pwc-archive/papers-with-abstracts huggingface dataset:
Fusion of EEG and Musical Features in Continuous Music-emotion Recognition
Authors: Nattapong Thammasan, Ken-ichi Fukui, Masayuki Numao
Date: 2016-11-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Emotion Recognition, Music Emotion Recognition

Emotion estimation in music listening is confronting challenges to capture
the emotion variation of listeners. Recent years have witnessed attempts to
exploit multimodality fusing information from musical contents and
physiological signals captured from listeners to improve the performance of
emotion recognition. In this paper, we present a study of fusion of signals of
electroencephalogram (EEG), a tool to capture brainwaves at a high-temporal
resolution, and musical features at decision level in recognizing the
time-varying binary classes of arousal and valence. Our empirical results
showed that the fusion could outperform the performance of emotion recognition
using only EEG modality that was suffered from inter-subject variability, and
this suggested the promise of multimodal fusion in improving the accuracy of
music-emotion recognition.
Multiple object tracking with context awareness
Authors: Laura Leal-Taixé
Date: 2014-11-24 00:00:00
Tasks: Activity Recognition, Multiple Object Tracking, Multiple People Tracking, Object, Object Tracking

Multiple people tracking is a key problem for many applications such as
surveillance, animation or car navigation, and a key input for tasks such as
activity recognition. In crowded environments occlusions and false detections
are common, and although there have been substantial advances in recent years,
tracking is still a challenging task. Tracking is typically divided into two
steps: detection, i.e., locating the pedestrians in the image, and data
association, i.e., linking detections across frames to form complete
trajectories.
  For the data association task, approaches typically aim at developing new,
more complex formulations, which in turn put the focus on the optimization
techniques required to solve them. However, they still utilize very basic
information such as distance between detections. In this thesis, I focus on the
data association task and argue that there is contextual information that has
not been fully exploited yet in the tracking community, mainly social context
and spatial context coming from different views.
User Dependent Features in Online Signature Verification
Authors: D. S. Guru, K. S. Manjunatha, S. Manjunath
Date: 2016-11-30 00:00:00
Tasks: feature selection

In this paper, we propose a novel approach for verification of on-line
signatures based on user dependent feature selection and symbolic
representation. Unlike other signature verification methods, which work with
same features for all users, the proposed approach introduces the concept of
user dependent features. It exploits the typicality of each and every user to
select different features for different users. Initially all possible features
are extracted for all users and a method of feature selection is employed for
selecting user dependent features. The selected features are clustered using
Fuzzy C means algorithm. In order to preserve the intra-class variation within
each user, we recommend to represent each cluster in the form of an interval
valued symbolic feature vector. A method of signature verification based on the
proposed cluster based symbolic representation is also presented. Extensive
experimentations are conducted on MCYT-100 User (DB1) and MCYT-330 User (DB2)
online signature data sets to demonstrate the effectiveness of the proposed
novel approach.
System-Generated Requests for Rewriting Proposals
Authors: Pietro Speroni di Fenizio, Cyril Velikanov
Date: 2016-11-30 00:00:00

We present an online deliberation system using mutual evaluation in order to
collaboratively develop solutions. Participants submit their proposals and
evaluate each other's proposals; some of them may then be invited by the system
to rewrite 'problematic' proposals. Two cases are discussed: a proposal
supported by many, but not by a given person, who is then invited to rewrite it
for making yet more acceptable; and a poorly presented but presumably
interesting proposal. The first of these cases has been successfully
implemented. Proposals are evaluated along two axes-understandability (or
clarity, or, more generally, quality), and agreement. The latter is used by the
system to cluster proposals according to their ideas, while the former is used
both to present the best proposals on top of their clusters, and to find poorly
written proposals candidates for rewriting. These functionalities may be
considered as important components of a large scale online deliberation system.
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
Authors: Zifeng Wu, Chunhua Shen, Anton Van Den Hengel
Date: 2016-11-30 00:00:00
Tasks: Semantic Segmentation

