pwc-archive/papers-with-abstracts huggingface dataset:
Online Learning of Portfolio Ensembles with Sector Exposure Regularization
Authors: Guy Uziel, Ran El-Yaniv
Date: 2016-04-12 00:00:00

We consider online learning of ensembles of portfolio selection algorithms
and aim to regularize risk by encouraging diversification with respect to a
predefined risk-driven grouping of stocks. Our procedure uses online convex
optimization to control capital allocation to underlying investment algorithms
while encouraging non-sparsity over the given grouping. We prove a logarithmic
regret for this procedure with respect to the best-in-hindsight ensemble. We
applied the procedure with known mean-reversion portfolio selection algorithms
using the standard GICS industry sector grouping. Empirical Experimental
results showed an impressive percentage increase of risk-adjusted return
(Sharpe ratio).
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer
Authors: Jun Xie, Martin Kiefel, Ming-Ting Sun, Andreas Geiger
Date: 2015-11-10 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object Recognition, Scene Understanding, Semantic Segmentation

Semantic annotations are vital for training models for object recognition,
semantic segmentation or scene understanding. Unfortunately, pixelwise
annotation of images at very large scale is labor-intensive and only little
labeled data is available, particularly at instance level and for street
scenes. In this paper, we propose to tackle this problem by lifting the
semantic instance labeling task from 2D into 3D. Given reconstructions from
stereo or laser data, we annotate static 3D scene elements with rough bounding
primitives and develop a model which transfers this information into the image
domain. We leverage our method to obtain 2D labels for a novel suburban video
dataset which we have collected, resulting in 400k semantic and instance image
annotations. A comparison of our method to state-of-the-art label transfer
baselines reveals that 3D information enables more efficient annotation while
at the same time resulting in improved accuracy and time-coherent labels.
Attributes as Semantic Units between Natural Language and Visual Recognition
Authors: Marcus Rohrbach
Date: 2016-04-12 00:00:00
Tasks: Sentence

Impressive progress has been made in the fields of computer vision and
natural language processing. However, it remains a challenge to find the best
point of interaction for these very different modalities. In this chapter we
discuss how attributes allow us to exchange information between the two
modalities and in this way lead to an interaction on a semantic level.
Specifically we discuss how attributes allow using knowledge mined from
language resources for recognizing novel visual categories, how we can generate
sentence description about images and video, how we can ground natural language
in visual content, and finally, how we can answer natural language questions
about images.
The Univariate Flagging Algorithm (UFA): a Fully-Automated Approach for Identifying Optimal Thresholds in Data
Authors: Mallory Sheth, Roy Welsch, Natasha Markuzon
Date: 2016-04-12 00:00:00
Tasks: General Classification

In many data classification problems, there is no linear relationship between
an explanatory and the dependent variables. Instead, there may be ranges of the
input variable for which the observed outcome is signficantly more or less
likely. This paper describes an algorithm for automatic detection of such
thresholds, called the Univariate Flagging Algorithm (UFA). The algorithm
searches for a separation that optimizes the difference between separated areas
while providing the maximum support. We evaluate its performance using three
examples and demonstrate that thresholds identified by the algorithm align well
with visual inspection and subject matter expertise. We also introduce two
classification approaches that use UFA and show that the performance attained
on unseen test data is equal to or better than that of more traditional
classifiers. We demonstrate that the proposed algorithm is robust against
missing data and noise, is scalable, and is easy to interpret and visualize. It
is also well suited for problems where incidence of the target is low.
Thesis: Multiple Kernel Learning for Object Categorization
Authors: Dinesh Govindaraj
Date: 2016-04-12 00:00:00
Tasks: Object, Object Categorization

