pwc-archive/papers-with-abstracts huggingface dataset:
OBJ2TEXT: Generating Visually Descriptive Language from Object Layouts
Authors: Xuwang Yin, Vicente Ordonez
Date: 2017-07-22 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Caption Generation, Descriptive, Image Captioning, Language Modeling, Language Modelling, Object

Generating captions for images is a task that has recently received
considerable attention. In this work we focus on caption generation for
abstract scenes, or object layouts where the only information provided is a set
of objects and their locations. We propose OBJ2TEXT, a sequence-to-sequence
model that encodes a set of objects and their locations as an input sequence
using an LSTM network, and decodes this representation using an LSTM language
model. We show that our model, despite encoding object layouts as a sequence,
can represent spatial relationships between objects, and generate descriptions
that are globally coherent and semantically relevant. We test our approach in a
task of object-layout captioning by using only object annotations as inputs. We
additionally show that our model, combined with a state-of-the-art object
detector, improves an image captioning model from 0.863 to 0.950 (CIDEr score)
in the test benchmark of the standard MS-COCO Captioning task.
Fast and Accurate Entity Recognition with Iterated Dilated Convolutions
Authors: Emma Strubell, Patrick Verga, David Belanger, Andrew McCallum
Date: 2017-02-07 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Computational Efficiency, GPU, Named Entity Recognition (NER), NER, Structured Prediction

Today when many practitioners run basic NLP on the entire web and
large-volume traffic, faster methods are paramount to saving time and energy
costs. Recent advances in GPU hardware have led to the emergence of
bi-directional LSTMs as a standard method for obtaining per-token vector
representations serving as input to labeling tasks such as NER (often followed
by prediction in a linear-chain CRF). Though expressive and accurate, these
models fail to fully exploit GPU parallelism, limiting their computational
efficiency. This paper proposes a faster alternative to Bi-LSTMs for NER:
Iterated Dilated Convolutional Neural Networks (ID-CNNs), which have better
capacity than traditional CNNs for large context and structured prediction.
Unlike LSTMs whose sequential processing on sentences of length N requires O(N)
time even in the face of parallelism, ID-CNNs permit fixed-depth convolutions
to run in parallel across entire documents. We describe a distinct combination
of network structure, parameter sharing and training procedures that enable
dramatic 14-20x test-time speedups while retaining accuracy comparable to the
Bi-LSTM-CRF. Moreover, ID-CNNs trained to aggregate context from the entire
document are even more accurate while maintaining 8x faster test time speeds.
Wavelet-based Reflection Symmetry Detection via Textural and Color Histograms
Authors: Mohamed Elawady, Christophe Ducottet, Olivier Alata, Cecile Barat, Philippe Colantoni
Date: 2017-07-10 00:00:00
Tasks: Symmetry Detection

Symmetry is one of the significant visual properties inside an image plane,
to identify the geometrically balanced structures through real-world objects.
Existing symmetry detection methods rely on descriptors of the local image
features and their neighborhood behavior, resulting incomplete symmetrical axis
candidates to discover the mirror similarities on a global scale. In this
paper, we propose a new reflection symmetry detection scheme, based on a
reliable edge-based feature extraction using Log-Gabor filters, plus an
efficient voting scheme parameterized by their corresponding textural and color
neighborhood information. Experimental evaluation on four single-case and three
multiple-case symmetry detection datasets validates the superior achievement of
the proposed work to find global symmetries inside an image.
Identifying civilians killed by police with distantly supervised entity-event extraction
Authors: Katherine A. Keith, Abram Handler, Michael Pinkham, Cara Magliozzi, Joshua McDuffie, Brendan O'Connor
Date: 2017-07-22 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Event Extraction, regression

