pwc-archive/papers-with-abstracts huggingface dataset:
Superimposition of eye fundus images for longitudinal analysis from large public health databases
Authors: Guillaume Noyel, Rebecca Thomas, Gavin Bhakta, Andrew Crowder, David Owens, Peter Boyle
Date: 2016-07-07 00:00:00

In this paper, a method is presented for superimposition (i.e. registration)
of eye fundus images from persons with diabetes screened over many years for
diabetic retinopathy. The method is fully automatic and robust to camera
changes and colour variations across the images both in space and time. All the
stages of the process are designed for longitudinal analysis of cohort public
health databases where retinal examinations are made at approximately yearly
intervals. The method relies on a model correcting two radial distortions and
an affine transformation between pairs of images which is robustly fitted on
salient points. Each stage involves linear estimators followed by non-linear
optimisation. The model of image warping is also invertible for fast
computation. The method has been validated (1) on a simulated montage and (2)
on public health databases with 69 patients with high quality images (271 pairs
acquired mostly with different types of camera and 268 pairs acquired mostly
with the same type of camera) with success rates of 92% and 98%, and five
patients (20 pairs) with low quality images with a success rate of 100%.
Compared to two state-of-the-art methods, ours gives better results.
A Type II Fuzzy Entropy Based Multi-Level Image Thresholding Using Adaptive Plant Propagation Algorithm
Authors: Sayan Nag
Date: 2017-08-23 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

One of the most straightforward, direct and efficient approaches to Image
Segmentation is Image Thresholding. Multi-level Image Thresholding is an
essential viewpoint in many image processing and Pattern Recognition based
real-time applications which can effectively and efficiently classify the
pixels into various groups denoting multiple regions in an Image. Thresholding
based Image Segmentation using fuzzy entropy combined with intelligent
optimization approaches are commonly used direct methods to properly identify
the thresholds so that they can be used to segment an Image accurately. In this
paper a novel approach for multi-level image thresholding is proposed using
Type II Fuzzy sets combined with Adaptive Plant Propagation Algorithm (APPA).
Obtaining the optimal thresholds for an image by maximizing the entropy is
extremely tedious and time consuming with increase in the number of thresholds.
Hence, Adaptive Plant Propagation Algorithm (APPA), a memetic algorithm based
on plant intelligence, is used for fast and efficient selection of optimal
thresholds. This fact is reasonably justified by comparing the accuracy of the
outcomes and computational time consumed by other modern state-of-the-art
algorithms such as Particle Swarm Optimization (PSO), Gravitational Search
Algorithm (GSA) and Genetic Algorithm (GA).
Graph-based Neural Multi-Document Summarization
Authors: Michihiro Yasunaga, Rui Zhang, Kshitijh Meelu, Ayush Pareek, Krishnan Srinivasan, Dragomir Radev
Date: 2017-06-20 00:00:00
Proceeding: CONLL 2017 8
Tasks: Document Summarization, Multi-Document Summarization, Relation, Sentence, Sentence Embeddings

We propose a neural multi-document summarization (MDS) system that
incorporates sentence relation graphs. We employ a Graph Convolutional Network
(GCN) on the relation graphs, with sentence embeddings obtained from Recurrent
Neural Networks as input node features. Through multiple layer-wise
propagation, the GCN generates high-level hidden sentence features for salience
estimation. We then use a greedy heuristic to extract salient sentences while
avoiding redundancy. In our experiments on DUC 2004, we consider three types of
sentence relation graphs and demonstrate the advantage of combining sentence
relations in graphs with the representation power of deep neural networks. Our
model improves upon traditional graph-based extractive approaches and the
vanilla GRU sequence model with no graph, and it achieves competitive results
against other state-of-the-art multi-document summarization systems.
Cascade one-vs-rest detection network for fine-grained recognition without part annotations
Authors: Long Chen, Junyu Dong, Shengke Wang, Kin-Man Lam, Muwei Jian, Hua Zhang, Xiaochun Cao
Date: 2017-02-28 00:00:00
Tasks: Object

