pwc-archive/papers-with-abstracts huggingface dataset:
Pyramid Vector Quantization for Deep Learning
Authors: Vincenzo Liguori
Date: 2017-04-10 00:00:00
Tasks: Deep Learning, Quantization

This paper explores the use of Pyramid Vector Quantization (PVQ) to reduce
the computational cost for a variety of neural networks (NNs) while, at the
same time, compressing the weights that describe them. This is based on the
fact that the dot product between an N dimensional vector of real numbers and
an N dimensional PVQ vector can be calculated with only additions and
subtractions and one multiplication. This is advantageous since tensor
products, commonly used in NNs, can be re-conduced to a dot product or a set of
dot products. Finally, it is stressed that any NN architecture that is based on
an operation that can be re-conduced to a dot product can benefit from the
techniques described here.
3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
Authors: Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao, Thomas Funkhouser
Date: 2016-03-27 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Reconstruction, Point Cloud Registration

Matching local geometric features on real-world depth images is a challenging
task due to the noisy, low-resolution, and incomplete nature of 3D scan data.
These difficulties limit the performance of current state-of-art methods, which
are typically based on histograms over geometric properties. In this paper, we
present 3DMatch, a data-driven model that learns a local volumetric patch
descriptor for establishing correspondences between partial 3D data. To amass
training data for our model, we propose a self-supervised feature learning
method that leverages the millions of correspondence labels found in existing
RGB-D reconstructions. Experiments show that our descriptor is not only able to
match local geometry in new scenes for reconstruction, but also generalize to
different tasks and spatial scales (e.g. instance-level object model alignment
for the Amazon Picking Challenge, and mesh surface correspondence). Results
show that 3DMatch consistently outperforms other state-of-the-art approaches by
a significant margin. Code, data, benchmarks, and pre-trained models are
available online at http://3dmatch.cs.princeton.edu
A Hybrid Supervised-unsupervised Method on Image Topic Visualization with Convolutional Neural Network and LDA
Authors: Kai Zhen, Mridul Birla, David Crandall, Bingjing Zhang, Judy Qiu
Date: 2017-03-15 00:00:00

Given the progress in image recognition with recent data driven paradigms,
it's still expensive to manually label a large training data to fit a
convolutional neural network (CNN) model. This paper proposes a hybrid
supervised-unsupervised method combining a pre-trained AlexNet with Latent
Dirichlet Allocation (LDA) to extract image topics from both an unlabeled
life-logging dataset and the COCO dataset. We generate the bag-of-words
representations of an egocentric dataset from the softmax layer of AlexNet and
use LDA to visualize the subject's living genre with duplicated images. We use
a subset of COCO on 4 categories as ground truth, and define consistent rate to
quantitatively analyze the performance of the method, it achieves 84% for
consistent rate on average comparing to 18.75% from a raw CNN model. The method
is capable of detecting false labels and multi-labels from COCO dataset. For
scalability test, parallelization experiments are conducted with Harp-LDA on a
Intel Knights Landing cluster: to extract 1,000 topic assignments for 241,035
COCO images, it takes 10 minutes with 60 threads.
Mixed Graphical Models for Causal Analysis of Multi-modal Variables
Authors: Andrew J Sedgewick, Joseph D. Ramsey, Peter Spirtes, Clark Glymour, Panayiotis V. Benos
Date: 2017-04-09 00:00:00
Tasks: feature selection, Graph Learning

Graphical causal models are an important tool for knowledge discovery because
they can represent both the causal relations between variables and the
multivariate probability distributions over the data. Once learned, causal
graphs can be used for classification, feature selection and hypothesis
generation, while revealing the underlying causal network structure and thus
allowing for arbitrary likelihood queries over the data. However, current
algorithms for learning sparse directed graphs are generally designed to handle
only one type of data (continuous-only or discrete-only), which limits their
applicability to a large class of multi-modal biological datasets that include
mixed type variables. To address this issue, we developed new methods that
modify and combine existing methods for finding undirected graphs with methods
for finding directed graphs. These hybrid methods are not only faster, but also
perform better than the directed graph estimation methods alone for a variety
of parameter settings and data set sizes. Here, we describe a new conditional
independence test for learning directed graphs over mixed data types and we
compare performances of different graph learning strategies on synthetic data.
Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crises
Authors: Dat Tien Nguyen, Firoj Alam, Ferda Ofli, Muhammad Imran
Date: 2017-04-09 00:00:00
Tasks: Humanitarian