Object Categorization is a challenging problem, especially when the images
have clutter background, occlusions or different lighting conditions. In the
past, many descriptors have been proposed which aid object categorization even
in such adverse conditions. Each descriptor has its own merits and de-merits.
Some descriptors are invariant to transformations while the others are more
discriminative. Past research has shown that, employing multiple descriptors
rather than any single descriptor leads to better recognition. The problem of
learning the optimal combination of the available descriptors for a particular
classification task is studied. Multiple Kernel Learning (MKL) framework has
been developed for learning an optimal combination of descriptors for object
categorization. Existing MKL formulations often employ block l-1 norm
regularization which is equivalent to selecting a single kernel from a library
of kernels. Since essentially a single descriptor is selected, the existing
formulations maybe sub- optimal for object categorization. A MKL formulation
based on block l-infinity norm regularization has been developed, which chooses
an optimal combination of kernels as opposed to selecting a single kernel. A
Composite Multiple Kernel Learning(CKL) formulation based on mixed l-infinity
and l-1 norm regularization has been developed. These formulations end in
Second Order Cone Programs(SOCP). Other efficient alter- native algorithms for
these formulation have been implemented. Empirical results on benchmark
datasets show significant improvement using these new MKL formulations.
CRAFT Objects from Images
Authors: Bin Yang, Junjie Yan, Zhen Lei, Stan Z. Li
Date: 2016-04-12 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object, object-detection, Object Detection, Region Proposal

Object detection is a fundamental problem in image understanding. One popular
solution is the R-CNN framework and its fast versions. They decompose the
object detection problem into two cascaded easier tasks: 1) generating object
proposals from images, 2) classifying proposals into various object categories.
Despite that we are handling with two relatively easier tasks, they are not
solved perfectly and there's still room for improvement. In this paper, we push
the "divide and conquer" solution even further by dividing each task into two
sub-tasks. We call the proposed method "CRAFT" (Cascade Region-proposal-network
And FasT-rcnn), which tackles each task with a carefully designed network
cascade. We show that the cascade structure helps in both tasks: in proposal
generation, it provides more compact and better localized object proposals; in
object classification, it reduces false positives (mainly between ambiguous
categories) by capturing both inter- and intra-category variances. CRAFT
achieves consistent and considerable improvement over the state-of-the-art on
object detection benchmarks like PASCAL VOC 07/12 and ILSVRC.
Convolutional Pose Machines
Authors: Shih-En Wei, Varun Ramakrishna, Takeo Kanade, Yaser Sheikh
Date: 2016-01-30 00:00:00
Proceeding: CVPR 2016 6
Tasks: 3D Human Pose Estimation, Car Pose Estimation, Classification, Pose Estimation, Structured Prediction

Pose Machines provide a sequential prediction framework for learning rich
implicit spatial models. In this work we show a systematic design for how
convolutional networks can be incorporated into the pose machine framework for
learning image features and image-dependent spatial models for the task of pose
estimation. The contribution of this paper is to implicitly model long-range
dependencies between variables in structured prediction tasks such as
articulated pose estimation. We achieve this by designing a sequential
architecture composed of convolutional networks that directly operate on belief
maps from previous stages, producing increasingly refined estimates for part
locations, without the need for explicit graphical model-style inference. Our
approach addresses the characteristic difficulty of vanishing gradients during
training by providing a natural learning objective function that enforces
intermediate supervision, thereby replenishing back-propagated gradients and
conditioning the learning procedure. We demonstrate state-of-the-art
performance and outperform competing methods on standard benchmarks including
the MPII, LSP, and FLIC datasets.
Recurrent Attentional Networks for Saliency Detection
Authors: Jason Kuen, Zhenhua Wang, Gang Wang
Date: 2016-04-12 00:00:00
Proceeding: CVPR 2016 6
Tasks: Saliency Detection

Convolutional-deconvolution networks can be adopted to perform end-to-end
saliency detection. But, they do not work well with objects of multiple scales.
To overcome such a limitation, in this work, we propose a recurrent attentional
convolutional-deconvolution network (RACDNN). Using spatial transformer and
recurrent network units, RACDNN is able to iteratively attend to selected image
sub-regions to perform saliency refinement progressively. Besides tackling the
scale problem, RACDNN can also learn context-aware features from past
iterations to enhance saliency refinement in future iterations. Experiments on
several challenging saliency detection datasets validate the effectiveness of
RACDNN, and show that RACDNN outperforms state-of-the-art saliency detection
methods.
Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines
Authors: Deepak Ghimire, Joonwhoan Lee
Date: 2016-04-12 00:00:00
Tasks: Dynamic Time Warping, Facial Expression Recognition, Facial Expression Recognition (FER), Graph Matching, Landmark Tracking

