pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Liver Segmentation with Adversarial Loss and Convolutional Neural Network
Authors: Bora Baydar, Savas Ozkan, Gozde Bozdagi Akar
Date: 2018-11-28 00:00:00
Tasks: Liver Segmentation, Segmentation

Automatic segmentation of medical images is among most demanded works in the
medical information field since it saves time of the experts in the field and
avoids human error factors. In this work, a method based on Conditional
Adversarial Networks and Fully Convolutional Networks is proposed for the
automatic segmentation of the liver MRIs. The proposed method, without any
post-processing, is achieved the second place in the SIU Liver Segmentation
Challenge 2018, data of which is provided by Dokuz Eyl\"ul University. In this
paper, some improvements for the post-processing step are also proposed and it
is shown that with these additions, the method outperforms other baseline
methods.
Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects
Authors: Michael A. Alcorn, Qi Li, Zhitao Gong, Chengfei Wang, Long Mai, Wei-Shinn Ku, Anh Nguyen
Date: 2018-11-28 00:00:00
Proceeding: CVPR 2019 6

Despite excellent performance on stationary test sets, deep neural networks
(DNNs) can fail to generalize to out-of-distribution (OoD) inputs, including
natural, non-adversarial ones, which are common in real-world settings. In this
paper, we present a framework for discovering DNN failures that harnesses 3D
renderers and 3D models. That is, we estimate the parameters of a 3D renderer
that cause a target DNN to misbehave in response to the rendered image. Using
our framework and a self-assembled dataset of 3D objects, we investigate the
vulnerability of DNNs to OoD poses of well-known objects in ImageNet. For
objects that are readily recognized by DNNs in their canonical poses, DNNs
incorrectly classify 97% of their pose space. In addition, DNNs are highly
sensitive to slight pose perturbations. Importantly, adversarial poses transfer
across models and datasets. We find that 99.9% and 99.4% of the poses
misclassified by Inception-v3 also transfer to the AlexNet and ResNet-50 image
classifiers trained on the same ImageNet dataset, respectively, and 75.5%
transfer to the YOLOv3 object detector trained on MS COCO.
Link Prediction in Networks with Core-Fringe Data
Authors: Austin R. Benson, Jon Kleinberg
Date: 2018-11-28 00:00:00
Tasks: Link Prediction, Prediction

Data collection often involves the partial measurement of a larger system. A
common example arises in collecting network data: we often obtain network
datasets by recording all of the interactions among a small set of core nodes,
so that we end up with a measurement of the network consisting of these core
nodes along with a potentially much larger set of fringe nodes that have links
to the core. Given the ubiquity of this process for assembling network data, it
is crucial to understand the role of such a `core-fringe' structure.
  Here we study how the inclusion of fringe nodes affects the standard task of
network link prediction. One might initially think the inclusion of any
additional data is useful, and hence that it should be beneficial to include
all fringe nodes that are available. However, we find that this is not true; in
fact, there is substantial variability in the value of the fringe nodes for
prediction. Once an algorithm is selected, in some datasets, including any
additional data from the fringe can actually hurt prediction performance; in
other datasets, including some amount of fringe information is useful before
prediction performance saturates or even declines; and in further cases,
including the entire fringe leads to the best performance. While such variety
might seem surprising, we show that these behaviors are exhibited by simple
random graph models.
Multi-granularity Generator for Temporal Action Proposal
Authors: Yuan Liu, Lin Ma, Yifeng Zhang, Wei Liu, Shih-Fu Chang
Date: 2018-11-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Recognition, Temporal Action Proposal Generation