We propose a new, socially-impactful task for natural language processing:
from a news corpus, extract names of persons who have been killed by police. We
present a newly collected police fatality corpus, which we release publicly,
and present a model to solve this problem that uses EM-based distant
supervision with logistic regression and convolutional neural network
classifiers. Our model outperforms two off-the-shelf event extractor systems,
and it can suggest candidate victim names in some cases faster than one of the
major manually-collected police fatality databases.
Automatic Curation of Golf Highlights using Multimodal Excitement Features
Authors: Michele Merler, Dhiraj Joshi, Quoc-Bao Nguyen, Stephen Hammer, John Kent, John R. Smith, Rogerio S. Feris
Date: 2017-07-22 00:00:00
Tasks: Action Recognition, Retrieval, Temporal Action Localization, Video Editing

The production of sports highlight packages summarizing a game's most
exciting moments is an essential task for broadcast media. Yet, it requires
labor-intensive video editing. We propose a novel approach for auto-curating
sports highlights, and use it to create a real-world system for the editorial
aid of golf highlight reels. Our method fuses information from the players'
reactions (action recognition such as high-fives and fist pumps), spectators
(crowd cheering), and commentator (tone of the voice and word analysis) to
determine the most interesting moments of a game. We accurately identify the
start and end frames of key shot highlights with additional metadata, such as
the player's name and the hole number, allowing personalized content
summarization and retrieval. In addition, we introduce new techniques for
learning our classifiers with reduced manual training data annotation by
exploiting the correlation of different modalities. Our work has been
demonstrated at a major golf tournament, successfully extracting highlights
from live video streams over four consecutive days.
PACRR: A Position-Aware Neural IR Model for Relevance Matching
Authors: Kai Hui, Andrew Yates, Klaus Berberich, Gerard de Melo
Date: 2017-04-12 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Ad-Hoc Information Retrieval, Information Retrieval, Position, Retrieval

In order to adopt deep learning for information retrieval, models are needed
that can capture all relevant information required to assess the relevance of a
document to a given user query. While previous works have successfully captured
unigram term matches, how to fully employ position-dependent information such
as proximity and term dependencies has been insufficiently explored. In this
work, we propose a novel neural IR model named PACRR aiming at better modeling
position-dependent interactions between a query and a document. Extensive
experiments on six years' TREC Web Track data confirm that the proposed model
yields better results under multiple benchmarks.
A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization
Authors: Xinyu Hua, Lu Wang
Date: 2017-07-21 00:00:00
Proceeding: WS 2017 9
Tasks: Abstractive Text Summarization, Articles, Domain Adaptation

We study the problem of domain adaptation for neural abstractive
summarization. We make initial efforts in investigating what information can be
transferred to a new domain. Experimental results on news stories and opinion
articles indicate that neural summarization model benefits from pre-training
based on extractive summaries. We also find that the combination of in-domain
and out-of-domain setup yields better summaries when in-domain data is
insufficient. Further analysis shows that, the model is capable to select
salient content even trained on out-of-domain data, but requires in-domain data
to capture the style for a target domain.
Structural Compression of Convolutional Neural Networks
Authors: Reza Abbasi-Asl, Bin Yu
Date: 2017-05-20 00:00:00
Tasks: Diversity, General Classification

Deep convolutional neural networks (CNNs) have been successful in many tasks in machine vision, however, millions of weights in the form of thousands of convolutional filters in CNNs makes them difficult for human intepretation or understanding in science. In this article, we introduce CAR, a greedy structural compression scheme to obtain smaller and more interpretable CNNs, while achieving close to original accuracy. The compression is based on pruning filters with the least contribution to the classification accuracy. We demonstrate the interpretability of CAR-compressed CNNs by showing that our algorithm prunes filters with visually redundant functionalities such as color filters. These compressed networks are easier to interpret because they retain the filter diversity of uncompressed networks with order of magnitude less filters. Finally, a variant of CAR is introduced to quantify the importance of each image category to each CNN filter. Specifically, the most and the least important class labels are shown to be meaningful interpretations of each filter.
A Data-Driven Approach to Pre-Operative Evaluation of Lung Cancer Patients
Authors: Oleksiy Budilovsky, Golnaz Alipour, Andre Knoesen, Lisa Brown, Soheil Ghiasi
Date: 2017-07-21 00:00:00
Tasks: General Classification