Fine-grained recognition is a challenging task due to the small
intra-category variances. Most of top-performing fine-grained recognition
methods leverage parts of objects for better performance. Therefore, part
annotations which are extremely computationally expensive are required. In this
paper, we propose a novel cascaded deep CNN detection framework for
fine-grained recognition which is trained to detect the whole object without
considering parts. Nevertheless, most of current top-performing detection
networks use the N+1 class (N object categories plus background) softmax loss,
and the background category with much more training samples dominates the
feature learning progress so that the features are not good for object
categories with fewer samples. To bridge this gap, we introduce a cascaded
structure to eliminate background and exploit a one-vs-rest loss to capture
more minute variances among different subordinate categories. Experiments show
that our proposed recognition framework achieves comparable performance with
state-of-the-art, part-free, fine-grained recognition methods on the
CUB-200-2011 Bird dataset. Moreover, our method even outperforms most of
part-based methods while does not need part annotations at the training stage
and is free from any annotations at test stage.
CNN Fixations: An unraveling approach to visualize the discriminative image regions
Authors: Konda Reddy Mopuri, Utsav Garg, R. Venkatesh Babu
Date: 2017-08-22 00:00:00
Tasks: Caption Generation, Image Captioning, Object Recognition

Deep convolutional neural networks (CNN) have revolutionized various fields
of vision research and have seen unprecedented adoption for multiple tasks such
as classification, detection, captioning, etc. However, they offer little
transparency into their inner workings and are often treated as black boxes
that deliver excellent performance. In this work, we aim at alleviating this
opaqueness of CNNs by providing visual explanations for the network's
predictions. Our approach can analyze variety of CNN based models trained for
vision applications such as object recognition and caption generation. Unlike
existing methods, we achieve this via unraveling the forward pass operation.
Proposed method exploits feature dependencies across the layer hierarchy and
uncovers the discriminative image locations that guide the network's
predictions. We name these locations CNN-Fixations, loosely analogous to human
eye fixations.
  Our approach is a generic method that requires no architectural changes,
additional training or gradient computation and computes the important image
locations (CNN Fixations). We demonstrate through a variety of applications
that our approach is able to localize the discriminative image locations across
different network architectures, diverse vision tasks and data modalities.
The Reachability of Computer Programs
Authors: Reginaldo I. Silva Filho, Ricardo L. Azevedo da Rocha, Camila Leite Silva, Ricardo H. Gracini Guiraldelli
Date: 2017-08-23 00:00:00

Would it be possible to explain the emergence of new computational ideas
using the computation itself? Would it be feasible to describe the discovery
process of new algorithmic solutions using only mathematics? This study is the
first effort to analyze the nature of such inquiry from the viewpoint of effort
to find a new algorithmic solution to a given problem. We define program
reachability as a probability function whose argument is a form of the
energetic cost (algorithmic entropy) of the problem.
Prototypal Analysis and Prototypal Regression
Authors: Chenyue Wu, Esteban G. Tabak
Date: 2017-01-31 00:00:00
Tasks: regression, Sensitivity

Prototypal analysis is introduced to overcome two shortcomings of archetypal
analysis: its sensitivity to outliers and its non-locality, which reduces its
applicability as a learning tool. Same as archetypal analysis, prototypal
analysis finds prototypes through convex combination of the data points and
approximates the data through convex combination of the archetypes, but it adds
a penalty for using prototypes distant from the data points for their
reconstruction. Prototypal analysis can be extended---via kernel embedding---to
probability distributions, since the convexity of the prototypes makes them
interpretable as mixtures. Finally, prototypal regression is developed, a
robust supervised procedure which allows the use of distributions as either
features or labels.
ElasticPlay: Interactive Video Summarization with Dynamic Time Budgets
Authors: Haojian Jin, Yale Song, Koji Yatani
Date: 2017-08-23 00:00:00
Tasks: Video Summarization, Video Understanding