Facial expressions are widely used in the behavioral interpretation of
emotions, cognitive science, and social interactions. In this paper, we present
a novel method for fully automatic facial expression recognition in facial
image sequences. As the facial expression evolves over time facial landmarks
are automatically tracked in consecutive video frames, using displacements
based on elastic bunch graph matching displacement estimation. Feature vectors
from individual landmarks, as well as pairs of landmarks tracking results are
extracted, and normalized, with respect to the first frame in the sequence. The
prototypical expression sequence for each class of facial expression is formed,
by taking the median of the landmark tracking results from the training facial
expression sequences. Multi-class AdaBoost with dynamic time warping similarity
distance between the feature vector of input facial expression and prototypical
facial expression, is used as a weak classifier to select the subset of
discriminative feature vectors. Finally, two methods for facial expression
recognition are presented, either by using multi-class AdaBoost with dynamic
time warping, or by using support vector machine on the boosted feature
vectors. The results on the Cohn-Kanade (CK+) facial expression database show a
recognition accuracy of 95.17% and 97.35% using multi-class AdaBoost and
support vector machines, respectively.
Disfluency Detection using a Bidirectional LSTM
Authors: Vicky Zayats, Mari Ostendorf, Hannaneh Hajishirzi
Date: 2016-04-12 00:00:00

We introduce a new approach for disfluency detection using a Bidirectional
Long-Short Term Memory neural network (BLSTM). In addition to the word
sequence, the model takes as input pattern match features that were developed
to reduce sensitivity to vocabulary size in training, which lead to improved
performance over the word sequence alone. The BLSTM takes advantage of explicit
repair states in addition to the standard reparandum states. The final output
leverages integer linear programming to incorporate constraints of disfluency
structure. In experiments on the Switchboard corpus, the model achieves
state-of-the-art performance for both the standard disfluency detection task
and the correction detection task. Analysis shows that the model has better
detection of non-repetition disfluencies, which tend to be much harder to
detect.
Compact Bilinear Pooling
Authors: Yang Gao, Oscar Beijbom, Ning Zhang, Trevor Darrell
Date: 2015-11-19 00:00:00
Proceeding: CVPR 2016 6
Tasks: Face Recognition, Few-Shot Learning, General Classification, image-classification, Image Classification, Semantic Segmentation

Bilinear models has been shown to achieve impressive performance on a wide
range of visual tasks, such as semantic segmentation, fine grained recognition
and face recognition. However, bilinear features are high dimensional,
typically on the order of hundreds of thousands to a few million, which makes
them impractical for subsequent analysis. We propose two compact bilinear
representations with the same discriminative power as the full bilinear
representation but with only a few thousand dimensions. Our compact
representations allow back-propagation of classification errors enabling an
end-to-end optimization of the visual recognition system. The compact bilinear
representations are derived through a novel kernelized analysis of bilinear
pooling which provide insights into the discriminative power of bilinear
pooling, and a platform for further research in compact pooling methods.
Experimentation illustrate the utility of the proposed representations for
image classification and few-shot learning across several datasets.
Efficient Classification of Multi-Labelled Text Streams by Clashing
Authors: Ricardo Ñanculef, Ilias Flaounas, Nello Cristianini
Date: 2016-04-12 00:00:00
Tasks: Classification, General Classification