Lung cancer is the number one cause of cancer deaths. Many early stage lung
cancer patients have resectable tumors; however, their cardiopulmonary function
needs to be properly evaluated before they are deemed operative candidates.
Consequently, a subset of such patients is asked to undergo standard pulmonary
function tests, such as cardiopulmonary exercise tests (CPET) or stair climbs,
to have their pulmonary function evaluated. The standard tests are expensive,
labor intensive, and sometimes ineffective due to co-morbidities, such as
limited mobility. Recovering patients would benefit greatly from a device that
can be worn at home, is simple to use, and is relatively inexpensive. Using
advances in information technology, the goal is to design a continuous,
inexpensive, mobile and patient-centric mechanism for evaluation of a patient's
pulmonary function. A light mobile mask is designed, fitted with CO2, O2, flow
volume, and accelerometer sensors and tested on 18 subjects performing 15
minute exercises. The data collected from the device is stored in a cloud
service and machine learning algorithms are used to train and predict a user's
activity .Several classification techniques are compared - K Nearest Neighbor,
Random Forest, Support Vector Machine, Artificial Neural Network, and Naive
Bayes. One useful area of interest involves comparing a patient's predicted
activity levels, especially using only breath data, to that of a normal
person's, using the classification models.
Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation
Authors: Vicente Dominguez, Pablo Messina, Denis Parra, Domingo Mery, Christoph Trattner, Alvaro Soto
Date: 2017-06-22 00:00:00

Advances in image processing and computer vision in the latest years have
brought about the use of visual features in artwork recommendation. Recent
works have shown that visual features obtained from pre-trained deep neural
networks (DNNs) perform very well for recommending digital art. Other recent
works have shown that explicit visual features (EVF) based on attractiveness
can perform well in preference prediction tasks, but no previous work has
compared DNN features versus specific attractiveness-based visual features
(e.g. brightness, texture) in terms of recommendation performance. In this
work, we study and compare the performance of DNN and EVF features for the
purpose of physical artwork recommendation using transactional data from
UGallery, an online store of physical paintings. In addition, we perform an
exploratory analysis to understand if DNN embedded features have some relation
with certain EVF. Our results show that DNN features outperform EVF, that
certain EVF features are more suited for physical artwork recommendation and,
finally, we show evidence that certain neurons in the DNN might be partially
encoding visual features such as brightness, providing an opportunity for
explaining recommendations based on visual neural models.
Robotic Grasp Detection using Deep Convolutional Neural Networks
Authors: Sulabh Kumra, Christopher Kanan
Date: 2016-11-24 00:00:00
Tasks: Deep Learning, Robotic Grasping

Deep learning has significantly advanced computer vision and natural language
processing. While there have been some successes in robotics using deep
learning, it has not been widely adopted. In this paper, we present a novel
robotic grasp detection system that predicts the best grasping pose of a
parallel-plate robotic gripper for novel objects using the RGB-D image of the
scene. The proposed model uses a deep convolutional neural network to extract
features from the scene and then uses a shallow convolutional neural network to
predict the grasp configuration for the object of interest. Our multi-modal
model achieved an accuracy of 89.21% on the standard Cornell Grasp Dataset and
runs at real-time speeds. This redefines the state-of-the-art for robotic grasp
detection.
Dependency Parsing with Dilated Iterated Graph CNNs
Authors: Emma Strubell, Andrew McCallum
Date: 2017-05-01 00:00:00
Proceeding: WS 2017 9
Tasks: Dependency Parsing, GPU, Sentence