Temporal action proposal generation is an important task, aiming to localize
the video segments containing human actions in an untrimmed video. In this
paper, we propose a multi-granularity generator (MGG) to perform the temporal
action proposal from different granularity perspectives, relying on the video
visual features equipped with the position embedding information. First, we
propose to use a bilinear matching model to exploit the rich local information
within the video sequence. Afterwards, two components, namely segment proposal
producer (SPP) and frame actionness producer (FAP), are combined to perform the
task of temporal action proposal at two distinct granularities. SPP considers
the whole video in the form of feature pyramid and generates segment proposals
from one coarse perspective, while FAP carries out a finer actionness
evaluation for each video frame. Our proposed MGG can be trained in an
end-to-end fashion. By temporally adjusting the segment proposals with
fine-grained frame actionness information, MGG achieves the superior
performance over state-of-the-art methods on the public THUMOS-14 and
ActivityNet-1.3 datasets. Moreover, we employ existing action classifiers to
perform the classification of the proposals generated by MGG, leading to
significant improvements compared against the competing methods for the video
detection task.
Sequence Learning with RNNs for Medical Concept Normalization in User-Generated Texts
Authors: Elena Tutubalina, Zulfat Miftahutdinov, Sergey Nikolenko, Valentin Malykh
Date: 2018-11-28 00:00:00
Tasks: Medical Concept Normalization, Semantic Similarity, Semantic Textual Similarity

In this work, we consider the medical concept normalization problem, i.e.,
the problem of mapping a disease mention in free-form text to a concept in a
controlled vocabulary, usually to the standard thesaurus in the Unified Medical
Language System (UMLS). This task is challenging since medical terminology is
very different when coming from health care professionals or from the general
public in the form of social media texts. We approach it as a sequence learning
problem, with recurrent neural networks trained to obtain semantic
representations of one- and multi-word expressions. We develop end-to-end
neural architectures tailored specifically to medical concept normalization,
including bidirectional LSTM and GRU with an attention mechanism and additional
semantic similarity features based on UMLS. Our evaluation over a standard
benchmark shows that our model improves over a state of the art baseline for
classification based on CNNs.
Towards Decentralization of Social Media
Authors: Sarang Mahajan, Amey Kasar
Date: 2018-11-28 00:00:00
Tasks: Marketing

Facebook uses Artificial Intelligence for targeting users with advertisements
based on the events in which they engage like sharing, liking, making comments,
posts by a friend, a group creation, etcetera. Each user interacts with these
events in different ways, thus receiving different recommendations curated by
Facebook's intelligent systems. Facebook segregates its users into chambers,
fragmenting them into communities. The technology has completely changed the
marketing domain. It is however caught in a race for our finite attention with
a motive to make more and more money. Facebook is not a neutral product. It is
programmed to get users addicted to it with a goal of gaining added information
about the users and optimizing the recommendations provided to the users
according to his or her preferences. This paper delineates how Facebook's
recommendation system works and presents three methods to safeguard human
vulnerabilities exploited by Facebook and other corporations.
Identity Preserving Generative Adversarial Network for Cross-Domain Person Re-identification
Authors: Jialun Liu
Date: 2018-11-28 00:00:00
Tasks: Domain Adaptation, Generative Adversarial Network, Person Re-Identification, Retrieval

Person re-identification is to retrieval pedestrian images from no-overlap
camera views detected by pedestrian detectors. Most existing person
re-identification (re-ID) models often fail to generalize well from the source
domain where the models are trained to a new target domain without labels,
because of the bias between the source and target domain. This issue
significantly limits the scalability and usability of the models in the real
world. Providing a labeled source training set and an unlabeled target training
set, the aim of this paper is to improve the generalization ability of re-ID
models to the target domain. To this end, we propose an image generative
network named identity preserving generative adversarial network (IPGAN). The
proposed method has two excellent properties: 1) only a single model is
employed to translate the labeled images from the source domain to the target
camera domains in an unsupervised manner; 2) The identity information of images
from the source domain is preserved before and after translation. Furthermore,
we propose IBN-reID model for the person re-identification task. It has better
generalization ability than baseline models, especially in the cases without
any domain adaptation. The IBN-reID model is trained on the translated images
by supervised methods. Experimental results on Market-1501 and DukeMTMC-reID
show that the images generated by IPGAN are more suitable for cross-domain
person re-identification. Very competitive re-ID accuracy is achieved by our
method.
One-Shot Instance Segmentation
Authors: Claudio Michaelis, Ivan Ustyuzhaninov, Matthias Bethge, Alexander S. Ecker
Date: 2018-11-28 00:00:00
Tasks: Few-Shot Learning, Few-Shot Object Detection, Instance Segmentation, Object Detection, One-Shot Instance Segmentation, One-Shot Learning, One-Shot Object Detection, Segmentation

