pwc-archive/papers-with-abstracts huggingface dataset:
Hand Gesture Detection and Conversion to Speech and Text
Authors: K. Manikandan, Ayush Patidar, Pallav Walia, Aneek Barman Roy
Date: 2018-11-29 00:00:00

The hand gestures are one of the typical methods used in sign language. It is
very difficult for the hearing-impaired people to communicate with the world.
This project presents a solution that will not only automatically recognize the
hand gestures but will also convert it into speech and text output so that
impaired person can easily communicate with normal people. A camera attached to
computer will capture images of hand and the contour feature extraction is used
to recognize the hand gestures of the person. Based on the recognized gestures,
the recorded soundtrack will be played.
Effective, Fast, and Memory-Efficient Compressed Multi-function Convolutional Neural Networks for More Accurate Medical Image Classification
Authors: Luna M. Zhang
Date: 2018-11-29 00:00:00
Tasks: General Classification, image-classification, Image Classification, Medical Image Classification

Convolutional Neural Networks (CNNs) usually use the same activation
function, such as RELU, for all convolutional layers. There are performance
limitations of just using RELU. In order to achieve better classification
performance, reduce training and testing times, and reduce power consumption
and memory usage, a new "Compressed Multi-function CNN" is developed. Google's
Inception-V4, for example, is a very deep CNN that consists of 4 Inception-A
blocks, 7 Inception-B blocks, and 3 Inception-C blocks. RELU is used for all
convolutional layers. A new "Compressed Multi-function Inception-V4" (CMI) that
can use different activation functions is created with k Inception-A blocks, m
Inception-B blocks, and n Inception-C blocks where k in {1, 2, 3, 4}, m in {1,
2, 3, 4, 5, 6, 7}, n in {1, 2, 3}, and (k+m+n)<14. For performance analysis, a
dataset for classifying brain MRI images into one of the four stages of
Alzheimer's disease is used to compare three CMI architectures with
Inception-V4 in terms of F1-score, training and testing times (related to power
consumption), and memory usage (model size). Overall, simulations show that the
new CMI models can outperform both the commonly used Inception-V4 and
Inception-V4 using different activation functions. In the future, other
"Compressed Multi-function CNNs", such as "Compressed Multi-function ResNets
and DenseNets" that have a reduced number of convolutional blocks using
different activation functions, will be developed to further increase
classification accuracy, reduce training and testing times, reduce
computational power, and reduce memory usage (model size) for building more
effective healthcare systems, such as implementing accurate and convenient
disease diagnosis systems on mobile devices that have limited battery power and
memory.
Shape-conditioned Image Generation by Learning Latent Appearance Representation from Unpaired Data
Authors: Yutaro Miyauchi, Yusuke Sugano, Yasuyuki Matsushita
Date: 2018-11-29 00:00:00
Tasks: Conditional Image Generation, Image Generation, Object

Conditional image generation is effective for diverse tasks including
training data synthesis for learning-based computer vision. However, despite
the recent advances in generative adversarial networks (GANs), it is still a
challenging task to generate images with detailed conditioning on object
shapes. Existing methods for conditional image generation use category labels
and/or keypoints and are only give limited control over object categories. In
this work, we present SCGAN, an architecture to generate images with a desired
shape specified by an input normal map. The shape-conditioned image generation
task is achieved by explicitly modeling the image appearance via a latent
appearance vector. The network is trained using unpaired training samples of
real images and rendered normal maps. This approach enables us to generate
images of arbitrary object categories with the target shape and diverse image
appearances. We show the effectiveness of our method through both qualitative
and quantitative evaluation on training data generation tasks.
Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method
Authors: Dongruo Zhou, Pan Xu, Quanquan Gu
Date: 2018-11-29 00:00:00

