pwc-archive/papers-with-abstracts huggingface dataset:
An overview of deep learning in medical imaging focusing on MRI
Authors: Alexander Selvikvåg Lundervold, Arvid Lundervold
Date: 2018-11-25 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Disease Prediction, Image Retrieval, Medical Image Analysis, Retrieval

What has happened in machine learning lately, and what does it mean for the
future of medical image analysis? Machine learning has witnessed a tremendous
amount of attention over the last few years. The current boom started around
2009 when so-called deep artificial neural networks began outperforming other
established models on a number of important benchmarks. Deep neural networks
are now the state-of-the-art machine learning models across a variety of areas,
from image analysis to natural language processing, and widely deployed in
academia and industry. These developments have a huge potential for medical
imaging technology, medical data analysis, medical diagnostics and healthcare
in general, slowly being realized. We provide a short overview of recent
advances and some associated challenges in machine learning applied to medical
image processing and image analysis. As this has become a very broad and fast
expanding field we will not survey the entire landscape of applications, but
put particular focus on deep learning in MRI.
  Our aim is threefold: (i) give a brief introduction to deep learning with
pointers to core references; (ii) indicate how deep learning has been applied
to the entire MRI processing chain, from acquisition to image retrieval, from
segmentation to disease prediction; (iii) provide a starting point for people
interested in experimenting and perhaps contributing to the field of machine
learning for medical imaging by pointing out good educational resources,
state-of-the-art open-source code, and interesting sources of data and problems
related medical imaging.
Joint Facade Registration and Segmentation for Urban Localization
Authors: Antoine Fond, Marie-Odile Berger, Gilles Simon
Date: 2018-11-25 00:00:00
Tasks: Segmentation, Semantic Segmentation

This paper presents an efficient approach for solving jointly facade
registration and semantic segmentation. Progress in facade detection and
recognition enable good initialization for the registration of a reference
facade to a newly acquired target image. We propose here to rely on semantic
segmentation to improve the accuracy of that initial registration.
Simultaneously we aim to improve the quality of the semantic segmentation
through the registration. These two problems are jointly solved in a
Expectation-Maximization framework. We especially introduce a bayesian model
that use prior semantic segmentation as well as geometric structure of the
facade reference modeled by $L_p$ Gaussian Mixtures. We show the advantages of
our method in term of robustness to clutter and change of illumination on urban
images from various database.
Multi-view Point Cloud Registration with Adaptive Convergence Threshold and its Application on 3D Model Retrieval
Authors: Yaochen Li, Ying Liu, Rui Sun, Rui Guo, Li Zhu, Yong Qi
Date: 2018-11-25 00:00:00
Tasks: Point Cloud Registration, Retrieval

Multi-view point cloud registration is a hot topic in the communities of
multimedia technology and artificial intelligence (AI). In this paper, we
propose a framework to reconstruct the 3D models by the multi-view point cloud
registration algorithm with adaptive convergence threshold, and subsequently
apply it to 3D model retrieval. The iterative closest point (ICP) algorithm is
implemented combining with the motion average algorithm for the registration of
multi-view point clouds. After the registration process, we design applications
for 3D model retrieval. The geometric saliency map is computed based on the
vertex curvature. The test facial triangle is then generated based on the
saliency map, which is applied to compare with the standard facial triangle.
The face and non-face models are then discriminated. The experiments and
comparisons prove the effectiveness of the proposed framework.
Background Subtraction with Real-time Semantic Segmentation
Authors: Dongdong Zeng, Xiang Chen, Ming Zhu, Michael Goesele, Arjan Kuijper
Date: 2018-11-25 00:00:00
Tasks: Foreground Segmentation, Object Tracking, Real-Time Semantic Segmentation, Semantic Segmentation