Video consumption is being shifted from sit-and-watch to selective skimming.
Existing video player interfaces, however, only provide indirect manipulation
to support this emerging behavior. Video summarization alleviates this issue to
some extent, shortening a video based on the desired length of a summary as an
input variable. But an optimal length of a summarized video is often not
available in advance. Moreover, the user cannot edit the summary once it is
produced, limiting its practical applications. We argue that video
summarization should be an interactive, mixed-initiative process in which users
have control over the summarization procedure while algorithms help users
achieve their goal via video understanding. In this paper, we introduce
ElasticPlay, a mixed-initiative approach that combines an advanced video
summarization technique with direct interface manipulation to help users
control the video summarization process. Users can specify a time budget for
the remaining content while watching a video; our system then immediately
updates the playback plan using our proposed cut-and-forward algorithm,
determining which parts to skip or to fast-forward. This interactive process
allows users to fine-tune the summarization result with immediate feedback. We
show that our system outperforms existing video summarization techniques on the
TVSum50 dataset. We also report two lab studies (22 participants) and a
Mechanical Turk deployment study (60 participants), and show that the
participants responded favorably to ElasticPlay.
On Relaxing Determinism in Arithmetic Circuits
Authors: Arthur Choi, Adnan Darwiche
Date: 2017-08-22 00:00:00
Proceeding: ICML 2017 8

The past decade has seen a significant interest in learning tractable
probabilistic representations. Arithmetic circuits (ACs) were among the first
proposed tractable representations, with some subsequent representations being
instances of ACs with weaker or stronger properties. In this paper, we provide
a formal basis under which variants on ACs can be compared, and where the
precise roles and semantics of their various properties can be made more
transparent. This allows us to place some recent developments on ACs in a
clearer perspective and to also derive new results for ACs. This includes an
exponential separation between ACs with and without determinism; completeness
and incompleteness results; and tractability results (or lack thereof) when
computing most probable explanations (MPEs).
Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration
Authors: Zheng Tang, Gaoang Wang, Tao Liu, Young-Gun Lee, Adwin Jahn, Xu Liu, Xiaodong He, Jenq-Neng Hwang
Date: 2017-08-22 00:00:00
Tasks: Ensemble Learning, object-detection, Object Detection

Tracking of multiple objects is an important application in AI City geared
towards solving salient problems related to safety and congestion in an urban
environment. Frequent occlusion in traffic surveillance has been a major
problem in this research field. In this challenge, we propose a model-based
vehicle localization method, which builds a kernel at each patch of the 3D
deformable vehicle model and associates them with constraints in 3D space. The
proposed method utilizes shape fitness evaluation besides color information to
track vehicle objects robustly and efficiently. To build 3D car models in a
fully unsupervised manner, we also implement evolutionary camera
self-calibration from tracking of walking humans to automatically compute
camera parameters. Additionally, the segmented foreground masks which are
crucial to 3D modeling and camera self-calibration are adaptively refined by
multiple-kernel feedback from tracking. For object detection/classification,
the state-of-the-art single shot multibox detector (SSD) is adopted to train
and test on the NVIDIA AI City Dataset. To improve the accuracy on categories
with only few objects, like bus, bicycle and motorcycle, we also employ the
pretrained model from YOLO9000 with multi-scale testing. We combine the results
from SSD and YOLO9000 based on ensemble learning. Experiments show that our
proposed tracking system outperforms both state-of-the-art of tracking by
segmentation and tracking by detection.
Classification of Radiology Reports Using Neural Attention Models
Authors: Bonggun Shin, Falgun H. Chokshi, Timothy Lee, Jinho D. Choi
Date: 2017-08-22 00:00:00
Tasks: Classification, General Classification