The trend towards increasingly deep neural networks has been driven by a
general observation that increasing depth increases the performance of a
network. Recently, however, evidence has been amassing that simply increasing
depth may not be the best way to increase performance, particularly given other
limitations. Investigations into deep residual networks have also suggested
that they may not in fact be operating as a single deep network, but rather as
an ensemble of many relatively shallow networks. We examine these issues, and
in doing so arrive at a new interpretation of the unravelled view of deep
residual networks which explains some of the behaviours that have been observed
experimentally. As a result, we are able to derive a new, shallower,
architecture of residual networks which significantly outperforms much deeper
models such as ResNet-200 on the ImageNet classification dataset. We also show
that this performance is transferable to other problem domains by developing a
semantic segmentation approach which outperforms the state-of-the-art by a
remarkable margin on datasets including PASCAL VOC, PASCAL Context, and
Cityscapes. The architecture that we propose thus outperforms its comparators,
including very deep ResNets, and yet is more efficient in memory use and
sometimes also in training time. The code and models are available at
https://github.com/itijyou/ademxapp
Subsampled online matrix factorization with convergence guarantees
Authors: Arthur Mensch, Julien Mairal, Gaël Varoquaux, Bertrand Thirion
Date: 2016-11-30 00:00:00

We present a matrix factorization algorithm that scales to input matrices
that are large in both dimensions (i.e., that contains morethan 1TB of data).
The algorithm streams the matrix columns while subsampling them, resulting in
low complexity per iteration andreasonable memory footprint. In contrast to
previous online matrix factorization methods, our approach relies on
low-dimensional statistics from past iterates to control the extra variance
introduced by subsampling. We present a convergence analysis that guarantees us
to reach a stationary point of the problem. Large speed-ups can be obtained
compared to previous online algorithms that do not perform subsampling, thanks
to the feature redundancy that often exists in high-dimensional settings.
Towards Accurate Word Segmentation for Chinese Patents
Authors: Si Li, Nianwen Xue
Date: 2016-11-30 00:00:00
Tasks: Chinese Word Segmentation, Domain Adaptation

A patent is a property right for an invention granted by the government to
the inventor. An invention is a solution to a specific technological problem.
So patents often have a high concentration of scientific and technical terms
that are rare in everyday language. The Chinese word segmentation model trained
on currently available everyday language data sets performs poorly because it
cannot effectively recognize these scientific and technical terms. In this
paper we describe a pragmatic approach to Chinese word segmentation on patents
where we train a character-based semi-supervised sequence labeling model by
extracting features from a manually segmented corpus of 142 patents, enhanced
with information extracted from the Chinese TreeBank. Experiments show that the
accuracy of our model reached 95.08% (F1 score) on a held-out test set and
96.59% on development set, compared with an F1 score of 91.48% on development
set if the model is trained on the Chinese TreeBank. We also experimented with
some existing domain adaptation techniques, the results show that the amount of
target domain data and the selected features impact the performance of the
domain adaptation techniques.
Comparison of the COG Defuzzification Technique and Its Variations to the GPA Index
Authors: Michael Gr. Voskoglou
Date: 2016-11-30 00:00:00

The Center of Gravity (COG) method is one of the most popular defuzzification
techniques of fuzzy mathematics. In earlier works the COG technique was
properly adapted to be used as an assessment model (RFAM)and several variations
of it (GRFAM, TFAM and TpFAM)were also constructed for the same purpose. In
this paper the outcomes of all these models are compared to the corresponding
outcomes of a traditional assessment method of the bi-valued logic, the Grade
Point Average (GPA) Index. Examples are also presented illustrating our
results.
Active Deep Learning for Classification of Hyperspectral Images
Authors: Peng Liu, HUI ZHANG, Kie B. Eom
Date: 2016-11-30 00:00:00
Tasks: Active Learning, Classification, Classification Of Hyperspectral Images, Deep Learning, Dictionary Learning, General Classification