Accurate and fast foreground object extraction is very important for object
tracking and recognition in video surveillance. Although many background
subtraction (BGS) methods have been proposed in the recent past, it is still
regarded as a tough problem due to the variety of challenging situations that
occur in real-world scenarios. In this paper, we explore this problem from a
new perspective and propose a novel background subtraction framework with
real-time semantic segmentation (RTSS). Our proposed framework consists of two
components, a traditional BGS segmenter $\mathcal{B}$ and a real-time semantic
segmenter $\mathcal{S}$. The BGS segmenter $\mathcal{B}$ aims to construct
background models and segments foreground objects. The real-time semantic
segmenter $\mathcal{S}$ is used to refine the foreground segmentation outputs
as feedbacks for improving the model updating accuracy. $\mathcal{B}$ and
$\mathcal{S}$ work in parallel on two threads. For each input frame $I_t$, the
BGS segmenter $\mathcal{B}$ computes a preliminary foreground/background
(FG/BG) mask $B_t$. At the same time, the real-time semantic segmenter
$\mathcal{S}$ extracts the object-level semantics ${S}_t$. Then, some specific
rules are applied on ${B}_t$ and ${S}_t$ to generate the final detection
${D}_t$. Finally, the refined FG/BG mask ${D}_t$ is fed back to update the
background model. Comprehensive experiments evaluated on the CDnet 2014 dataset
demonstrate that our proposed method achieves state-of-the-art performance
among all unsupervised background subtraction methods while operating at
real-time, and even performs better than some deep learning based supervised
algorithms. In addition, our proposed framework is very flexible and has the
potential for generalization.
Dissimilarity Coefficient based Weakly Supervised Object Detection
Authors: Aditya Arun, C. V. Jawahar, M. Pawan Kumar
Date: 2018-11-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object, object-detection, Object Detection, Stochastic Optimization, Weakly Supervised Object Detection

We consider the problem of weakly supervised object detection, where the
training samples are annotated using only image-level labels that indicate the
presence or absence of an object category. In order to model the uncertainty in
the location of the objects, we employ a dissimilarity coefficient based
probabilistic learning objective. The learning objective minimizes the
difference between an annotation agnostic prediction distribution and an
annotation aware conditional distribution. The main computational challenge is
the complex nature of the conditional distribution, which consists of terms
over hundreds or thousands of variables. The complexity of the conditional
distribution rules out the possibility of explicitly modeling it. Instead, we
exploit the fact that deep learning frameworks rely on stochastic optimization.
This allows us to use a state of the art discrete generative model that can
provide annotation consistent samples from the conditional distribution.
Extensive experiments on PASCAL VOC 2007 and 2012 data sets demonstrate the
efficacy of our proposed approach.
Describe and Attend to Track: Learning Natural Language guided Structural Representation and Visual Attention for Object Tracking
Authors: Xiao Wang, Chenglong Li, Rui Yang, Tianzhu Zhang, Jin Tang, Bin Luo
Date: 2018-11-25 00:00:00
Tasks: Object Tracking

The tracking-by-detection framework requires a set of positive and negative
training samples to learn robust tracking models for precise localization of
target objects. However, existing tracking models mostly treat different
samples independently while ignores the relationship information among them. In
this paper, we propose a novel structure-aware deep neural network to overcome
such limitations. In particular, we construct a graph to represent the pairwise
relationships among training samples, and additionally take the natural
language as the supervised information to learn both feature representations
and classifiers robustly. To refine the states of the target and re-track the
target when it is back to view from heavy occlusion and out of view, we
elaborately design a novel subnetwork to learn the target-driven visual
attentions from the guidance of both visual and natural language cues.
Extensive experiments on five tracking benchmark datasets validated the
effectiveness of our proposed method.
Nonlinear Dynamics of Binocular Rivalry: A Comparative Study
Authors: Yashaswini Murthy
Date: 2018-11-25 00:00:00