We propose a sample efficient stochastic variance-reduced cubic
regularization (Lite-SVRC) algorithm for finding the local minimum efficiently
in nonconvex optimization. The proposed algorithm achieves a lower sample
complexity of Hessian matrix computation than existing cubic regularization
based methods. At the heart of our analysis is the choice of a constant batch
size of Hessian matrix computation at each iteration and the stochastic
variance reduction techniques. In detail, for a nonconvex function with $n$
component functions, Lite-SVRC converges to the local minimum within
$\tilde{O}(n+n^{2/3}/\epsilon^{3/2})$ Hessian sample complexity, which is
faster than all existing cubic regularization based methods. Numerical
experiments with different nonconvex optimization problems conducted on real
datasets validate our theoretical results.
Deep learning for pedestrians: backpropagation in CNNs
Authors: Laurent Boué
Date: 2018-11-29 00:00:00
Tasks: Deep Learning

The goal of this document is to provide a pedagogical introduction to the
main concepts underpinning the training of deep neural networks using gradient
descent; a process known as backpropagation. Although we focus on a very
influential class of architectures called "convolutional neural networks"
(CNNs) the approach is generic and useful to the machine learning community as
a whole. Motivated by the observation that derivations of backpropagation are
often obscured by clumsy index-heavy narratives that appear somewhat
mathemagical, we aim to offer a conceptually clear, vectorized description that
articulates well the higher level logic. Following the principle of "writing is
nature's way of letting you know how sloppy your thinking is", we try to make
the calculations meticulous, self-contained and yet as intuitive as possible.
Taking nothing for granted, ample illustrations serve as visual guides and an
extensive bibliography is provided for further explorations.
  (For the sake of clarity, long mathematical derivations and visualizations
have been broken up into short "summarized views" and longer "detailed views"
encoded into the PDF as optional content groups. Some figures contain
animations designed to illustrate important concepts in a more engaging style.
For these reasons, we advise to download the document locally and open it using
Adobe Acrobat Reader. Other viewers were not tested and may not render the
detailed views, animations correctly.)
Weakly Supervised Silhouette-based Semantic Scene Change Detection
Authors: Ken Sakurada, Mikiya Shibuya, Weimin WANG
Date: 2018-11-29 00:00:00
Tasks: Change Detection, Scene Change Detection

This paper presents a novel semantic scene change detection scheme with only weak supervision. A straightforward approach for this task is to train a semantic change detection network directly from a large-scale dataset in an end-to-end manner. However, a specific dataset for this task, which is usually labor-intensive and time-consuming, becomes indispensable. To avoid this problem, we propose to train this kind of network from existing datasets by dividing this task into change detection and semantic extraction. On the other hand, the difference in camera viewpoints, for example, images of the same scene captured from a vehicle-mounted camera at different time points, usually brings a challenge to the change detection task. To address this challenge, we propose a new siamese network structure with the introduction of correlation layer. In addition, we collect and annotate a publicly available dataset for semantic change detection to evaluate the proposed method. The experimental results verified both the robustness to viewpoint difference in change detection task and the effectiveness for semantic change detection of the proposed networks. Our code and dataset are available at https://kensakurada.github.io/pscd.
Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound
Authors: Hadi Kazemi, Sobhan Soleymani, Fariborz Taherkhani, Seyed Mehdi Iranmanesh, Nasser M. Nasrabadi
Date: 2018-11-29 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Image-to-Image Translation, Translation, Unsupervised Image-To-Image Translation

Unsupervised image-to-image translation is a class of computer vision
problems which aims at modeling conditional distribution of images in the
target domain, given a set of unpaired images in the source and target domains.
An image in the source domain might have multiple representations in the target
domain. Therefore, ambiguity in modeling of the conditional distribution
arises, specially when the images in the source and target domains come from
different modalities. Current approaches mostly rely on simplifying assumptions
to map both domains into a shared-latent space. Consequently, they are only
able to model the domain-invariant information between the two modalities.
These approaches usually fail to model domain-specific information which has no
representation in the target domain. In this work, we propose an unsupervised
image-to-image translation framework which maximizes a domain-specific
variational information bound and learns the target domain-invariant
representation of the two domain. The proposed framework makes it possible to
map a single source image into multiple images in the target domain, utilizing
several target domain-specific codes sampled randomly from the prior
distribution, or extracted from reference images.
DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama
Authors: Shang-Ta Yang, Fu-En Wang, Chi-Han Peng, Peter Wonka, Min Sun, Hung-Kuo Chu
Date: 2018-11-29 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Room Layouts From A Single RGB Panorama, Decoder