We present a method for the classification of multi-labelled text documents
explicitly designed for data stream applications that require to process a
virtually infinite sequence of data using constant memory and constant
processing time. Our method is composed of an online procedure used to
efficiently map text into a low-dimensional feature space and a partition of
this space into a set of regions for which the system extracts and keeps
statistics used to predict multi-label text annotations. Documents are fed into
the system as a sequence of words, mapped to a region of the partition, and
annotated using the statistics computed from the labelled instances colliding
in the same region. This approach is referred to as clashing. We illustrate the
method in real-world text data, comparing the results with those obtained using
other text classifiers. In addition, we provide an analysis about the effect of
the representation space dimensionality on the predictive performance of the
system. Our results show that the online embedding indeed approximates the
geometry of the full corpus-wise TF and TF-IDF space. The model obtains
competitive F measures with respect to the most accurate methods, using
significantly fewer computational resources. In addition, the method achieves a
higher macro-averaged F measure than methods with similar running time.
Furthermore, the system is able to learn faster than the other methods from
partially labelled streams.
TGIF: A New Dataset and Benchmark on Animated GIF Description
Authors: Yuncheng Li, Yale Song, Liangliang Cao, Joel Tetreault, Larry Goldberg, Alejandro Jaimes, Jiebo Luo
Date: 2016-04-10 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Captioning, Machine Translation, Text Generation, Translation, Video Description

With the recent popularity of animated GIFs on social media, there is need
for ways to index them with rich metadata. To advance research on animated GIF
understanding, we collected a new dataset, Tumblr GIF (TGIF), with 100K
animated GIFs from Tumblr and 120K natural language descriptions obtained via
crowdsourcing. The motivation for this work is to develop a testbed for image
sequence description systems, where the task is to generate natural language
descriptions for animated GIFs or video clips. To ensure a high quality
dataset, we developed a series of novel quality controls to validate free-form
text input from crowdworkers. We show that there is unambiguous association
between visual content and natural language descriptions in our dataset, making
it an ideal benchmark for the visual content captioning task. We perform
extensive statistical analyses to compare our dataset to existing image and
video description datasets. Next, we provide baseline results on the animated
GIF description task, using three representative techniques: nearest neighbor,
statistical machine translation, and recurrent neural networks. Finally, we
show that models fine-tuned from our animated GIF description dataset can be
helpful for automatic movie description.
Multi-Type Activity Recognition in Robot-Centric Scenarios
Authors: Ilaria Gori, J. K. Aggarwal, Larry Matthies, Michael S. Ryoo
Date: 2015-07-09 00:00:00
Tasks: Activity Recognition, Vocal Bursts Type Prediction

Activity recognition is very useful in scenarios where robots interact with,
monitor or assist humans. In the past years many types of activities -- single
actions, two persons interactions or ego-centric activities, to name a few --
have been analyzed. Whereas traditional methods treat such types of activities
separately, an autonomous robot should be able to detect and recognize multiple
types of activities to effectively fulfill its tasks. We propose a method that
is intrinsically able to detect and recognize activities of different types
that happen in sequence or concurrently. We present a new unified descriptor,
called Relation History Image (RHI), which can be extracted from all the
activity types we are interested in. We then formulate an optimization
procedure to detect and recognize activities of different types. We apply our
approach to a new dataset recorded from a robot-centric perspective and
systematically evaluate its quality compared to multiple baselines. Finally, we
show the efficacy of the RHI descriptor on publicly available datasets
performing extensive comparisons.
Application of the Second-Order Statistics for Estimation of the Pure Spectra of Individual Components from the Visible Hyperspectral Images of Their Mixture
Authors: Sung-Ho Jong, Yong-U Ri, Kye-Ryong Sin
Date: 2016-04-12 00:00:00

The second-order statistics (SOS) can be applied in estimation of the pure
spectra of chemical components from the spectrum of their mixture, when SOS
seems to be good at estimation of spectral patterns, but their peak directions
are opposite in some cases. In this paper, one method for judgment of the peak
direction of the pure spectra was proposed, where the base line of the pure
spectra was drawn by using their histograms and the peak directions were chosen
so as to make all of the pure spectra located upwards over the base line.
Results of the SOS analysis on the visible hyperspectral images of the mixture
composed of two or three chemical components showed that the present method
offered the reasonable shape and direction of the pure spectra of its
components.
Deep Learning for Tactile Understanding From Visual and Haptic Data
Authors: Yang Gao, Lisa Anne Hendricks, Katherine J. Kuchenbecker, Trevor Darrell
Date: 2015-11-19 00:00:00
Tasks: Deep Learning

