pwc-archive/papers-with-abstracts huggingface dataset:
Cascaded Continuous Regression for Real-time Incremental Face Tracking
Authors: Enrique Sánchez-Lozano, Brais Martinez, Georgios Tzimiropoulos, Michel Valstar
Date: 2016-08-03 00:00:00
Tasks: Face Alignment, Incremental Learning, Landmark Tracking, regression

This paper introduces a novel real-time algorithm for facial landmark
tracking. Compared to detection, tracking has both additional challenges and
opportunities. Arguably the most important aspect in this domain is updating a
tracker's models as tracking progresses, also known as incremental (face)
tracking. While this should result in more accurate localisation, how to do
this online and in real time without causing a tracker to drift is still an
important open research question. We address this question in the cascaded
regression framework, the state-of-the-art approach for facial landmark
localisation. Because incremental learning for cascaded regression is costly,
we propose a much more efficient yet equally accurate alternative using
continuous regression. More specifically, we first propose cascaded continuous
regression (CCR) and show its accuracy is equivalent to the Supervised Descent
Method. We then derive the incremental learning updates for CCR (iCCR) and show
that it is an order of magnitude faster than standard incremental learning for
cascaded regression, bringing the time required for the update from seconds
down to a fraction of a second, thus enabling real-time tracking. Finally, we
evaluate iCCR and show the importance of incremental learning in achieving
state-of-the-art performance. Code for our iCCR is available from
http://www.cs.nott.ac.uk/~psxes1
Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation
Authors: Alexander Kolesnikov, Christoph H. Lampert
Date: 2016-03-19 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

We introduce a new loss function for the weakly-supervised training of
semantic image segmentation models based on three guiding principles: to seed
with weak localization cues, to expand objects based on the information about
which classes can occur in an image, and to constrain the segmentations to
coincide with object boundaries. We show experimentally that training a deep
convolutional neural network using the proposed loss function leads to
substantially better segmentations than previous state-of-the-art methods on
the challenging PASCAL VOC 2012 dataset. We furthermore give insight into the
working mechanism of our method by a detailed experimental study that
illustrates how the segmentation quality is affected by each term of the
proposed loss function as well as their combinations.
Modeling Coverage for Neural Machine Translation
Authors: Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, Hang Li
Date: 2016-01-19 00:00:00
Proceeding: ACL 2016 8
Tasks: Machine Translation, NMT, Translation

Attention mechanism has enhanced state-of-the-art Neural Machine Translation
(NMT) by jointly learning to align and translate. It tends to ignore past
alignment information, however, which often leads to over-translation and
under-translation. To address this problem, we propose coverage-based NMT in
this paper. We maintain a coverage vector to keep track of the attention
history. The coverage vector is fed to the attention model to help adjust
future attention, which lets NMT system to consider more about untranslated
source words. Experiments show that the proposed approach significantly
improves both translation quality and alignment quality over standard
attention-based NMT.
Spoofing 2D Face Detection: Machines See People Who Aren't There
Authors: Michael McCoyd, David Wagner
Date: 2016-08-06 00:00:00
Tasks: BIG-bench Machine Learning, Face Detection

Machine learning is increasingly used to make sense of the physical world yet
may suffer from adversarial manipulation. We examine the Viola-Jones 2D face
detection algorithm to study whether images can be created that humans do not
notice as faces yet the algorithm detects as faces. We show that it is possible
to construct images that Viola-Jones recognizes as containing faces yet no
human would consider a face. Moreover, we show that it is possible to construct
images that fool facial detection even when they are printed and then
photographed.
How Much Did it Rain? Predicting Real Rainfall Totals Based on Radar Data
Authors: Adam Lesnikowski
Date: 2016-08-06 00:00:00
Tasks: BIG-bench Machine Learning

We applied a variety of parametric and non-parametric machine learning models
to predict the probability distribution of rainfall based on 1M training
examples over a single year across several U.S. states. Our top performing
model based on a squared loss objective was a cross-validated parametric
k-nearest-neighbor predictor that took about six days to compute, and was
competitive in a world-wide competition.
Effective Data Mining Technique for Classification Cancers via Mutations in Gene using Neural Network
Authors: Ayad Ghany Ismaeel, Dina Yousif Mikhail
Date: 2016-08-06 00:00:00
Tasks: Diagnostic, General Classification