We present a deep learning framework, called DuLa-Net, to predict
Manhattan-world 3D room layouts from a single RGB panorama. To achieve better
prediction accuracy, our method leverages two projections of the panorama at
once, namely the equirectangular panorama-view and the perspective
ceiling-view, that each contains different clues about the room layouts. Our
network architecture consists of two encoder-decoder branches for analyzing
each of the two views. In addition, a novel feature fusion structure is
proposed to connect the two branches, which are then jointly trained to predict
the 2D floor plans and layout heights. To learn more complex room layouts, we
introduce the Realtor360 dataset that contains panoramas of Manhattan-world
room layouts with different numbers of corners. Experimental results show that
our work outperforms recent state-of-the-art in prediction accuracy and
performance, especially in the rooms with non-cuboid layouts.
Efficient Online Multi-Person 2D Pose Tracking with Recurrent Spatio-Temporal Affinity Fields
Authors: Yaadhav Raaj, Haroon Idrees, Gines Hidalgo, Yaser Sheikh
Date: 2018-11-29 00:00:00
Proceeding: CVPR 2019 6
Tasks: GPU, Pose Tracking

We present an online approach to efficiently and simultaneously detect and track the 2D pose of multiple people in a video sequence. We build upon Part Affinity Field (PAF) representation designed for static images, and propose an architecture that can encode and predict Spatio-Temporal Affinity Fields (STAF) across a video sequence. In particular, we propose a novel temporal topology cross-linked across limbs which can consistently handle body motions of a wide range of magnitudes. Additionally, we make the overall approach recurrent in nature, where the network ingests STAF heatmaps from previous frames and estimates those for the current frame. Our approach uses only online inference and tracking, and is currently the fastest and the most accurate bottom-up approach that is runtime invariant to the number of people in the scene and accuracy invariant to input frame rate of camera. Running at $\sim$30 fps on a single GPU at single scale, it achieves highly competitive results on the PoseTrack benchmarks.
Simple stopping criteria for information theoretic feature selection
Authors: Shujian Yu, Jose C. Principe
Date: 2018-11-29 00:00:00
Tasks: feature selection

Feature selection aims to select the smallest feature subset that yields the
minimum generalization error. In the rich literature in feature selection,
information theory-based approaches seek a subset of features such that the
mutual information between the selected features and the class labels is
maximized. Despite the simplicity of this objective, there still remain several
open problems in optimization. These include, for example, the automatic
determination of the optimal subset size (i.e., the number of features) or a
stopping criterion if the greedy searching strategy is adopted. In this paper,
we suggest two stopping criteria by just monitoring the conditional mutual
information (CMI) among groups of variables. Using the recently developed
multivariate matrix-based Renyi's \alpha-entropy functional, which can be
directly estimated from data samples, we showed that the CMI among groups of
variables can be easily computed without any decomposition or approximation,
hence making our criteria easy to implement and seamlessly integrated into any
existing information theoretic feature selection methods with a greedy search
strategy.
Traffic Danger Recognition With Surveillance Cameras Without Training Data
Authors: Lijun Yu, Dawei Zhang, Xiangqun Chen, Alexander Hauptmann
Date: 2018-11-29 00:00:00
Tasks: 3D Reconstruction, Position, Prediction

We propose a traffic danger recognition model that works with arbitrary
traffic surveillance cameras to identify and predict car crashes. There are too
many cameras to monitor manually. Therefore, we developed a model to predict
and identify car crashes from surveillance cameras based on a 3D reconstruction
of the road plane and prediction of trajectories. For normal traffic, it
supports real-time proactive safety checks of speeds and distances between
vehicles to provide insights about possible high-risk areas. We achieve good
prediction and recognition of car crashes without using any labeled training
data of crashes. Experiments on the BrnoCompSpeed dataset show that our model
can accurately monitor the road, with mean errors of 1.80% for distance
measurement, 2.77 km/h for speed measurement, 0.24 m for car position
prediction, and 2.53 km/h for speed prediction.
ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding
Authors: Ning Liu, Yongchao Long, Changqing Zou, Qun Niu, Li Pan, Hefeng Wu
Date: 2018-11-29 00:00:00
Proceeding: CVPR 2019 6
Tasks: Crowd Counting