Active deep learning classification of hyperspectral images is considered in
this paper. Deep learning has achieved success in many applications, but
good-quality labeled samples are needed to construct a deep learning network.
It is expensive getting good labeled samples in hyperspectral images for remote
sensing applications. An active learning algorithm based on a weighted
incremental dictionary learning is proposed for such applications. The proposed
algorithm selects training samples that maximize two selection criteria, namely
representative and uncertainty. This algorithm trains a deep network
efficiently by actively selecting training samples at each iteration. The
proposed algorithm is applied for the classification of hyperspectral images,
and compared with other classification algorithms employing active learning. It
is shown that the proposed algorithm is efficient and effective in classifying
hyperspectral images.
Auditing Black-box Models for Indirect Influence
Authors: Philip Adler, Casey Falk, Sorelle A. Friedler, Gabriel Rybeck, Carlos Scheidegger, Brandon Smith, Suresh Venkatasubramanian
Date: 2016-02-23 00:00:00
Tasks: Attribute, feature selection

Data-trained predictive models see widespread use, but for the most part they
are used as black boxes which output a prediction or score. It is therefore
hard to acquire a deeper understanding of model behavior, and in particular how
different features influence the model prediction. This is important when
interpreting the behavior of complex models, or asserting that certain
problematic attributes (like race or gender) are not unduly influencing
decisions.
  In this paper, we present a technique for auditing black-box models, which
lets us study the extent to which existing models take advantage of particular
features in the dataset, without knowing how the models work. Our work focuses
on the problem of indirect influence: how some features might indirectly
influence outcomes via other, related features. As a result, we can find
attribute influences even in cases where, upon further direct examination of
the model, the attribute is not referred to by the model at all.
  Our approach does not require the black-box model to be retrained. This is
important if (for example) the model is only accessible via an API, and
contrasts our work with other methods that investigate feature influence like
feature selection. We present experimental evidence for the effectiveness of
our procedure using a variety of publicly available datasets and models. We
also validate our procedure using techniques from interpretable learning and
feature selection, as well as against other black-box auditing procedures.
Fast Supervised Discrete Hashing and its Analysis
Authors: Gou Koutaki, Keiichiro Shirai, Mitsuru Ambai
Date: 2016-11-30 00:00:00
Tasks: Image Retrieval, Quantization, Retrieval

In this paper, we propose a learning-based supervised discrete hashing
method. Binary hashing is widely used for large-scale image retrieval as well
as video and document searches because the compact representation of binary
code is essential for data storage and reasonable for query searches using
bit-operations. The recently proposed Supervised Discrete Hashing (SDH)
efficiently solves mixed-integer programming problems by alternating
optimization and the Discrete Cyclic Coordinate descent (DCC) method. We show
that the SDH model can be simplified without performance degradation based on
some preliminary experiments; we call the approximate model for this the "Fast
SDH" (FSDH) model. We analyze the FSDH model and provide a mathematically exact
solution for it. In contrast to SDH, our model does not require an alternating
optimization algorithm and does not depend on initial values. FSDH is also
easier to implement than Iterative Quantization (ITQ). Experimental results
involving a large-scale database showed that FSDH outperforms conventional SDH
in terms of precision, recall, and computation time.
Deep Cuboid Detection: Beyond 2D Bounding Boxes
Authors: Debidatta Dwibedi, Tomasz Malisiewicz, Vijay Badrinarayanan, Andrew Rabinovich
Date: 2016-11-30 00:00:00
Tasks: Deep Learning