Dependency parses are an effective way to inject linguistic knowledge into
many downstream tasks, and many practitioners wish to efficiently parse
sentences at scale. Recent advances in GPU hardware have enabled neural
networks to achieve significant gains over the previous best models, these
models still fail to leverage GPUs' capability for massive parallelism due to
their requirement of sequential processing of the sentence. In response, we
propose Dilated Iterated Graph Convolutional Neural Networks (DIG-CNNs) for
graph-based dependency parsing, a graph convolutional architecture that allows
for efficient end-to-end GPU parsing. In experiments on the English Penn
TreeBank benchmark, we show that DIG-CNNs perform on par with some of the best
neural network parsers.
Detecting Parts for Action Localization
Authors: Nicolas Chesneau, Grégory Rogez, Karteek Alahari, Cordelia Schmid
Date: 2017-07-19 00:00:00
Tasks: Action Localization

In this paper, we propose a new framework for action localization that tracks
people in videos and extracts full-body human tubes, i.e., spatio-temporal
regions localizing actions, even in the case of occlusions or truncations. This
is achieved by training a novel human part detector that scores visible parts
while regressing full-body bounding boxes. The core of our method is a
convolutional neural network which learns part proposals specific to certain
body parts. These are then combined to detect people robustly in each frame.
Our tracking algorithm connects the image detections temporally to extract
full-body human tubes. We apply our new tube extraction method on the problem
of human action localization, on the popular JHMDB dataset, and a very recent
challenging dataset DALY (Daily Action Localization in YouTube), showing
state-of-the-art results.
Reinforcement Learning with Deep Energy-Based Policies
Authors: Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, Sergey Levine
Date: 2017-02-27 00:00:00
Proceeding: ICML 2017 8
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a method for learning expressive energy-based policies for
continuous states and actions, which has been feasible only in tabular domains
before. We apply our method to learning maximum entropy policies, resulting
into a new algorithm, called soft Q-learning, that expresses the optimal policy
via a Boltzmann distribution. We use the recently proposed amortized Stein
variational gradient descent to learn a stochastic sampling network that
approximates samples from this distribution. The benefits of the proposed
algorithm include improved exploration and compositionality that allows
transferring skills between tasks, which we confirm in simulated experiments
with swimming and walking robots. We also draw a connection to actor-critic
methods, which can be viewed performing approximate inference on the
corresponding energy-based model.
Confidence estimation in Deep Neural networks via density modelling
Authors: Akshayvarun Subramanya, Suraj Srinivas, R. Venkatesh Babu
Date: 2017-07-21 00:00:00

State-of-the-art Deep Neural Networks can be easily fooled into providing
incorrect high-confidence predictions for images with small amounts of
adversarial noise. Does this expose a flaw with deep neural networks, or do we
simply need a better way to estimate confidence? In this paper we consider the
problem of accurately estimating predictive confidence. We formulate this
problem as that of density modelling, and show how traditional methods such as
softmax produce poor estimates. To address this issue, we propose a novel
confidence measure based on density modelling approaches. We test these
measures on images distorted by blur, JPEG compression, random noise and
adversarial noise. Experiments show that our confidence measure consistently
shows reduced confidence scores in the presence of such distortions - a
property which softmax often lacks.
Fast Algorithms for Demixing Sparse Signals from Nonlinear Observations
Authors: Mohammadreza Soltani, Chinmay Hegde
Date: 2016-08-03 00:00:00
Tasks: Astronomy

We study the problem of demixing a pair of sparse signals from noisy,
nonlinear observations of their superposition. Mathematically, we consider a
nonlinear signal observation model, $y_i = g(a_i^Tx) + e_i, \ i=1,\ldots,m$,
where $x = \Phi w+\Psi z$ denotes the superposition signal, $\Phi$ and $\Psi$
are orthonormal bases in $\mathbb{R}^n$, and $w, z\in\mathbb{R}^n$ are sparse
coefficient vectors of the constituent signals, and $e_i$ represents the noise.
Moreover, $g$ represents a nonlinear link function, and $a_i\in\mathbb{R}^n$ is
the $i$-th row of the measurement matrix, $A\in\mathbb{R}^{m\times n}$.
Problems of this nature arise in several applications ranging from astronomy,
computer vision, and machine learning. In this paper, we make some concrete
algorithmic progress for the above demixing problem. Specifically, we consider
two scenarios: (i) the case when the demixing procedure has no knowledge of the
link function, and (ii) the case when the demixing algorithm has perfect
knowledge of the link function. In both cases, we provide fast algorithms for
recovery of the constituents $w$ and $z$ from the observations. Moreover, we
support these algorithms with a rigorous theoretical analysis, and derive
(nearly) tight upper bounds on the sample complexity of the proposed algorithms
for achieving stable recovery of the component signals. We also provide a range
of numerical simulations to illustrate the performance of the proposed
algorithms on both real and synthetic signals and images.
Memory-Efficient Implementation of DenseNets
Authors: Geoff Pleiss, Danlu Chen, Gao Huang, Tongcheng Li, Laurens van der Maaten, Kilian Q. Weinberger
Date: 2017-07-21 00:00:00
Tasks: GPU