When our eyes are presented with the same image, the brain processes it to
view it as a single coherent one. The lateral shift in the position of our
eyes, causes the two images to possess certain differences, which our brain
exploits for the purpose of depth perception and to gauge the size of objects
at different distances, a process commonly known as stereopsis. However, when
presented with two different visual stimuli, the visual awareness alternates.
This phenomenon of binocular rivalry is a result of competition between the
corresponding neuronal populations of the two eyes. The article presents a
comparative study of various dynamical models proposed to capture this process.
It goes on to study the effect of a certain parameter on the rate of perceptual
alternations and proceeds to disprove the initial propositions laid down to
characterise this phenomenon. It concludes with a discussion on the possible
future work that can be conducted to obtain a better picture of the neuronal
functioning behind this rivalry.
Visual Attention on the Sun: What Do Existing Models Actually Predict?
Authors: Jia Li, Daowei Li, Kui Fu, Long Xu
Date: 2018-11-25 00:00:00
Tasks: Benchmarking, Deep Attention

Visual attention prediction is a classic problem that seems to be well
addressed in the deep learning era. One compelling concern, however, gradually
arise along with the rapidly growing performance scores over existing visual
attention datasets: do existing deep models really capture the inherent
mechanism of human visual attention? To address this concern, this paper
proposes a new dataset, named VASUN, that records the free-viewing human
attention on solar images. Different from previous datasets, images in VASUN
contain many irregular visual patterns that existing deep models have never
seen. By benchmarking existing models on VASUN, we find the performances of
many state-of-the-art deep models drop remarkably, while many classic shallow
models perform impressively. From these results, we find that the significant
performance advance of existing deep attention models may come from their
capabilities of memorizing and predicting the occurrence of some specific
visual patterns other than learning the inherent mechanism of human visual
attention. In addition, we also train several baseline models on VASUN to
demonstrate the feasibility and key issues of predicting visual attention on
the sun. These baseline models, together with the proposed dataset, can be used
to revisit the problem of visual attention prediction from a novel perspective
that are complementary to existing ones.
A pooling based scene text proposal technique for scene text reading in the wild
Authors: Dinh NguyenVan, Shijian Lu, Shangxuan Tian, Nizar Ouarti, Mounir Mokhtari
Date: 2018-11-25 00:00:00
Tasks: Scene Understanding, Text Spotting

Automatic reading texts in scenes has attracted increasing interest in recent
years as texts often carry rich semantic information that is useful for scene
understanding. In this paper, we propose a novel scene text proposal technique
aiming for accurate reading texts in scenes. Inspired by the pooling layer in
the deep neural network architecture, a pooling based scene text proposal
technique is developed. A novel score function is designed which exploits the
histogram of oriented gradients and is capable of ranking the proposals
according to their probabilities of being text. An end-to-end scene text
reading system has also been developed by incorporating the proposed scene text
proposal technique where false alarms elimination and words recognition are
performed simultaneously. Extensive experiments over several public datasets
show that the proposed technique can handle multi-orientation and
multi-language scene texts and obtains outstanding proposal performance. The
developed end-to-end systems also achieve very competitive scene text spotting
and reading performance.
Non-local RoI for Cross-Object Perception
Authors: Shou-Yao Roy Tseng, Hwann-Tzong Chen, Shao-Heng Tai, Tyng-Luh Liu
Date: 2018-11-25 00:00:00
Tasks: Instance Segmentation, Object, object-detection, Object Detection, Region Proposal, Segmentation, Semantic Segmentation

We present a generic and flexible module that encodes region proposals by
both their intrinsic features and the extrinsic correlations to the others. The
proposed non-local region of interest (NL-RoI) can be seamlessly adapted into
different generalized R-CNN architectures to better address various perception
tasks. Observe that existing techniques from R-CNN treat RoIs independently and
perform the prediction solely based on image features within each region
proposal. However, the pairwise relationships between proposals could further
provide useful information for detection and segmentation. NL-RoI is thus
formulated to enrich each RoI representation with the information from all
other RoIs, and yield a simple, low-cost, yet effective module for region-based
convolutional networks. Our experimental results show that NL-RoI can improve
the performance of Faster/Mask R-CNN for object detection and instance
segmentation.
Low-resolution Face Recognition in the Wild via Selective Knowledge Distillation
Authors: Shiming Ge, Shengwei Zhao, Chenyu Li, Jia Li
Date: 2018-11-25 00:00:00
Tasks: CPU, Face Model, Face Recognition, GPU, Knowledge Distillation