We present a Deep Cuboid Detector which takes a consumer-quality RGB image of
a cluttered scene and localizes all 3D cuboids (box-like objects). Contrary to
classical approaches which fit a 3D model from low-level cues like corners,
edges, and vanishing points, we propose an end-to-end deep learning system to
detect cuboids across many semantic categories (e.g., ovens, shipping boxes,
and furniture). We localize cuboids with a 2D bounding box, and simultaneously
localize the cuboid's corners, effectively producing a 3D interpretation of
box-like objects. We refine keypoints by pooling convolutional features
iteratively, improving the baseline method significantly. Our deep learning
cuboid detector is trained in an end-to-end fashion and is suitable for
real-time applications in augmented reality (AR) and robotics.
Predicting Motivations of Actions by Leveraging Text
Authors: Carl Vondrick, Deniz Oktay, Hamed Pirsiavash, Antonio Torralba
Date: 2014-06-20 00:00:00
Proceeding: CVPR 2016 6

Understanding human actions is a key problem in computer vision. However,
recognizing actions is only the first step of understanding what a person is
doing. In this paper, we introduce the problem of predicting why a person has
performed an action in images. This problem has many applications in human
activity understanding, such as anticipating or explaining an action. To study
this problem, we introduce a new dataset of people performing actions annotated
with likely motivations. However, the information in an image alone may not be
sufficient to automatically solve this task. Since humans can rely on their
lifetime of experiences to infer motivation, we propose to give computer vision
systems access to some of these experiences by using recently developed natural
language models to mine knowledge stored in massive amounts of text. While we
are still far away from fully understanding motivation, our results suggest
that transferring knowledge from language into vision can help machines
understand why people in images might be performing an action.
Anticipating Visual Representations from Unlabeled Video
Authors: Carl Vondrick, Hamed Pirsiavash, Antonio Torralba
Date: 2015-04-29 00:00:00
Proceeding: CVPR 2016 6

Anticipating actions and objects before they start or appear is a difficult
problem in computer vision with several real-world applications. This task is
challenging partly because it requires leveraging extensive knowledge of the
world that is difficult to write down. We believe that a promising resource for
efficiently learning this knowledge is through readily available unlabeled
video. We present a framework that capitalizes on temporal structure in
unlabeled video to learn to anticipate human actions and objects. The key idea
behind our approach is that we can train deep networks to predict the visual
representation of images in the future. Visual representations are a promising
prediction target because they encode images at a higher semantic level than
pixels yet are automatic to compute. We then apply recognition algorithms on
our predicted representation to anticipate objects and actions. We
experimentally validate this idea on two datasets, anticipating actions one
second in the future and objects five seconds in the future.
Modeling Relationships in Referential Expressions with Compositional Modular Networks
Authors: Ronghang Hu, Marcus Rohrbach, Jacob Andreas, Trevor Darrell, Kate Saenko
Date: 2016-11-30 00:00:00
Proceeding: CVPR 2017 7
Tasks: Visual Question Answering (VQA)

People often refer to entities in an image in terms of their relationships
with other entities. For example, "the black cat sitting under the table"
refers to both a "black cat" entity and its relationship with another "table"
entity. Understanding these relationships is essential for interpreting and
grounding such natural language expressions. Most prior work focuses on either
grounding entire referential expressions holistically to one region, or
localizing relationships based on a fixed set of categories. In this paper we
instead present a modular deep architecture capable of analyzing referential
expressions into their component parts, identifying entities and relationships
mentioned in the input expression and grounding them all in the scene. We call
this approach Compositional Modular Networks (CMNs): a novel architecture that
learns linguistic analysis and visual inference end-to-end. Our approach is
built around two types of neural modules that inspect local regions and
pairwise interactions between regions. We evaluate CMNs on multiple referential
expression datasets, outperforming state-of-the-art approaches on all tasks.
The Upper Bound on Knots in Neural Networks
Authors: Kevin K. Chen
Date: 2016-11-29 00:00:00

