations, we evaluated
BubbleView on a variety of image types: information visualizations, natural
images, static webpages, and graphic designs, and compared the clicks to eye
fixations collected with eye-trackers in controlled lab settings. We found that
BubbleView clicks can both (i) successfully approximate eye fixations on
different images, and (ii) be used to rank image and design elements by
importance. BubbleView is designed to collect clicks on static images, and
works best for defined tasks such as describing the content of an information
visualization or measuring image importance. BubbleView data is cleaner and
more consistent than related methodologies that use continuous mouse movements.
Our analyses validate the use of mouse-contingent, moving-window methodologies
as approximating eye fixations for different image and task types.
Measuring Inconsistency in Argument Graphs
Authors: Anthony Hunter
Date: 2017-08-09 00:00:00
Tasks: Abstract Argumentation

There have been a number of developments in measuring inconsistency in
logic-based representations of knowledge. In contrast, the development of
inconsistency measures for computational models of argument has been limited.
To address this shortcoming, this paper provides a general framework for
measuring inconsistency in abstract argumentation, together with some proposals
for specific measures, and a consideration of measuring inconsistency in
logic-based instantiations of argument graphs, including a review of some
existing proposals and a consideration of how existing logic-based measures of
inconsistency can be applied.