The extensive use of social media platforms, especially during disasters,
creates unique opportunities for humanitarian organizations to gain situational
awareness and launch relief operations accordingly. In addition to the textual
content, people post overwhelming amounts of imagery data on social networks
within minutes of a disaster hit. Studies point to the importance of this
online imagery content for emergency response. Despite recent advances in the
computer vision field, automatic processing of the crisis-related social media
imagery data remains a challenging task. It is because a majority of which
consists of redundant and irrelevant content. In this paper, we present an
image processing pipeline that comprises de-duplication and relevancy filtering
mechanisms to collect and filter social media image content in real-time during
a crisis event. Results obtained from extensive experiments on real-world
crisis datasets demonstrate the significance of the proposed pipeline for
optimal utilization of both human and machine computing resources.
MLC Toolbox: A MATLAB/OCTAVE Library for Multi-Label Classification
Authors: Keigo Kimura, Lu Sun, Mineichi Kudo
Date: 2017-04-09 00:00:00
Tasks: Classification, Clustering, Dimensionality Reduction, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Multi-Label Classification toolbox is a MATLAB/OCTAVE library for Multi-Label
Classification (MLC). There exists a few Java libraries for MLC, but no
MATLAB/OCTAVE library that covers various methods. This toolbox offers an
environment for evaluation, comparison and visualization of the MLC results.
One attraction of this toolbox is that it enables us to try many combinations
of feature space dimension reduction, sample clustering, label space dimension
reduction and ensemble, etc.
A Comparison of Word Embeddings for English and Cross-Lingual Chinese Word Sense Disambiguation
Authors: Hong Jin Kang, Tao Chen, Muthu Kumar Chandrasekaran, Min-Yen Kan
Date: 2016-11-09 00:00:00
Proceeding: WS 2016 12
Tasks: Benchmarking, Translation, Word Embeddings, Word Sense Disambiguation

Word embeddings are now ubiquitous forms of word representation in natural
language processing. There have been applications of word embeddings for
monolingual word sense disambiguation (WSD) in English, but few comparisons
have been done. This paper attempts to bridge that gap by examining popular
embeddings for the task of monolingual English WSD. Our simplified method leads
to comparable state-of-the-art performance without expensive retraining.
Cross-Lingual WSD - where the word senses of a word in a source language e come
from a separate target translation language f - can also assist in language
learning; for example, when providing translations of target vocabulary for
learners. Thus we have also applied word embeddings to the novel task of
cross-lingual WSD for Chinese and provide a public dataset for further
benchmarking. We have also experimented with using word embeddings for LSTM
networks and found surprisingly that a basic LSTM network does not work well.
We discuss the ramifications of this outcome.
Richer Convolutional Features for Edge Detection
Authors: Yun Liu, Ming-Ming Cheng, Xiao-Wei Hu, Kai Wang, Xiang Bai
Date: 2016-12-07 00:00:00
Proceeding: CVPR 2017 7
Tasks: Edge Detection

In this paper, we propose an accurate edge detector using richer convolutional features (RCF). Since objects in nature images have various scales and aspect ratios, the automatically learned rich hierarchical representations by CNNs are very critical and effective to detect edges and object boundaries. And the convolutional features gradually become coarser with receptive fields increasing. Based on these observations, our proposed network architecture makes full use of multiscale and multi-level information to perform the image-to-image edge prediction by combining all of the useful convolutional features into a holistic framework. It is the first attempt to adopt such rich convolutional features in computer vision tasks. Using VGG16 network, we achieve \sArt results on several available datasets. When evaluating on the well-known BSDS500 benchmark, we achieve ODS F-measure of \textbf{.811} while retaining a fast speed (\textbf{8} FPS). Besides, our fast version of RCF achieves ODS F-measure of \textbf{.806} with \textbf{30} FPS.
Bottom-Up Top-Down Cues for Weakly-Supervised Semantic Segmentation
Authors: Qinbin Hou, Puneet Kumar Dokania, Daniela Massiceti, Yunchao Wei, Ming-Ming Cheng, Philip Torr
Date: 2016-12-07 00:00:00
Tasks: Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