Neural networks with rectified linear unit activations are essentially
multivariate linear splines. As such, one of many ways to measure the
"complexity" or "expressivity" of a neural network is to count the number of
knots in the spline model. We study the number of knots in fully-connected
feedforward neural networks with rectified linear unit activation functions. We
intentionally keep the neural networks very simple, so as to make theoretical
analyses more approachable. An induction on the number of layers $l$ reveals a
tight upper bound on the number of knots in $\mathbb{R} \to \mathbb{R}^p$ deep
neural networks. With $n_i \gg 1$ neurons in layer $i = 1, \dots, l$, the upper
bound is approximately $n_1 \dots n_l$. We then show that the exact upper bound
is tight, and we demonstrate the upper bound with an example. The purpose of
these analyses is to pave a path for understanding the behavior of general
$\mathbb{R}^q \to \mathbb{R}^p$ neural networks.
Sequential Person Recognition in Photo Albums with a Recurrent Network
Authors: Yao Li, Guosheng Lin, Bohan Zhuang, Lingqiao Liu, Chunhua Shen, Anton Van Den Hengel
Date: 2016-11-30 00:00:00
Proceeding: CVPR 2017 7
Tasks: Person Recognition

Recognizing the identities of people in everyday photos is still a very
challenging problem for machine vision, due to non-frontal faces, changes in
clothing, location, lighting and similar. Recent studies have shown that rich
relational information between people in the same photo can help in recognizing
their identities. In this work, we propose to model the relational information
between people as a sequence prediction task. At the core of our work is a
novel recurrent network architecture, in which relational information between
instances' labels and appearance are modeled jointly. In addition to relational
cues, scene context is incorporated in our sequence prediction model with no
additional cost. In this sense, our approach is a unified framework for
modeling both contextual cues and visual appearance of person instances. Our
model is trained end-to-end with a sequence of annotated instances in a photo
as inputs, and a sequence of corresponding labels as targets. We demonstrate
that this simple but elegant formulation achieves state-of-the-art performance
on the newly released People In Photo Albums (PIPA) dataset.
Low-rank Bilinear Pooling for Fine-Grained Classification
Authors: Shu Kong, Charless Fowlkes
Date: 2016-11-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Classification, General Classification

Pooling second-order local feature statistics to form a high-dimensional
bilinear feature has been shown to achieve state-of-the-art performance on a
variety of fine-grained classification tasks. To address the computational
demands of high feature dimensionality, we propose to represent the covariance
features as a matrix and apply a low-rank bilinear classifier. The resulting
classifier can be evaluated without explicitly computing the bilinear feature
map which allows for a large reduction in the compute time as well as
decreasing the effective number of parameters to be learned.
  To further compress the model, we propose classifier co-decomposition that
factorizes the collection of bilinear classifiers into a common factor and
compact per-class terms. The co-decomposition idea can be deployed through two
convolutional layers and trained in an end-to-end architecture. We suggest a
simple yet effective initialization that avoids explicitly first training and
factorizing the larger bilinear classifiers. Through extensive experiments, we
show that our model achieves state-of-the-art performance on several public
datasets for fine-grained classification trained with only category labels.
Importantly, our final model is an order of magnitude smaller than the recently
proposed compact bilinear model, and three orders smaller than the standard
bilinear CNN model.
Semantic Facial Expression Editing using Autoencoded Flow
Authors: Raymond Yeh, Ziwei Liu, Dan B. Goldman, Aseem Agarwala
Date: 2016-11-30 00:00:00

High-level manipulation of facial expressions in images --- such as changing
a smile to a neutral expression --- is challenging because facial expression
changes are highly non-linear, and vary depending on the appearance of the
face. We present a fully automatic approach to editing faces that combines the
advantages of flow-based face manipulation with the more recent generative
capabilities of Variational Autoencoders (VAEs). During training, our model
learns to encode the flow from one expression to another over a low-dimensional
latent space. At test time, expression editing can be done simply using latent
vector arithmetic. We evaluate our methods on two applications: 1) single-image
facial expression editing, and 2) facial expression interpolation between two
images. We demonstrate that our method generates images of higher perceptual
quality than previous VAE and flow-based methods.