The prediction plays the important role in detecting efficient protection and
therapy of cancer. The prediction of mutations in gene needs a diagnostic and
classification, which is based on the whole database (big dataset), to reach
sufficient accuracy results. Since the tumor suppressor P53 is approximately
about fifty percentage of all human tumors because mutations that occur in the
TP53 gene into the cells. So, this paper is applied on tumor p53, where the
problem is there are several primitive databases (excel database) contain
datasets of TP53 gene with its tumor protein p53, these databases are rich
datasets that cover all mutations and cause diseases (cancers). But these Data
Bases cannot reach to predict and diagnosis cancers, i.e. the big datasets have
not efficient Data Mining method, which can predict, diagnosis the mutation,
and classify the cancer of patient. The goal of this paper to reach a Data
Mining technique, that employs neural network, which bases on the big datasets.
Also, offers friendly predictions, flexible, and effective classified cancers,
in order to overcome the previous techniques drawbacks. This proposed technique
is done by using two approaches, first, bioinformatics techniques by using
BLAST, CLUSTALW, etc, in order to know if there are malignant mutations or not.
The second, data mining by using neural network; it is selected (12) out of
(53) TP53 gene database fields. To clarify, one of these 12 fields (gene
location field) did not exists in TP53 gene database; therefore, it is added to
the database of TP53 gene in training and testing back propagation algorithm,
in order to classify specifically the types of cancers. Feed Forward Back
Propagation supports this Data Mining method with data training rate (1) and
Mean Square Error (MSE) (0.00000000000001). This effective technique allows in
a quick, accurate and easy way to classify the type of cancer.
Desiderata for Vector-Space Word Representations
Authors: Leon Derczynski
Date: 2016-08-06 00:00:00

A plethora of vector-space representations for words is currently available,
which is growing. These consist of fixed-length vectors containing real values,
which represent a word. The result is a representation upon which the power of
many conventional information processing and data mining techniques can be
brought to bear, as long as the representations are designed with some
forethought and fit certain constraints. This paper details desiderata for the
design of vector space representations of words.
Intra-layer Nonuniform Quantization for Deep Convolutional Neural Network
Authors: Fangxuan Sun, Jun Lin, Zhongfeng Wang
Date: 2016-07-10 00:00:00
Tasks: Clustering, General Classification, object-detection, Object Detection, Quantization, speech-recognition, Speech Recognition

Deep convolutional neural network (DCNN) has achieved remarkable performance
on object detection and speech recognition in recent years. However, the
excellent performance of a DCNN incurs high computational complexity and large
memory requirement. In this paper, an equal distance nonuniform quantization
(ENQ) scheme and a K-means clustering nonuniform quantization (KNQ) scheme are
proposed to reduce the required memory storage when low complexity hardware or
software implementations are considered. For the VGG-16 and the AlexNet, the
proposed nonuniform quantization schemes reduce the number of required memory
storage by approximately 50\% while achieving almost the same or even better
classification accuracy compared to the state-of-the-art quantization method.
Compared to the ENQ scheme, the proposed KNQ scheme provides a better tradeoff
when higher accuracy is required.
Transferring Knowledge from Text to Predict Disease Onset
Authors: Yun Liu, Kun-Ta Chuang, Fu-Wen Liang, Huey-Jen Su, Collin M. Stultz, John V. Guttag
Date: 2016-08-06 00:00:00

In many domains such as medicine, training data is in short supply. In such
cases, external knowledge is often helpful in building predictive models. We
propose a novel method to incorporate publicly available domain expertise to
build accurate models. Specifically, we use word2vec models trained on a
domain-specific corpus to estimate the relevance of each feature's text
description to the prediction problem. We use these relevance estimates to
rescale the features, causing more important features to experience weaker
regularization.
  We apply our method to predict the onset of five chronic diseases in the next
five years in two genders and two age groups. Our rescaling approach improves
the accuracy of the model, particularly when there are few positive examples.
Furthermore, our method selects 60% fewer features, easing interpretation by
physicians. Our method is applicable to other domains where feature and outcome
descriptions are available.
Neutro-Connectedness Cut
Authors: Min Xian, Yingtao Zhang, H. D. Cheng, Fei Xu, Jianrui Ding
Date: 2015-12-19 00:00:00
Tasks: Image Segmentation, Interactive Segmentation, Segmentation, Semantic Segmentation

Interactive image segmentation is a challenging task and receives increasing
attention recently; however, two major drawbacks exist in interactive
segmentation approaches. First, the segmentation performance of ROI-based
methods is sensitive to the initial ROI: different ROIs may produce results
with great difference. Second, most seed-based methods need intense
interactions, and are not applicable in many cases. In this work, we generalize
the Neutro-Connectedness (NC) to be independent of top-down priors of objects
and to model image topology with indeterminacy measurement on image regions,
propose a novel method for determining object and background regions, which is
applied to exclude isolated background regions and enforce label consistency,
and put forward a hybrid interactive segmentation method, Neutro-Connectedness
Cut (NC-Cut), which can overcome the above two problems by utilizing both
pixel-wise appearance information and region-based NC properties. We evaluate
the proposed NC-Cut by employing two image datasets (265 images), and
demonstrate that the proposed approach outperforms state-of-the-art interactive
image segmentation methods (Grabcut, MILCut, One-Cut, MGC_max^sum and pPBC).
Weighted diffusion LMP algorithm for distributed estimation in non-uniform noise conditions
Authors: H. Zayyani, M. Korki
Date: 2016-08-06 00:00:00