We propose an attention-injective deformable convolutional network called
ADCrowdNet for crowd understanding that can address the accuracy degradation
problem of highly congested noisy scenes. ADCrowdNet contains two concatenated
networks. An attention-aware network called Attention Map Generator (AMG) first
detects crowd regions in images and computes the congestion degree of these
regions. Based on detected crowd regions and congestion priors, a multi-scale
deformable network called Density Map Estimator (DME) then generates
high-quality density maps. With the attention-aware training scheme and
multi-scale deformable convolutional scheme, the proposed ADCrowdNet achieves
the capability of being more effective to capture the crowd features and more
resistant to various noises. We have evaluated our method on four popular crowd
counting datasets (ShanghaiTech, UCF_CC_50, WorldEXPO'10, and UCSD) and an
extra vehicle counting dataset TRANCOS, and our approach beats existing
state-of-the-art approaches on all of these datasets.
Prediction Factory: automated development and collaborative evaluation of predictive models
Authors: Gaurav Sheni, Benjamin Schreck, Roy Wedge, James Max Kanter, Kalyan Veeramachaneni
Date: 2018-11-29 00:00:00

In this paper, we present a data science automation system called Prediction
Factory. The system uses several key automation algorithms to enable data
scientists to rapidly develop predictive models and share them with domain
experts. To assess the system's impact, we implemented 3 different interfaces
for creating predictive modeling projects: baseline automation, full
automation, and optional automation. With a dataset of online grocery shopper
behaviors, we divided data scientists among the interfaces to specify
prediction problems, learn and evaluate models, and write a report for domain
experts to judge whether or not to fund to continue working on. In total, 22
data scientists created 94 reports that were judged 296 times by 26 experts. In
a head-to-head trial, reports generated utilizing full data science automation
interface reports were funded 57.5% of the time, while the ones that used
baseline automation were only funded 42.5% of the time. An intermediate
interface which supports optional automation generated reports were funded
58.6% more often compared to the baseline. Full automation and optional
automation reports were funded about equally when put head-to-head. These
results demonstrate that Prediction Factory has implemented a critical amount
of automation to augment the role of data scientists and improve business
outcomes.
Large-scale Generative Modeling to Improve Automated Veterinary Disease Coding
Authors: Yuhui Zhang, Allen Nie, James Zou
Date: 2018-11-29 00:00:00

Supervised learning is limited both by the quantity and quality of the
labeled data. In the field of medical record tagging, writing styles between
hospitals vary drastically. The knowledge learned from one hospital might not
transfer well to another. This problem is amplified in veterinary medicine
domain because veterinary clinics rarely apply medical codes to their records.
We proposed and trained the first large-scale generative modeling algorithm in
automated disease coding. We demonstrate that generative modeling can learn
discriminative features when additionally trained with supervised fine-tuning.
We systematically ablate and evaluate the effect of generative modeling on the
final system's performance. We compare the performance of our model with
several baselines in a challenging cross-hospital setting with substantial
domain shift. We outperform competitive baselines by a large margin. In
addition, we provide interpretation for what is learned by our model.
Network Uncertainty Informed Semantic Feature Selection for Visual SLAM
Authors: Pranav Ganti, Steven L. Waslander
Date: 2018-11-29 00:00:00
Tasks: feature selection, Semantic Segmentation, Simultaneous Localization and Mapping, Visual Odometry

