pwc-archive/papers-with-abstracts huggingface dataset:
Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking
Authors: Cong Ma, Changshui Yang, Fan Yang, Yueqing Zhuang, Ziwei Zhang, Huizhu Jia, Xiaodong Xie
Date: 2018-04-12 00:00:00
Tasks: Autonomous Driving, Multi-Object Tracking, Multiple Object Tracking, Object, Object Tracking

Multi-Object Tracking (MOT) is a challenging task in the complex scene such
as surveillance and autonomous driving. In this paper, we propose a novel
tracklet processing method to cleave and re-connect tracklets on crowd or
long-term occlusion by Siamese Bi-Gated Recurrent Unit (GRU). The tracklet
generation utilizes object features extracted by CNN and RNN to create the
high-confidence tracklet candidates in sparse scenario. Due to mis-tracking in
the generation process, the tracklets from different objects are split into
several sub-tracklets by a bidirectional GRU. After that, a Siamese GRU based
tracklet re-connection method is applied to link the sub-tracklets which belong
to the same object to form a whole trajectory. In addition, we extract the
tracklet images from existing MOT datasets and propose a novel dataset to train
our networks. The proposed dataset contains more than 95160 pedestrian images.
It has 793 different persons in it. On average, there are 120 images for each
person with positions and sizes. Experimental results demonstrate the
advantages of our model over the state-of-the-art methods on MOT16.
Seed-Point Based Geometric Partitioning of Nuclei Clumps
Authors: James Kapaldo
Date: 2018-04-12 00:00:00

When applying automatic analysis of fluorescence or histopathological images
of cells, it is necessary to partition, or de-clump, partially overlapping cell
nuclei. In this work, I describe a method of partitioning partially overlapping
cell nuclei using a seed-point based geometric partitioning. The geometric
partitioning creates two different types of cuts, cuts between two boundary
vertices and cuts between one boundary vertex and a new vertex introduced to
the boundary interior. The cuts are then ranked according to a scoring metric,
and the highest scoring cuts are used. This method was tested on a set of 2420
clumps of nuclei and was found to produced better results than current popular
analysis software.
End-to-end Deep Learning of Optical Fiber Communications
Authors: Boris Karanov, Mathieu Chagnon, Félix Thouin, Tobias A. Eriksson, Henning Bülow, Domaniç Lavery, Polina Bayvel, Laurent Schmalen
Date: 2018-04-11 00:00:00
Tasks: Deep Learning

In this paper, we implement an optical fiber communication system as an
end-to-end deep neural network, including the complete chain of transmitter,
channel model, and receiver. This approach enables the optimization of the
transceiver in a single end-to-end process. We illustrate the benefits of this
method by applying it to intensity modulation/direct detection (IM/DD) systems
and show that we can achieve bit error rates below the 6.7\% hard-decision
forward error correction (HD-FEC) threshold. We model all componentry of the
transmitter and receiver, as well as the fiber channel, and apply deep learning
to find transmitter and receiver configurations minimizing the symbol error
rate. We propose and verify in simulations a training method that yields robust
and flexible transceivers that allow---without reconfiguration---reliable
transmission over a large range of link dispersions. The results from
end-to-end deep learning are successfully verified for the first time in an
experiment. In particular, we achieve information rates of 42\,Gb/s below the
HD-FEC threshold at distances beyond 40\,km. We find that our results
outperform conventional IM/DD solutions based on 2 and 4 level pulse amplitude
modulation (PAM2/PAM4) with feedforward equalization (FFE) at the receiver. Our
study is the first step towards end-to-end deep learning-based optimization of
optical fiber communication systems.
Online convex optimization and no-regret learning: Algorithms, guarantees and applications
Authors: E. Veronica Belmega, Panayotis Mertikopoulos, Romain Negrel, Luca Sanguinetti
Date: 2018-04-12 00:00:00
Tasks: Metric Learning