The electronic health record (EHR) contains a large amount of
multi-dimensional and unstructured clinical data of significant operational and
research value. Distinguished from previous studies, our approach embraces a
double-annotated dataset and strays away from obscure "black-box" models to
comprehensive deep learning models. In this paper, we present a novel neural
attention mechanism that not only classifies clinically important findings.
Specifically, convolutional neural networks (CNN) with attention analysis are
used to classify radiology head computed tomography reports based on five
categories that radiologists would account for in assessing acute and
communicable findings in daily practice. The experiments show that our CNN
attention models outperform non-neural models, especially when trained on a
larger dataset. Our attention analysis demonstrates the intuition behind the
classifier's decision by generating a heatmap that highlights attended terms
used by the CNN model; this is valuable when potential downstream medical
decisions are to be performed by human experts or the classifier information is
to be used in cohort construction such as for epidemiological studies.
Lexicon Integrated CNN Models with Attention for Sentiment Analysis
Authors: Bonggun Shin, Timothy Lee, Jinho D. Choi
Date: 2016-10-20 00:00:00
Proceeding: WS 2017 9
Tasks: Sentiment Analysis, Word Embeddings

With the advent of word embeddings, lexicons are no longer fully utilized for
sentiment analysis although they still provide important features in the
traditional setting. This paper introduces a novel approach to sentiment
analysis that integrates lexicon embeddings and an attention mechanism into
Convolutional Neural Networks. Our approach performs separate convolutions for
word and lexicon embeddings and provides a global view of the document using
attention. Our models are experimented on both the SemEval'16 Task 4 dataset
and the Stanford Sentiment Treebank, and show comparative or better results
against the existing state-of-the-art systems. Our analysis shows that lexicon
embeddings allow to build high-performing models with much smaller word
embeddings, and the attention mechanism effectively dims out noisy words for
sentiment analysis.
Spatio-temporal Person Retrieval via Natural Language Queries
Authors: Masataka Yamaguchi, Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada
Date: 2017-04-26 00:00:00
Proceeding: ICCV 2017 10
Tasks: Human Detection, Natural Language Queries, Person Retrieval, Retrieval

In this paper, we address the problem of spatio-temporal person retrieval
from multiple videos using a natural language query, in which we output a tube
(i.e., a sequence of bounding boxes) which encloses the person described by the
query. For this problem, we introduce a novel dataset consisting of videos
containing people annotated with bounding boxes for each second and with five
natural language descriptions. To retrieve the tube of the person described by
a given natural language query, we design a model that combines methods for
spatio-temporal human detection and multimodal retrieval. We conduct
comprehensive experiments to compare a variety of tube and text representations
and multimodal retrieval methods, and present a strong baseline in this task as
well as demonstrate the efficacy of our tube representation and multimodal
feature embedding technique. Finally, we demonstrate the versatility of our
model by applying it to two other important tasks.
Dynamic Input Structure and Network Assembly for Few-Shot Learning
Authors: Nathan Hilliard, Nathan O. Hodas, Courtney D. Corley
Date: 2017-08-22 00:00:00
Tasks: Few-Shot Learning

The ability to learn from a small number of examples has been a difficult
problem in machine learning since its inception. While methods have succeeded
with large amounts of training data, research has been underway in how to
accomplish similar performance with fewer examples, known as one-shot or more
generally few-shot learning. This technique has been shown to have promising
performance, but in practice requires fixed-size inputs making it impractical
for production systems where class sizes can vary. This impedes training and
the final utility of few-shot learning systems. This paper describes an
approach to constructing and training a network that can handle arbitrary
example sizes dynamically as the system is used.
Human Action Recognition System using Good Features and Multilayer Perceptron Network
Authors: Jonti Talukdar, Bhavana Mehta
Date: 2017-08-22 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization

Human action recognition involves the characterization of human actions
through the automated analysis of video data and is integral in the development
of smart computer vision systems. However, several challenges like dynamic
backgrounds, camera stabilization, complex actions, occlusions etc. make action
recognition in a real time and robust fashion difficult. Several complex
approaches exist but are computationally intensive. This paper presents a novel
approach of using a combination of good features along with iterative optical
flow algorithm to compute feature vectors which are classified using a
multilayer perceptron (MLP) network. The use of multiple features for motion
descriptors enhances the quality of tracking. Resilient backpropagation
algorithm is used for training the feedforward neural network reducing the
learning time. The overall system accuracy is improved by optimizing the
various parameters of the multilayer perceptron network.
Deep Bilateral Learning for Real-Time Image Enhancement
Authors: Michaël Gharbi, Jiawen Chen, Jonathan T. Barron, Samuel W. Hasinoff, Frédo Durand
Date: 2017-07-10 00:00:00
Tasks: Image Enhancement, Image Retouching