Robots which interact with the physical world will benefit from a
fine-grained tactile understanding of objects and surfaces. Additionally, for
certain tasks, robots may need to know the haptic properties of an object
before touching it. To enable better tactile understanding for robots, we
propose a method of classifying surfaces with haptic adjectives (e.g.,
compressible or smooth) from both visual and physical interaction data. Humans
typically combine visual predictions and feedback from physical interactions to
accurately predict haptic properties and interact with the world. Inspired by
this cognitive pattern, we propose and explore a purely visual haptic
prediction model. Purely visual models enable a robot to "feel" without
physical interaction. Furthermore, we demonstrate that using both visual and
physical interaction signals together yields more accurate haptic
classification. Our models take advantage of recent advances in deep neural
networks by employing a unified approach to learning features for physical
interaction and visual observations. Even though we employ little domain
specific knowledge, our model still achieves better results than methods based
on hand-designed features.
Stochastic And-Or Grammars: A Unified Framework and Logic Perspective
Authors: Kewei Tu
Date: 2015-06-02 00:00:00
Tasks: Relational Reasoning

Stochastic And-Or grammars (AOG) extend traditional stochastic grammars of
language to model other types of data such as images and events. In this paper
we propose a representation framework of stochastic AOGs that is agnostic to
the type of the data being modeled and thus unifies various domain-specific
AOGs. Many existing grammar formalisms and probabilistic models in natural
language processing, computer vision, and machine learning can be seen as
special cases of this framework. We also propose a domain-independent inference
algorithm of stochastic context-free AOGs and show its tractability under a
reasonable assumption. Furthermore, we provide two interpretations of
stochastic context-free AOGs as a subset of probabilistic logic, which connects
stochastic AOGs to the field of statistical relational learning and clarifies
their relation with a few existing statistical relational models.
Learning Simple Auctions
Authors: Jamie Morgenstern, Tim Roughgarden
Date: 2016-04-11 00:00:00

We present a general framework for proving polynomial sample complexity
bounds for the problem of learning from samples the best auction in a class of
"simple" auctions. Our framework captures all of the most prominent examples of
"simple" auctions, including anonymous and non-anonymous item and bundle
pricings, with either a single or multiple buyers. The technique we propose is
to break the analysis of auctions into two natural pieces. First, one shows
that the set of allocation rules have large amounts of structure; second,
fixing an allocation on a sample, one shows that the set of auctions agreeing
with this allocation on that sample have revenue functions with low
dimensionality. Our results effectively imply that whenever it's possible to
compute a near-optimal simple auction with a known prior, it is also possible
to compute such an auction with an unknown prior (given a polynomial number of
samples).
Affinity CNN: Learning Pixel-Centric Pairwise Relations for Figure/Ground Embedding
Authors: Michael Maire, Takuya Narihira, Stella X. Yu
Date: 2015-12-09 00:00:00
Proceeding: CVPR 2016 6
Tasks: Edge Detection, Image Segmentation, Segmentation, Semantic Segmentation

Spectral embedding provides a framework for solving perceptual organization
problems, including image segmentation and figure/ground organization. From an
affinity matrix describing pairwise relationships between pixels, it clusters
pixels into regions, and, using a complex-valued extension, orders pixels
according to layer. We train a convolutional neural network (CNN) to directly
predict the pairwise relationships that define this affinity matrix. Spectral
embedding then resolves these predictions into a globally-consistent
segmentation and figure/ground organization of the scene. Experiments
demonstrate significant benefit to this direct coupling compared to prior works
which use explicit intermediate stages, such as edge detection, on the pathway
from image to affinities. Our results suggest spectral embedding as a powerful
alternative to the conditional random field (CRF)-based globalization schemes
typically coupled to deep neural networks.