Spurred by the enthusiasm surrounding the "Big Data" paradigm, the
mathematical and algorithmic tools of online optimization have found widespread
use in problems where the trade-off between data exploration and exploitation
plays a predominant role. This trade-off is of particular importance to several
branches and applications of signal processing, such as data mining,
statistical inference, multimedia indexing and wireless communications (to name
but a few). With this in mind, the aim of this tutorial paper is to provide a
gentle introduction to online optimization and learning algorithms that are
asymptotically optimal in hindsight - i.e., they approach the performance of a
virtual algorithm with unlimited computational power and full knowledge of the
future, a property known as no-regret. Particular attention is devoted to
identifying the algorithms' theoretical performance guarantees and to establish
links with classic optimization paradigms (both static and stochastic). To
allow a better understanding of this toolbox, we provide several examples
throughout the tutorial ranging from metric learning to wireless resource
allocation problems.
EventKG: A Multilingual Event-Centric Temporal Knowledge Graph
Authors: Simon Gottschalk, Elena Demidova
Date: 2018-04-12 00:00:00
Tasks: Knowledge Graphs

One of the key requirements to facilitate semantic analytics of information
regarding contemporary and historical events on the Web, in the news and in
social media is the availability of reference knowledge repositories containing
comprehensive representations of events and temporal relations. Existing
knowledge graphs, with popular examples including DBpedia, YAGO and Wikidata,
focus mostly on entity-centric information and are insufficient in terms of
their coverage and completeness with respect to events and temporal relations.
EventKG presented in this paper is a multilingual event-centric temporal
knowledge graph that addresses this gap. EventKG incorporates over 690 thousand
contemporary and historical events and over 2.3 million temporal relations
extracted from several large-scale knowledge graphs and semi-structured sources
and makes them available through a canonical representation.
Simultaneous Fidelity and Regularization Learning for Image Restoration
Authors: Dongwei Ren, WangMeng Zuo, David Zhang, Lei Zhang, Ming-Hsuan Yang
Date: 2018-04-12 00:00:00
Tasks: Denoising, Image Deconvolution, Image Restoration

Most existing non-blind restoration methods are based on the assumption that a precise degradation model is known. As the degradation process can only be partially known or inaccurately modeled, images may not be well restored. Rain streak removal and image deconvolution with inaccurate blur kernels are two representative examples of such tasks. For rain streak removal, although an input image can be decomposed into a scene layer and a rain streak layer, there exists no explicit formulation for modeling rain streaks and the composition with scene layer. For blind deconvolution, as estimation error of blur kernel is usually introduced, the subsequent non-blind deconvolution process does not restore the latent image well. In this paper, we propose a principled algorithm within the maximum a posterior framework to tackle image restoration with a partially known or inaccurate degradation model. Specifically, the residual caused by a partially known or inaccurate degradation model is spatially dependent and complexly distributed. With a training set of degraded and ground-truth image pairs, we parameterize and learn the fidelity term for a degradation model in a task-driven manner. Furthermore, the regularization term can also be learned along with the fidelity term, thereby forming a simultaneous fidelity and regularization learning model. Extensive experimental results demonstrate the effectiveness of the proposed model for image deconvolution with inaccurate blur kernels, deconvolution with multiple degradations and rain streak removal.
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images
Authors: Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab
Date: 2018-04-12 00:00:00
Tasks: Anatomy, Anomaly Detection, Anomaly Segmentation, Clustering, Representation Learning, Retrieval, Unsupervised Anomaly Detection

Reliably modeling normality and differentiating abnormal appearances from
normal cases is a very appealing approach for detecting pathologies in medical
images. A plethora of such unsupervised anomaly detection approaches has been
made in the medical domain, based on statistical methods, content-based
retrieval, clustering and recently also deep learning. Previous approaches
towards deep unsupervised anomaly detection model patches of normal anatomy
with variants of Autoencoders or GANs, and detect anomalies either as outliers
in the learned feature space or from large reconstruction errors. In contrast
to these patch-based approaches, we show that deep spatial autoencoding models
can be efficiently used to capture normal anatomical variability of entire 2D
brain MR images. A variety of experiments on real MR data containing MS lesions
corroborates our hypothesis that we can detect and even delineate anomalies in
brain MR images by simply comparing input images to their reconstruction.
Results show that constraints on the latent space and adversarial training can
further improve the segmentation performance over standard deep representation
learning.
PCN: Part and Context Information for Pedestrian Detection with CNNs
Authors: Shiguang Wang, Jian Cheng, Haijun Liu, Ming Tang
Date: 2018-04-12 00:00:00
Tasks: Occlusion Handling, Pedestrian Detection