This letter presents an improved version of diffusion least mean ppower (LMP)
algorithm for distributed estimation. Instead of sum of mean square errors, a
weighted sum of mean square error is defined as the cost function for global
and local cost functions of a network of sensors. The weight coefficients are
updated by a simple steepest-descent recursion to minimize the error signal of
the global and local adaptive algorithm. Simulation results show the advantages
of the proposed weighted diffusion LMP over the diffusion LMP algorithm
specially in the non-uniform noise conditions in a sensor network.
Signs in time: Encoding human motion as a temporal image
Authors: Joon Son Chung, Andrew Zisserman
Date: 2016-08-06 00:00:00
Tasks: Time Series, Time Series Analysis

The goal of this work is to recognise and localise short temporal signals in
image time series, where strong supervision is not available for training.
  To this end we propose an image encoding that concisely represents human
motion in a video sequence in a form that is suitable for learning with a
ConvNet. The encoding reduces the pose information from an image to a single
column, dramatically diminishing the input requirements for the network, but
retaining the essential information for recognition.
  The encoding is applied to the task of recognizing and localizing signed
gestures in British Sign Language (BSL) videos. We demonstrate that using the
proposed encoding, signs as short as 10 frames duration can be learnt from
clips lasting hundreds of frames using only weak (clip level) supervision and
with considerable label noise.
Multi-Model Hypothesize-and-Verify Approach for Incremental Loop Closure Verification
Authors: Kanji Tanaka
Date: 2016-08-06 00:00:00
Tasks: Loop Closure Detection, Robot Navigation, Visual Odometry, Visual Place Recognition

Loop closure detection, which is the task of identifying locations revisited
by a robot in a sequence of odometry and perceptual observations, is typically
formulated as a visual place recognition (VPR) task. However, even
state-of-the-art VPR techniques generate a considerable number of false
positives as a result of confusing visual features and perceptual aliasing. In
this paper, we propose a robust incremental framework for loop closure
detection, termed incremental loop closure verification. Our approach
reformulates the problem of loop closure detection as an instance of a
multi-model hypothesize-and-verify framework, in which multiple loop closure
hypotheses are generated and verified in terms of the consistency between loop
closure hypotheses and VPR constraints at multiple viewpoints along the robot's
trajectory. Furthermore, we consider the general incremental setting of loop
closure detection, in which the system must update both the set of VPR
constraints and that of loop closure hypotheses when new constraints or
hypotheses arrive during robot navigation. Experimental results using a stereo
SLAM system and DCNN features and visual odometry validate effectiveness of the
proposed approach.
Compressive Change Retrieval for Moving Object Detection
Authors: Tomoya Murase, Kanji Tanaka
Date: 2016-08-06 00:00:00
Tasks: Anomaly Detection, Autonomous Driving, Change Detection, Moving Object Detection, Object, object-detection, Object Detection, Retrieval

Change detection, or anomaly detection, from street-view images acquired by
an autonomous robot at multiple different times, is a major problem in robotic
mapping and autonomous driving. Formulation as an image comparison task, which
operates on a given pair of query and reference images is common to many
existing approaches to this problem. Unfortunately, providing relevant
reference images is not straightforward. In this paper, we propose a novel
formulation for change detection, termed compressive change retrieval, which
can operate on a query image and similar reference images retrieved from the
web. Compared to previous formulations, there are two sources of difficulty.
First, the retrieved reference images may frequently contain non-relevant
reference images, because even state-of-the-art place-recognition techniques
suffer from retrieval noise. Second, image comparison needs to be conducted in
a compressed domain to minimize the storage cost of large collections of
street-view images. To address the above issues, we also present a practical
change detection algorithm that uses compressed bag-of-words (BoW) image
representation as a scalable solution. The results of experiments conducted on
a practical change detection task, "moving object detection (MOD)," using the
publicly available Malaga dataset validate the effectiveness of the proposed
approach.
Look-ahead before you leap: end-to-end active recognition by forecasting the effect of motion
Authors: Dinesh Jayaraman, Kristen Grauman
Date: 2016-04-30 00:00:00