We tackle the problem of one-shot instance segmentation: Given an example image of a novel, previously unknown object category, find and segment all objects of this category within a complex scene. To address this challenging new task, we propose Siamese Mask R-CNN. It extends Mask R-CNN by a Siamese backbone encoding both reference image and scene, allowing it to target detection and segmentation towards the reference category. We demonstrate empirical results on MS Coco highlighting challenges of the one-shot setting: while transferring knowledge about instance segmentation to novel object categories works very well, targeting the detection network towards the reference category appears to be more difficult. Our work provides a first strong baseline for one-shot instance segmentation and will hopefully inspire further research into more powerful and flexible scene analysis algorithms. Code is available at: https://github.com/bethgelab/siamese-mask-rcnn
Counting Complexity for Reasoning in Abstract Argumentation
Authors: Johannes K. Fichte, Markus Hecher, Arne Meier
Date: 2018-11-28 00:00:00
Tasks: Abstract Argumentation

In this paper, we consider counting and projected model counting of
extensions in abstract argumentation for various semantics. When asking for
projected counts we are interested in counting the number of extensions of a
given argumentation framework while multiple extensions that are identical when
restricted to the projected arguments count as only one projected extension. We
establish classical complexity results and parameterized complexity results
when the problems are parameterized by treewidth of the undirected
argumentation graph. To obtain upper bounds for counting projected extensions,
we introduce novel algorithms that exploit small treewidth of the undirected
argumentation graph of the input instance by dynamic programming (DP). Our
algorithms run in time double or triple exponential in the treewidth depending
on the considered semantics. Finally, we take the exponential time hypothesis
(ETH) into account and establish lower bounds of bounded treewidth algorithms
for counting extensions and projected extension.
A randomized gradient-free attack on ReLU networks
Authors: Francesco Croce, Matthias Hein
Date: 2018-11-28 00:00:00
Tasks: Object Recognition

It has recently been shown that neural networks but also other classifiers
are vulnerable to so called adversarial attacks e.g. in object recognition an
almost non-perceivable change of the image changes the decision of the
classifier. Relatively fast heuristics have been proposed to produce these
adversarial inputs but the problem of finding the optimal adversarial input,
that is with the minimal change of the input, is NP-hard. While methods based
on mixed-integer optimization which find the optimal adversarial input have
been developed, they do not scale to large networks. Currently, the attack
scheme proposed by Carlini and Wagner is considered to produce the best
adversarial inputs. In this paper we propose a new attack scheme for the class
of ReLU networks based on a direct optimization on the resulting linear
regions. In our experimental validation we improve in all except one experiment
out of 18 over the Carlini-Wagner attack with a relative improvement of up to
9\%. As our approach is based on the geometrical structure of ReLU networks, it
is less susceptible to defences targeting their functional properties.
Fixed-length Bit-string Representation of Fingerprint by Normalized Local Structures
Authors: Jun Beom Kho, Andrew B. J. Teoh, Wonjune Lee, Jaihie Kim
Date: 2018-11-28 00:00:00
Tasks: Clustering, fingerprint verification