Pedestrian detection has achieved great improvements in recent years, while
complex occlusion handling is still one of the most important problems. To take
advantage of the body parts and context information for pedestrian detection,
we propose the part and context network (PCN) in this work. PCN specially
utilizes two branches which detect the pedestrians through body parts semantic
and context information, respectively. In the Part Branch, the semantic
information of body parts can communicate with each other via recurrent neural
networks. In the Context Branch, we adopt a local competition mechanism for
adaptive context scale selection. By combining the outputs of all branches, we
develop a strong complementary pedestrian detector with a lower miss rate and
better localization accuracy, especially for occlusion pedestrian.
Comprehensive evaluations on two challenging pedestrian detection datasets
(i.e. Caltech and INRIA) well demonstrated the effectiveness of the proposed
PCN.
Learning Multilingual Embeddings for Cross-Lingual Information Retrieval in the Presence of Topically Aligned Corpora
Authors: Mitodru Niyogi, Kripabandhu Ghosh, Arnab Bhattacharya
Date: 2018-04-12 00:00:00
Tasks: Cross-Lingual Information Retrieval, Information Retrieval, Retrieval, Sentence

Cross-lingual information retrieval is a challenging task in the absence of
aligned parallel corpora. In this paper, we address this problem by considering
topically aligned corpora designed for evaluating an IR setup. To emphasize, we
neither use any sentence-aligned corpora or document-aligned corpora, nor do we
use any language specific resources such as dictionary, thesaurus, or grammar
rules. Instead, we use an embedding into a common space and learn word
correspondences directly from there. We test our proposed approach for
bilingual IR on standard FIRE datasets for Bangla, Hindi and English. The
proposed method is superior to the state-of-the-art method not only for IR
evaluation measures but also in terms of time requirements. We extend our
method successfully to the trilingual setting.
Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly
Authors: Yongqin Xian, Christoph H. Lampert, Bernt Schiele, Zeynep Akata
Date: 2017-07-03 00:00:00
Tasks: Zero-Shot Learning

Due to the importance of zero-shot learning, i.e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g. pre-training on zero-shot test classes. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it.
Generative models for local network community detection
Authors: Twan van Laarhoven
Date: 2018-04-12 00:00:00
Tasks: Community Detection, Local Community Detection, Stochastic Block Model

Local network community detection aims to find a single community in a large
network, while inspecting only a small part of that network around a given seed
node. This is much cheaper than finding all communities in a network. Most
methods for local community detection are formulated as ad-hoc optimization
problems. In this work, we instead start from a generative model for networks
with community structure. By assuming that the network is uniform, we can
approximate the structure of unobserved parts of the network to obtain a method
for local community detection. We apply this local approximation technique to
two variants of the stochastic block model. To our knowledge, this results in
the first local community detection methods based on probabilistic models.
Interestingly, in the limit, one of the proposed approximations corresponds to
conductance, a popular metric in this field. Experiments on real and synthetic
datasets show comparable or improved results compared to state-of-the-art local
community detection algorithms.
Feature Generating Networks for Zero-Shot Learning
Authors: Yongqin Xian, Tobias Lorenz, Bernt Schiele, Zeynep Akata
Date: 2017-12-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: Generalized Zero-Shot Learning, Generative Adversarial Network, Zero-Shot Learning

Suffering from the extreme training data imbalance between seen and unseen
classes, most of existing state-of-the-art approaches fail to achieve
satisfactory results for the challenging generalized zero-shot learning task.
To circumvent the need for labeled examples of unseen classes, we propose a
novel generative adversarial network (GAN) that synthesizes CNN features
conditioned on class-level semantic information, offering a shortcut directly
from a semantic descriptor of a class to a class-conditional feature
distribution. Our proposed approach, pairing a Wasserstein GAN with a
classification loss, is able to generate sufficiently discriminative CNN
features to train softmax classifiers or any multimodal embedding method. Our
experimental results demonstrate a significant boost in accuracy over the state
of the art on five challenging datasets -- CUB, FLO, SUN, AWA and ImageNet --
in both the zero-shot learning and generalized zero-shot learning settings.
CubeNet: Equivariance to 3D Rotation and Translation
Authors: Daniel Worrall, Gabriel Brostow
Date: 2018-04-12 00:00:00
Proceeding: ECCV 2018 9
Tasks: General Classification, Translation