Performance is a critical challenge in mobile image processing. Given a
reference imaging pipeline, or even human-adjusted pairs of images, we seek to
reproduce the enhancements and enable real-time evaluation. For this, we
introduce a new neural network architecture inspired by bilateral grid
processing and local affine color transforms. Using pairs of input/output
images, we train a convolutional neural network to predict the coefficients of
a locally-affine model in bilateral space. Our architecture learns to make
local, global, and content-dependent decisions to approximate the desired image
transformation. At runtime, the neural network consumes a low-resolution
version of the input image, produces a set of affine transformations in
bilateral space, upsamples those transformations in an edge-preserving fashion
using a new slicing node, and then applies those upsampled transformations to
the full-resolution image. Our algorithm processes high-resolution images on a
smartphone in milliseconds, provides a real-time viewfinder at 1080p
resolution, and matches the quality of state-of-the-art approximation
techniques on a large class of image operators. Unlike previous work, our model
is trained off-line from data and therefore does not require access to the
original operator at runtime. This allows our model to learn complex,
scene-dependent transformations for which no reference implementation is
available, such as the photographic edits of a human retoucher.
Reflection Separation and Deblurring of Plenoptic Images
Authors: Paramanand Chandramouli, Mehdi Noroozi, Paolo Favaro
Date: 2017-08-22 00:00:00
Tasks: Deblurring, Depth Estimation, Reflection Removal

In this paper, we address the problem of reflection removal and deblurring
from a single image captured by a plenoptic camera. We develop a two-stage
approach to recover the scene depth and high resolution textures of the
reflected and transmitted layers. For depth estimation in the presence of
reflections, we train a classifier through convolutional neural networks. For
recovering high resolution textures, we assume that the scene is composed of
planar regions and perform the reconstruction of each layer by using an
explicit form of the plenoptic camera point spread function. The proposed
framework also recovers the sharp scene texture with different motion blurs
applied to each layer. We demonstrate our method on challenging real and
synthetic images.
Flexible Low-Rank Statistical Modeling with Side Information
Authors: William Fithian, Rahul Mazumder
Date: 2013-08-20 00:00:00
Tasks: Matrix Completion, Multi-Task Learning

We propose a general framework for reduced-rank modeling of matrix-valued
data. By applying a generalized nuclear norm penalty we can directly model
low-dimensional latent variables associated with rows and columns. Our
framework flexibly incorporates row and column features, smoothing kernels, and
other sources of side information by penalizing deviations from the row and
column models. Moreover, a large class of these models can be estimated
scalably using convex optimization. The computational bottleneck in each case
is one singular value decomposition per iteration of a large but easy-to-apply
matrix. Our framework generalizes traditional convex matrix completion and
multi-task learning methods as well as maximum a posteriori estimation under a
large class of popular hierarchical Bayesian models.
Representation Learning by Learning to Count
Authors: Mehdi Noroozi, Hamed Pirsiavash, Paolo Favaro
Date: 2017-08-22 00:00:00
Proceeding: ICCV 2017 10
Tasks: Relation, Representation Learning, Self-Supervised Image Classification, Transfer Learning

We introduce a novel method for representation learning that uses an
artificial supervision signal based on counting visual primitives. This
supervision signal is obtained from an equivariance relation, which does not
require any manual annotation. We relate transformations of images to
transformations of the representations. More specifically, we look for the
representation that satisfies such relation rather than the transformations
that match a given representation. In this paper, we use two image
transformations in the context of counting: scaling and tiling. The first
transformation exploits the fact that the number of visual primitives should be
invariant to scale. The second transformation allows us to equate the total
number of visual primitives in each tile to that in the whole image. These two
transformations are combined in one constraint and used to train a neural
network with a contrastive loss. The proposed task produces representations
that perform on par or exceed the state of the art in transfer learning
benchmarks.