We consider the task of learning a classifier for semantic segmentation using
weak supervision in the form of image labels which specify the object classes
present in the image. Our method uses deep convolutional neural networks (CNNs)
and adopts an Expectation-Maximization (EM) based approach. We focus on the
following three aspects of EM: (i) initialization; (ii) latent posterior
estimation (E-step) and (iii) the parameter update (M-step). We show that
saliency and attention maps, our bottom-up and top-down cues respectively, of
simple images provide very good cues to learn an initialization for the
EM-based algorithm. Intuitively, we show that before trying to learn to segment
complex images, it is much easier and highly effective to first learn to
segment a set of simple images and then move towards the complex ones. Next, in
order to update the parameters, we propose minimizing the combination of the
standard softmax loss and the KL divergence between the true latent posterior
and the likelihood given by the CNN. We argue that this combination is more
robust to wrong predictions made by the expectation step of the EM method. We
support this argument with empirical and visual results. Extensive experiments
and discussions show that: (i) our method is very simple and intuitive; (ii)
requires only image-level labels; and (iii) consistently outperforms other
weakly-supervised state-of-the-art methods with a very high margin on the
PASCAL VOC 2012 dataset.
Weighted Motion Averaging for the Registration of Multi-View Range Scans
Authors: Rui Guo, Jihua Zhu, Yaochen Li, Dapeng Chen, Zhongyu Li, Yongqin Zhang
Date: 2017-02-21 00:00:00
Tasks: 3D Reconstruction

Multi-view registration is a fundamental but challenging problem in 3D
reconstruction and robot vision. Although the original motion averaging
algorithm has been introduced as an effective means to solve the multi-view
registration problem, it does not consider the reliability and accuracy of each
relative motion. Accordingly, this paper proposes a novel motion averaging
algorithm for multi-view registration. Firstly, it utilizes the pair-wise
registration algorithm to estimate the relative motion and overlapping
percentage of each scan pair with a certain degree of overlap. With the
overlapping percentage available, it views the overlapping percentage as the
corresponding weight of each scan pair and proposes the weight motion averaging
algorithm, which can pay more attention to reliable and accurate relative
motions. By treating each relative motion distinctively, more accurate
registration can be achieved by applying the weighted motion averaging to
multi-view range scans. Experimental results demonstrate the superiority of our
proposed approach compared with the state-of-the-art methods in terms of
accuracy, robustness and efficiency.
Motion Saliency Based Automatic Delineation of Glottis Contour in High-speed Digital Images
Authors: Xin Chen, Emma Marriott, Yuling Yan
Date: 2017-04-09 00:00:00

In recent years, high-speed videoendoscopy (HSV) has significantly aided the
diagnosis of voice pathologies and furthered the understanding the voice
production in recent years. As the first step of these studies, automatic
segmentation of glottal images till presents a major challenge for this
technique. In this paper, we propose an improved Saliency Network that
automatically delineates the contour of the glottis from HSV image sequences.
Our proposed additional saliency measure, Motion Saliency (MS), improves upon
the original Saliency Network by using the velocities of defined edges. In our
results and analysis, we demonstrate the effectiveness of our approach and
discuss its potential applications for computer-aided assessment of voice
pathologies and understanding voice production.
Riemannian stochastic variance reduced gradient on Grassmann manifold
Authors: Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra
Date: 2016-05-24 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion, Riemannian optimization, Translation