In this paper, we propose a method to represent a fingerprint image by an
ordered, fixed-length bit-string providing improved accuracy performance,
faster matching time and compressibility. First, we devise a novel
minutia-based local structure modeled by a mixture of 2D elliptical Gaussian
functions in the pixel space. Each local structure is mapped to the Euclidean
space by normalizing the local structure with the number of minutiae that
associates to it. This simple yet crucial crux enables fast dissimilarity
computation of two local structures with Euclidean distance without distortion.
A complementary texture-based local structure to the minutia-based local
structure is also introduced whereby both can be compressed via principal
component analysis and fused easily in the Euclidean space. The fused local
structure is then converted to a K-bit ordered string via a K-means clustering
algorithm. This chain of computation with sole use of Euclidean distance is
vital for speedy and discriminative bit-string conversion. The accuracy can be
further improved by a finger-specific bit-training algorithm in which two
criteria are leveraged to select useful bit positions for matching. Experiments
are performed on Fingerprint Verification Competition (FVC) databases for
comparison with existing techniques to show the superiority of the proposed
method.
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data
Authors: Eunjeong Jeong, Seungeun Oh, Hyesung Kim, Jihong Park, Mehdi Bennis, Seong-Lyun Kim
Date: 2018-11-28 00:00:00
Tasks: BIG-bench Machine Learning, Federated Learning

On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples. To enjoy this benefit, inter-device communication overhead should be minimized. With this end, we propose federated distillation (FD), a distributed model training algorithm whose communication payload size is much smaller than a benchmark scheme, federated learning (FL), particularly when the model size is large. Moreover, user-generated data samples are likely to become non-IID across devices, which commonly degrades the performance compared to the case with an IID dataset. To cope with this, we propose federated augmentation (FAug), where each device collectively trains a generative model, and thereby augments its local data towards yielding an IID dataset. Empirical studies demonstrate that FD with FAug yields around 26x less communication overhead while achieving 95-98% test accuracy compared to FL.
Improved Calibration of Numerical Integration Error in Sigma-Point Filters
Authors: Jakub Prüher, Toni Karvonen, Chris. J. Oates, Ondřej Straka, Simo Särkkä
Date: 2018-11-28 00:00:00
Tasks: Numerical Integration, Uncertainty Quantification

The sigma-point filters, such as the UKF, which exploit numerical quadrature to obtain an additional order of accuracy in the moment transformation step, are popular alternatives to the ubiquitous EKF. The classical quadrature rules used in the sigma-point filters are motivated via polynomial approximation of the integrand, however in the applied context these assumptions cannot always be justified. As a result, quadrature error can introduce bias into estimated moments, for which there is no compensatory mechanism in the classical sigma-point filters. This can lead in turn to estimates and predictions that are poorly calibrated. In this article, we investigate the Bayes-Sard quadrature method in the context of sigma-point filters, which enables uncertainty due to quadrature error to be formalised within a probabilistic model. Our first contribution is to derive the well-known classical quadratures as special cases of the Bayes-Sard quadrature method. Then a general-purpose moment transform is developed and utilised in the design of novel sigma-point filters, so that uncertainty due to quadrature error is explicitly quantified. Numerical experiments on a challenging tracking example with misspecified initial conditions show that the additional uncertainty quantification built into our method leads to better-calibrated state estimates with improved RMSE.
Isospectralization, or how to hear shape, style, and correspondence
Authors: Luca Cosmo, Mikhail Panine, Arianna Rampini, Maks Ovsjanikov, Michael M. Bronstein, Emanuele Rodolà
Date: 2018-11-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: Style Transfer

The question whether one can recover the shape of a geometric object from its
Laplacian spectrum ('hear the shape of the drum') is a classical problem in
spectral geometry with a broad range of implications and applications. While
theoretically the answer to this question is negative (there exist examples of
iso-spectral but non-isometric manifolds), little is known about the practical
possibility of using the spectrum for shape reconstruction and optimization. In
this paper, we introduce a numerical procedure called isospectralization,
consisting of deforming one shape to make its Laplacian spectrum match that of
another. We implement the isospectralization procedure using modern
differentiable programming techniques and exemplify its applications in some of
the classical and notoriously hard problems in geometry processing, computer
vision, and graphics such as shape reconstruction, pose and style transfer, and
dense deformable correspondence.
Coordinate-based Texture Inpainting for Pose-Guided Image Generation
Authors: Artur Grigorev, Artem Sevastopolsky, Alexander Vakhitov, Victor Lempitsky
Date: 2018-11-28 00:00:00
Tasks: Image Generation, Pose-Guided Image Generation, Resynthesis