Visual recognition systems mounted on autonomous moving agents face the
challenge of unconstrained data, but simultaneously have the opportunity to
improve their performance by moving to acquire new views of test data. In this
work, we first show how a recurrent neural network-based system may be trained
to perform end-to-end learning of motion policies suited for this "active
recognition" setting. Further, we hypothesize that active vision requires an
agent to have the capacity to reason about the effects of its motions on its
view of the world. To verify this hypothesis, we attempt to induce this
capacity in our active recognition pipeline, by simultaneously learning to
forecast the effects of the agent's motions on its internal representation of
the environment conditional on all past views. Results across two challenging
datasets confirm both that our end-to-end system successfully learns meaningful
policies for active category recognition, and that "learning to look ahead"
further boosts recognition performance.
Zipf's law is a consequence of coherent language production
Authors: Jake Ryland Williams, James P. Bagrow, Andrew J. Reagan, Sharon E. Alajajian, Christopher M. Danforth, Peter Sheridan Dodds
Date: 2016-01-29 00:00:00
Tasks: Segmentation, Text Generation, Text Segmentation

The task of text segmentation may be undertaken at many levels in text
analysis---paragraphs, sentences, words, or even letters. Here, we focus on a
relatively fine scale of segmentation, hypothesizing it to be in accord with a
stochastic model of language generation, as the smallest scale where
independent units of meaning are produced. Our goals in this letter include the
development of methods for the segmentation of these minimal independent units,
which produce feature-representations of texts that align with the independence
assumption of the bag-of-terms model, commonly used for prediction and
classification in computational text analysis. We also propose the measurement
of texts' association (with respect to realized segmentations) to the model of
language generation. We find (1) that our segmentations of phrases exhibit much
better associations to the generation model than words and (2), that texts
which are well fit are generally topically homogeneous. Because our generative
model produces Zipf's law, our study further suggests that Zipf's law may be a
consequence of homogeneity in language production.
Photometric Bundle Adjustment for Vision-Based SLAM
Authors: Hatem Alismail, Brett Browning, Simon Lucey
Date: 2016-08-05 00:00:00

We propose a novel algorithm for the joint refinement of structure and motion
parameters from image data directly without relying on fixed and known
correspondences. In contrast to traditional bundle adjustment (BA) where the
optimal parameters are determined by minimizing the reprojection error using
tracked features, the proposed algorithm relies on maximizing the photometric
consistency and estimates the correspondences implicitly. Since the proposed
algorithm does not require correspondences, its application is not limited to
corner-like structure; any pixel with nonvanishing gradient could be used in
the estimation process. Furthermore, we demonstrate the feasibility of refining
the motion and structure parameters simultaneously using the photometric in
unconstrained scenes and without requiring restrictive assumptions such as
planarity. The proposed algorithm is evaluated on range of challenging outdoor
datasets, and it is shown to improve upon the accuracy of the state-of-the-art
VSLAM methods obtained using the minimization of the reprojection error using
traditional BA as well as loop closure.
The Future of Data Analysis in the Neurosciences
Authors: Danilo Bzdok, B. T. Thomas Yeo
Date: 2016-08-05 00:00:00

Neuroscience is undergoing faster changes than ever before. Over 100 years
our field qualitatively described and invasively manipulated single or few
organisms to gain anatomical, physiological, and pharmacological insights. In
the last 10 years neuroscience spawned quantitative big-sample datasets on
microanatomy, synaptic connections, optogenetic brain-behavior assays, and
high-level cognition. While growing data availability and information
granularity have been amply discussed, we direct attention to a routinely
neglected question: How will the unprecedented data richness shape data
analysis practices? Statistical reasoning is becoming more central to distill
neurobiological knowledge from healthy and pathological brain recordings. We
believe that large-scale data analysis will use more models that are
non-parametric, generative, mixing frequentist and Bayesian aspects, and
grounded in different statistical inferences.
Communication-Efficient Parallel Block Minimization for Kernel Machines
Authors: Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon
Date: 2016-08-05 00:00:00

Kernel machines often yield superior predictive performance on various tasks;
however, they suffer from severe computational challenges. In this paper, we
show how to overcome the important challenge of speeding up kernel machines. In
particular, we develop a parallel block minimization framework for solving
kernel machines, including kernel SVM and kernel logistic regression. Our
framework proceeds by dividing the problem into smaller subproblems by forming
a block-diagonal approximation of the Hessian matrix. The subproblems are then
solved approximately in parallel. After that, a communication efficient line
search procedure is developed to ensure sufficient reduction of the objective
function value at each iteration. We prove global linear convergence rate of
the proposed method with a wide class of subproblem solvers, and our analysis
covers strongly convex and some non-strongly convex functions. We apply our
algorithm to solve large-scale kernel SVM problems on distributed systems, and
show a significant improvement over existing parallel solvers. As an example,
on the covtype dataset with half-a-million samples, our algorithm can obtain an
approximate solution with 96% accuracy in 20 seconds using 32 machines, while
all the other parallel kernel SVM solvers require more than 2000 seconds to
achieve a solution with 95% accuracy. Moreover, our algorithm can scale to very
large data sets, such as the kdd algebra dataset with 8 million samples and 20
million features.