Stochastic variance reduction algorithms have recently become popular for
minimizing the average of a large, but finite, number of loss functions. In
this paper, we propose a novel Riemannian extension of the Euclidean stochastic
variance reduced gradient algorithm (R-SVRG) to a compact manifold search
space. To this end, we show the developments on the Grassmann manifold. The key
challenges of averaging, addition, and subtraction of multiple gradients are
addressed with notions like logarithm mapping and parallel translation of
vectors on the Grassmann manifold. We present a global convergence analysis of
the proposed algorithm with decay step-sizes and a local convergence rate
analysis under fixed step-size with some natural assumptions. The proposed
algorithm is applied on a number of problems on the Grassmann manifold like
principal components analysis, low-rank matrix completion, and the Karcher mean
computation. In all these cases, the proposed algorithm outperforms the
standard Riemannian stochastic gradient descent algorithm.
Memcomputing Numerical Inversion with Self-Organizing Logic Gates
Authors: Haik Manukian, Fabio L. Traversa, Massimiliano Di Ventra
Date: 2016-12-13 00:00:00

We propose to use Digital Memcomputing Machines (DMMs), implemented with
self-organizing logic gates (SOLGs), to solve the problem of numerical
inversion. Starting from fixed-point scalar inversion we describe the
generalization to solving linear systems and matrix inversion. This method,
when realized in hardware, will output the result in only one computational
step. As an example, we perform simulations of the scalar case using a 5-bit
logic circuit made of SOLGs, and show that the circuit successfully performs
the inversion. Our method can be extended efficiently to any level of
precision, since we prove that producing n-bit precision in the output requires
extending the circuit by at most n bits. This type of numerical inversion can
be implemented by DMM units in hardware, it is scalable, and thus of great
benefit to any real-time computing application.
Noisy Tensor Completion for Tensors with a Sparse Canonical Polyadic Factor
Authors: Swayambhoo Jain, Alexander Gutierrez, Jarvis Haupt
Date: 2017-04-08 00:00:00

In this paper we study the problem of noisy tensor completion for tensors
that admit a canonical polyadic or CANDECOMP/PARAFAC (CP) decomposition with
one of the factors being sparse. We present general theoretical error bounds
for an estimate obtained by using a complexity-regularized maximum likelihood
principle and then instantiate these bounds for the case of additive white
Gaussian noise. We also provide an ADMM-type algorithm for solving the
complexity-regularized maximum likelihood problem and validate the theoretical
finding via experiments on synthetic data set.
Deep Reinforcement Learning framework for Autonomous Driving
Authors: Ahmad El Sallab, Mohammed Abdou, Etienne Perot, Senthil Yogamani
Date: 2017-04-08 00:00:00
Tasks: Atari Games, Autonomous Driving, Car Racing, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning is considered to be a strong AI paradigm which can be
used to teach machines through interaction with the environment and learning
from their mistakes. Despite its perceived utility, it has not yet been
successfully applied in automotive applications. Motivated by the successful
demonstrations of learning of Atari games and Go by Google DeepMind, we propose
a framework for autonomous driving using deep reinforcement learning. This is
of particular relevance as it is difficult to pose autonomous driving as a
supervised learning problem due to strong interactions with the environment
including other vehicles, pedestrians and roadworks. As it is a relatively new
area of research for autonomous driving, we provide a short overview of deep
reinforcement learning and then describe our proposed framework. It
incorporates Recurrent Neural Networks for information integration, enabling
the car to handle partially observable scenarios. It also integrates the recent
work on attention models to focus on relevant information, thereby reducing the
computational complexity for deployment on embedded hardware. The framework was
tested in an open source 3D car racing simulator called TORCS. Our simulation
results demonstrate learning of autonomous maneuvering in a scenario of complex
road curvatures and simple interaction of other vehicles.
An Empirical Evaluation of Visual Question Answering for Novel Objects
Authors: Santhosh K. Ramakrishnan, Ambar Pal, Gaurav Sharma, Anurag Mittal
Date: 2017-04-08 00:00:00
Proceeding: CVPR 2017 7
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We study the problem of answering questions about images in the harder
setting, where the test questions and corresponding images contain novel
objects, which were not queried about in the training data. Such setting is
inevitable in real world-owing to the heavy tailed distribution of the visual
categories, there would be some objects which would not be annotated in the
train set. We show that the performance of two popular existing methods drop
significantly (up to 28%) when evaluated on novel objects cf. known objects. We
propose methods which use large existing external corpora of (i) unlabeled
text, i.e. books, and (ii) images tagged with classes, to achieve novel object
based visual question answering. We do systematic empirical studies, for both
an oracle case where the novel objects are known textually, as well as a fully
automatic case without any explicit knowledge of the novel objects, but with
the minimal assumption that the novel objects are semantically related to the
existing objects in training. The proposed methods for novel object based
visual question answering are modular and can potentially be used with many
visual question answering architectures. We show consistent improvements with
the two popular architectures and give qualitative analysis of the cases where
the model does well and of those where it fails to bring improvements.
Novel Framework for Spectral Clustering using Topological Node Features(TNF)
Authors: Lalith Srikanth Chintalapati, Raghunatha Sarma Rachakonda
Date: 2017-03-31 00:00:00
Tasks: Clustering