In order to facilitate long-term localization using a visual simultaneous localization and mapping (SLAM) algorithm, careful feature selection can help ensure that reference points persist over long durations and the runtime and storage complexity of the algorithm remain consistent. We present SIVO (Semantically Informed Visual Odometry and Mapping), a novel information-theoretic feature selection method for visual SLAM which incorporates semantic segmentation and neural network uncertainty into the feature selection pipeline. Our algorithm selects points which provide the highest reduction in Shannon entropy between the entropy of the current state and the joint entropy of the state, given the addition of the new feature with the classification entropy of the feature from a Bayesian neural network. Each selected feature significantly reduces the uncertainty of the vehicle state and has been detected to be a static object (building, traffic sign, etc.) repeatedly with a high confidence. This selection strategy generates a sparse map which can facilitate long-term localization. The KITTI odometry dataset is used to evaluate our method, and we also compare our results against ORB_SLAM2. Overall, SIVO performs comparably to the baseline method while reducing the map size by almost 70%.
HYPE: A High Performing NLP System for Automatically Detecting Hypoglycemia Events from Electronic Health Record Notes
Authors: Yonghao Jin, Fei Li, Hong Yu
Date: 2018-11-29 00:00:00

Hypoglycemia is common and potentially dangerous among those treated for
diabetes. Electronic health records (EHRs) are important resources for
hypoglycemia surveillance. In this study, we report the development and
evaluation of deep learning-based natural language processing systems to
automatically detect hypoglycemia events from the EHR narratives. Experts in
Public Health annotated 500 EHR notes from patients with diabetes. We used this
annotated dataset to train and evaluate HYPE, supervised NLP systems for
hypoglycemia detection. In our experiment, the convolutional neural network
model yielded promising performance $Precision=0.96 \pm 0.03, Recall=0.86 \pm
0.03, F1=0.91 \pm 0.03$ in a 10-fold cross-validation setting. Despite the
annotated data is highly imbalanced, our CNN-based HYPE system still achieved a
high performance for hypoglycemia detection. HYPE could be used for EHR-based
hypoglycemia surveillance and to facilitate clinicians for timely treatment of
high-risk patients.
Automatic Rendering of Building Floor Plan Images from Textual Descriptions in English
Authors: Mahak Jain, Anurag Sanyal, Shreya Goyal, Chiranjoy Chattopadhyay, Gaurav Bhatnagar
Date: 2018-11-29 00:00:00

Human beings understand natural language description and could able to
imagine a corresponding visual for the same. For example, given a description
of the interior of a house, we could imagine its structure and arrangements of
furniture. Automatic synthesis of real-world images from text descriptions has
been explored in the computer vision community. However, there is no such
attempt in the area of document images, like floor plans. Floor plan synthesis
from sketches, as well as data-driven models, were proposed earlier. Ours is
the first attempt to render building floor plan images from textual description
automatically. Here, the input is a natural language description of the
internal structure and furniture arrangements within a house, and the output is
the 2D floor plan image of the same. We have experimented on publicly available
benchmark floor plan datasets. We were able to render realistic synthesized
floor plan images from the description written in English.
Multi-granularity hierarchical attention fusion networks for reading comprehension and question answering
Authors: Wei Wang, Ming Yan, Chen Wu
Date: 2018-11-29 00:00:00
Proceeding: ACL 2018 7
Tasks: Question Answering, Reading Comprehension, TriviaQA

This paper describes a novel hierarchical attention network for reading
comprehension style question answering, which aims to answer questions for a
given narrative paragraph. In the proposed method, attention and fusion are
conducted horizontally and vertically across layers at different levels of
granularity between question and paragraph. Specifically, it first encode the
question and paragraph with fine-grained language embeddings, to better capture
the respective representations at semantic level. Then it proposes a
multi-granularity fusion approach to fully fuse information from both global
and attended representations. Finally, it introduces a hierarchical attention
network to focuses on the answer span progressively with multi-level
softalignment. Extensive experiments on the large-scale SQuAD and TriviaQA
datasets validate the effectiveness of the proposed method. At the time of
writing the paper (Jan. 12th 2018), our model achieves the first position on
the SQuAD leaderboard for both single and ensemble models. We also achieves
state-of-the-art results on TriviaQA, AddSent and AddOne-Sent datasets.