The DenseNet architecture is highly computationally efficient as a result of
feature reuse. However, a naive DenseNet implementation can require a
significant amount of GPU memory: If not properly managed, pre-activation batch
normalization and contiguous convolution operations can produce feature maps
that grow quadratically with network depth. In this technical report, we
introduce strategies to reduce the memory consumption of DenseNets during
training. By strategically using shared memory allocations, we reduce the
memory cost for storing feature maps from quadratic to linear. Without the GPU
memory bottleneck, it is now possible to train extremely deep DenseNets.
Networks with 14M parameters can be trained on a single GPU, up from 4M. A
264-layer DenseNet (73M parameters), which previously would have been
infeasible to train, can now be trained on a single workstation with 8 NVIDIA
Tesla M40 GPUs. On the ImageNet ILSVRC classification dataset, this large
DenseNet obtains a state-of-the-art single-crop top-1 error of 20.26%.
Adversarial Examples for Semantic Segmentation and Object Detection
Authors: Cihang Xie, Jian-Yu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, Alan Yuille
Date: 2017-03-24 00:00:00
Proceeding: ICCV 2017 10
Tasks: Adversarial Attack, image-classification, Object, object-detection, Object Detection, Segmentation, Semantic Segmentation

It has been well demonstrated that adversarial examples, i.e., natural images
with visually imperceptible perturbations added, generally exist for deep
networks to fail on image classification. In this paper, we extend adversarial
examples to semantic segmentation and object detection which are much more
difficult. Our observation is that both segmentation and detection are based on
classifying multiple targets on an image (e.g., the basic target is a pixel or
a receptive field in segmentation, and an object proposal in detection), which
inspires us to optimize a loss function over a set of pixels/proposals for
generating adversarial perturbations. Based on this idea, we propose a novel
algorithm named Dense Adversary Generation (DAG), which generates a large
family of adversarial examples, and applies to a wide range of state-of-the-art
deep networks for segmentation and detection. We also find that the adversarial
perturbations can be transferred across networks with different training data,
based on different architectures, and even for different recognition tasks. In
particular, the transferability across networks with the same architecture is
more significant than in other cases. Besides, summing up heterogeneous
perturbations often leads to better transfer performance, which provides an
effective method of black-box adversarial attack.
Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation
Authors: Lei Tai, Giuseppe Paolo, Ming Liu
Date: 2017-03-01 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, Navigate, Position, Reinforcement Learning, Reinforcement Learning (RL)

We present a learning-based mapless motion planner by taking the sparse
10-dimensional range findings and the target position with respect to the
mobile robot coordinate frame as input and the continuous steering commands as
output. Traditional motion planners for mobile ground robots with a laser range
sensor mostly depend on the obstacle map of the navigation environment where
both the highly precise laser sensor and the obstacle map building work of the
environment are indispensable. We show that, through an asynchronous deep
reinforcement learning method, a mapless motion planner can be trained
end-to-end without any manually designed features and prior demonstrations. The
trained planner can be directly applied in unseen virtual and real
environments. The experiments show that the proposed mapless motion planner can
navigate the nonholonomic mobile robot to the desired targets without colliding
with any obstacles.