Spectral clustering has gained importance in recent years due to its ability
to cluster complex data as it requires only pairwise similarity among data
points with its ease of implementation. The central point in spectral
clustering is the process of capturing pair-wise similarity. In the literature,
many research techniques have been proposed for effective construction of
affinity matrix with suitable pair- wise similarity. In this paper a general
framework for capturing pairwise affinity using local features such as density,
proximity and structural similarity is been proposed. Topological Node Features
are exploited to define the notion of density and local structure. These local
features are incorporated into the construction of the affinity matrix.
Experimental results, on widely used datasets such as synthetic shape datasets,
UCI real datasets and MNIST handwritten datasets show that the proposed
framework outperforms standard spectral clustering methods.
Interactive Graphics for Visually Diagnosing Forest Classifiers in R
Authors: Natalia da Silva, Dianne Cook, Eun-Kyung Lee
Date: 2017-04-08 00:00:00
Tasks: Dimensionality Reduction, General Classification

This paper describes structuring data and constructing plots to explore
forest classification models interactively. A forest classifier is an example
of an ensemble, produced by bagging multiple trees. The process of bagging and
combining results from multiple trees, produces numerous diagnostics which,
with interactive graphics, can provide a lot of insight into class structure in
high dimensions. Various aspects are explored in this paper, to assess model
complexity, individual model contributions, variable importance and dimension
reduction, and uncertainty in prediction associated with individual
observations. The ideas are applied to the random forest algorithm, and to the
projection pursuit forest, but could be more broadly applied to other bagged
ensembles. Interactive graphics are built in R, using the ggplot2, plotly, and
shiny packages.
Analyzing the group sparsity based on the rank minimization methods
Authors: Zhiyuan Zha, Xin Liu, Xiaohua Huang, Henglin Shi, Yingyue Xu, Qiong Wang, Lan Tang, Xinggan Zhang
Date: 2016-11-28 00:00:00
Tasks: Compressive Sensing, Image Inpainting

Sparse coding has achieved a great success in various image processing
studies. However, there is not any benchmark to measure the sparsity of image
patch/group because sparse discriminant conditions cannot keep unchanged. This
paper analyzes the sparsity of group based on the strategy of the rank
minimization. Firstly, an adaptive dictionary for each group is designed. Then,
we prove that group-based sparse coding is equivalent to the rank minimization
problem, and thus the sparse coefficient of each group is measured by
estimating the singular values of each group. Based on that measurement, the
weighted Schatten $p$-norm minimization (WSNM) has been found to be the closest
solution to the real singular values of each group. Thus, WSNM can be
equivalently transformed into a non-convex $\ell_p$-norm minimization problem
in group-based sparse coding. To make the proposed scheme tractable and robust,
the alternating direction method of multipliers (ADMM) is used to solve the
$\ell_p$-norm minimization problem. Experimental results on two applications:
image inpainting and image compressive sensing (CS) recovery have shown that
the proposed scheme outperforms many state-of-the-art methods.