Typically, the deployment of face recognition models in the wild needs to
identify low-resolution faces with extremely low computational cost. To address
this problem, a feasible solution is compressing a complex face model to
achieve higher speed and lower memory at the cost of minimal performance drop.
Inspired by that, this paper proposes a learning approach to recognize
low-resolution faces via selective knowledge distillation. In this approach, a
two-stream convolutional neural network (CNN) is first initialized to recognize
high-resolution faces and resolution-degraded faces with a teacher stream and a
student stream, respectively. The teacher stream is represented by a complex
CNN for high-accuracy recognition, and the student stream is represented by a
much simpler CNN for low-complexity recognition. To avoid significant
performance drop at the student stream, we then selectively distil the most
informative facial features from the teacher stream by solving a sparse graph
optimization problem, which are then used to regularize the fine-tuning process
of the student stream. In this way, the student stream is actually trained by
simultaneously handling two tasks with limited computational resources:
approximating the most informative facial cues via feature regression, and
recovering the missing facial cues via low-resolution face classification.
Experimental results show that the student stream performs impressively in
recognizing low-resolution faces and costs only 0.15MB memory and runs at 418
faces per second on CPU and 9,433 faces per second on GPU.
Learning Conditional Random Fields with Augmented Observations for Partially Observed Action Recognition
Authors: Shih-Yao Lin, Yen-Yu Lin, Chu-Song Chen, Yi-Ping Hung
Date: 2018-11-25 00:00:00
Tasks: Action Recognition, Temporal Action Localization

This paper aims at recognizing partially observed human actions in videos.
Action videos acquired in uncontrolled environments often contain corrupt
frames, which make actions partially observed. Furthermore, these frames can
last for arbitrary lengths of time and appear irregularly. They are
inconsistent with training data and degrade the performance of pre-trained
action recognition systems. We present an approach to address this issue. For
each training and testing actions, we divide it into segments and explore the
mutual dependency between temporal segments. This property states that the
similarity of two actions at one segment often implies their similarity at
another. We augment each segment with extra alternatives retrieved from
training data. The augmentation algorithm is designed in a way where a few
alternatives are good enough to replace the original segment where corrupt
frames occur. Our approach is developed upon hidden conditional random fields
and leverages the flexibility of hidden variables for uncertainty handling. It
turns out that our approach integrates corrupt segment detection and
alternative selection into the process of prediction, and can recognize
partially observed actions more accurately. It is evaluated on both fully
observed actions and partially observed ones with either synthetic or real
corrupt frames. The experimental results manifest its general applicability and
superior performance, especially when corrupt frames are present in the action
videos.
Poisoning Behavioral Malware Clustering
Authors: Battista Biggio, Konrad Rieck, Davide Ariu, Christian Wressnegger, Igino Corona, Giorgio Giacinto, Fabio Roli
Date: 2018-11-25 00:00:00
Tasks: Clustering, Computer Security, Malware Clustering

Clustering algorithms have become a popular tool in computer security to
analyze the behavior of malware variants, identify novel malware families, and
generate signatures for antivirus systems. However, the suitability of
clustering algorithms for security-sensitive settings has been recently
questioned by showing that they can be significantly compromised if an attacker
can exercise some control over the input data. In this paper, we revisit this
problem by focusing on behavioral malware clustering approaches, and
investigate whether and to what extent an attacker may be able to subvert these
approaches through a careful injection of samples with poisoning behavior. To
this end, we present a case study on Malheur, an open-source tool for
behavioral malware clustering. Our experiments not only demonstrate that this
tool is vulnerable to poisoning attacks, but also that it can be significantly
compromised even if the attacker can only inject a very small percentage of
attacks into the input data. As a remedy, we discuss possible countermeasures
and highlight the need for more secure clustering algorithms.
Is Data Clustering in Adversarial Settings Secure?
Authors: Battista Biggio, Ignazio Pillai, Samuel Rota Bulò, Davide Ariu, Marcello Pelillo, Fabio Roli
Date: 2018-11-25 00:00:00
Tasks: Clustering