We present a new deep learning approach to pose-guided resynthesis of human photographs. At the heart of the new approach is the estimation of the complete body surface texture based on a single photograph. Since the input photograph always observes only a part of the surface, we suggest a new inpainting method that completes the texture of the human body. Rather than working directly with colors of texture elements, the inpainting network estimates an appropriate source location in the input image for each element of the body surface. This correspondence field between the input image and the texture is then further warped into the target image coordinate frame based on the desired pose, effectively establishing the correspondence between the source and the target view even when the pose change is drastic. The final convolutional network then uses the established correspondence and all other available information to synthesize the output image. A fully-convolutional architecture with deformable skip connections guided by the estimated correspondence field is used. We show state-of-the-art result for pose-guided image synthesis. Additionally, we demonstrate the performance of our system for garment transfer and pose-guided face resynthesis.
GIRNet: Interleaved Multi-Task Recurrent State Sequence Models
Authors: Divam Gupta, Tanmoy Chakraborty, Soumen Chakrabarti
Date: 2018-11-28 00:00:00
Tasks: Part-Of-Speech Tagging, Position, Sentiment Analysis, Sentiment Classification

In several natural language tasks, labeled sequences are available in
separate domains (say, languages), but the goal is to label sequences with
mixed domain (such as code-switched text). Or, we may have available models for
labeling whole passages (say, with sentiments), which we would like to exploit
toward better position-specific label inference (say, target-dependent
sentiment annotation). A key characteristic shared across such tasks is that
different positions in a primary instance can benefit from different `experts'
trained from auxiliary data, but labeled primary instances are scarce, and
labeling the best expert for each position entails unacceptable cognitive
burden. We propose GITNet, a unified position-sensitive multi-task recurrent
neural network (RNN) architecture for such applications. Auxiliary and primary
tasks need not share training instances. Auxiliary RNNs are trained over
auxiliary instances. A primary instance is also submitted to each auxiliary
RNN, but their state sequences are gated and merged into a novel composite
state sequence tailored to the primary inference task. Our approach is in sharp
contrast to recent multi-task networks like the cross-stitch and sluice
network, which do not control state transfer at such fine granularity. We
demonstrate the superiority of GIRNet using three applications: sentiment
classification of code-switched passages, part-of-speech tagging of
code-switched text, and target position-sensitive annotation of sentiment in
monolingual passages. In all cases, we establish new state-of-the-art
performance beyond recent competitive baselines.
CrowdCam: Dynamic Region Segmentation
Authors: Nir Zarrabi, Shai Avidan, Yael Moses
Date: 2018-11-28 00:00:00
Tasks: Dynamic Region Segmentation, Segmentation

We consider the problem of segmenting dynamic regions in CrowdCam images, where a dynamic region is the projection of a moving 3D object on the image plane. Quite often, these regions are the most interesting parts of an image. CrowdCam images is a set of images of the same dynamic event, captured by a group of non-collaborating users. Almost every event of interest today is captured this way. This new type of images raises the need to develop new algorithms tailored specifically for it. We propose a comprehensive solution to the problem. Our solution combines cues that are based on geometry, appearance and proximity. First, geometric reasoning is used to produce rough score maps that determine, for every pixel, how likely it is to be the projection of a static or dynamic scene point. These maps are noisy because CrowdCam images are usually few and far apart both in space and in time. Then, we use similarity in appearance space and proximity in the image plane to encourage neighboring pixels to be labeled similarly as either static or dynamic. We collected a new, and challenging, data set to evaluate our algorithm. Results show that the success score of our algorithm is nearly double that of the current state of the art approach.