Clustering algorithms have been increasingly adopted in security applications
to spot dangerous or illicit activities. However, they have not been originally
devised to deal with deliberate attack attempts that may aim to subvert the
clustering process itself. Whether clustering can be safely adopted in such
settings remains thus questionable. In this work we propose a general framework
that allows one to identify potential attacks against clustering algorithms,
and to evaluate their impact, by making specific assumptions on the adversary's
goal, knowledge of the attacked system, and capabilities of manipulating the
input data. We show that an attacker may significantly poison the whole
clustering process by adding a relatively small percentage of attack samples to
the input data, and that some attack samples may be obfuscated to be hidden
within some existing clusters. We present a case study on single-linkage
hierarchical clustering, and report experiments on clustering of malware
samples and handwritten digits.
Robust Super-Level Set Estimation using Gaussian Processes
Authors: Andrea Zanette, Junzi Zhang, Mykel J. Kochenderfer
Date: 2018-11-25 00:00:00
Tasks: Gaussian Processes

This paper focuses on the problem of determining as large a region as
possible where a function exceeds a given threshold with high probability. We
assume that we only have access to a noise-corrupted version of the function
and that function evaluations are costly. To select the next query point, we
propose maximizing the expected volume of the domain identified as above the
threshold as predicted by a Gaussian process, robustified by a variance term.
We also give asymptotic guarantees on the exploration effect of the algorithm,
regardless of the prior misspecification. We show by various numerical examples
that our approach also outperforms existing techniques in the literature in
practice.
Sequential Variational Autoencoders for Collaborative Filtering
Authors: Noveen Sachdeva, Giuseppe Manco, Ettore Ritacco, Vikram Pudi
Date: 2018-11-25 00:00:00
Tasks: Recommendation Systems

Variational autoencoders were proven successful in domains such as computer
vision and speech processing. Their adoption for modeling user preferences is
still unexplored, although recently it is starting to gain attention in the
current literature. In this work, we propose a model which extends variational
autoencoders by exploiting the rich information present in the past preference
history. We introduce a recurrent version of the VAE, where instead of passing
a subset of the whole history regardless of temporal dependencies, we rather
pass the consumption sequence subset through a recurrent neural network. At
each time-step of the RNN, the sequence is fed through a series of
fully-connected layers, the output of which models the probability distribution
of the most likely future preferences. We show that handling temporal
information is crucial for improving the accuracy of the VAE: In fact, our
model beats the current state-of-the-art by valuable margins because of its
ability to capture temporal dependencies among the user-consumption sequence
using the recurrent encoder still keeping the fundamentals of variational
autoencoders intact.
Temporal Bilinear Networks for Video Action Recognition
Authors: Yanghao Li, Sijie Song, Yuqi Li, Jiaying Liu
Date: 2018-11-25 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Temporal modeling in videos is a fundamental yet challenging problem in
computer vision. In this paper, we propose a novel Temporal Bilinear (TB) model
to capture the temporal pairwise feature interactions between adjacent frames.
Compared with some existing temporal methods which are limited in linear
transformations, our TB model considers explicit quadratic bilinear
transformations in the temporal domain for motion evolution and sequential
relation modeling. We further leverage the factorized bilinear model in linear
complexity and a bottleneck network design to build our TB blocks, which also
constrains the parameters and computation cost. We consider two schemes in
terms of the incorporation of TB blocks and the original 2D spatial
convolutions, namely wide and deep Temporal Bilinear Networks (TBN). Finally,
we perform experiments on several widely adopted datasets including Kinetics,
UCF101 and HMDB51. The effectiveness of our TBNs is validated by comprehensive
ablation analyses and comparisons with various state-of-the-art methods.