3D Convolutional Neural Networks are sensitive to transformations applied to
their input. This is a problem because a voxelized version of a 3D object, and
its rotated clone, will look unrelated to each other after passing through to
the last layer of a network. Instead, an idealized model would preserve a
meaningful representation of the voxelized object, while explaining the
pose-difference between the two inputs. An equivariant representation vector
has two components: the invariant identity part, and a discernable encoding of
the transformation. Models that can't explain pose-differences risk "diluting"
the representation, in pursuit of optimizing a classification or regression
loss function.
  We introduce a Group Convolutional Neural Network with linear equivariance to
translations and right angle rotations in three dimensions. We call this
network CubeNet, reflecting its cube-like symmetry. By construction, this
network helps preserve a 3D shape's global and local signature, as it is
transformed through successive layers. We apply this network to a variety of 3D
inference problems, achieving state-of-the-art on the ModelNet10 classification
challenge, and comparable performance on the ISBI 2012 Connectome Segmentation
Benchmark. To the best of our knowledge, this is the first 3D rotation
equivariant CNN for voxel representations.
Solving Bongard Problems with a Visual Language and Pragmatic Reasoning
Authors: Stefan Depeweg, Constantin A. Rothkopf, Frank Jäkel
Date: 2018-04-12 00:00:00
Tasks: Bayesian Inference

More than 50 years ago Bongard introduced 100 visual concept learning
problems as a testbed for intelligent vision systems. These problems are now
known as Bongard problems. Although they are well known in the cognitive
science and AI communities only moderate progress has been made towards
building systems that can solve a substantial subset of them. In the system
presented here, visual features are extracted through image processing and then
translated into a symbolic visual vocabulary. We introduce a formal language
that allows representing complex visual concepts based on this vocabulary.
Using this language and Bayesian inference, complex visual concepts can be
induced from the examples that are provided in each Bongard problem. Contrary
to other concept learning problems the examples from which concepts are induced
are not random in Bongard problems, instead they are carefully chosen to
communicate the concept, hence requiring pragmatic reasoning. Taking pragmatic
reasoning into account we find good agreement between the concepts with high
posterior probability and the solutions formulated by Bongard himself. While
this approach is far from solving all Bongard problems, it solves the biggest
fraction yet.
3D G-CNNs for Pulmonary Nodule Detection
Authors: Marysia Winkels, Taco S. Cohen
Date: 2018-04-12 00:00:00
Tasks: Data Augmentation, Translation

Convolutional Neural Networks (CNNs) require a large amount of annotated data
to learn from, which is often difficult to obtain in the medical domain. In
this paper we show that the sample complexity of CNNs can be significantly
improved by using 3D roto-translation group convolutions (G-Convs) instead of
the more conventional translational convolutions. These 3D G-CNNs were applied
to the problem of false positive reduction for pulmonary nodule detection, and
proved to be substantially more effective in terms of performance, sensitivity
to malignant nodules, and speed of convergence compared to a strong and
comparable baseline architecture with regular convolutions, data augmentation
and a similar number of parameters. For every dataset size tested, the G-CNN
achieved a FROC score close to the CNN trained on ten times more data.
Adversarial Alignment of Class Prediction Uncertainties for Domain Adaptation
Authors: Jeroen Manders, Twan van Laarhoven, Elena Marchiori
Date: 2018-04-12 00:00:00
Tasks: Domain Adaptation, image-classification, Image Classification, Prediction, Unsupervised Domain Adaptation

We consider unsupervised domain adaptation: given labelled examples from a
source domain and unlabelled examples from a related target domain, the goal is
to infer the labels of target examples. Under the assumption that features from
pre-trained deep neural networks are transferable across related domains,
domain adaptation reduces to aligning source and target domain at class
prediction uncertainty level. We tackle this problem by introducing a method
based on adversarial learning which forces the label uncertainty predictions on
the target domain to be indistinguishable from those on the source domain.
Pre-trained deep neural networks are used to generate deep features having high
transferability across related domains. We perform an extensive experimental
analysis of the proposed method over a wide set of publicly available
pre-trained deep neural networks. Results of our experiments on domain
adaptation tasks for image classification show that class prediction
uncertainty alignment with features extracted from pre-trained deep neural
networks provides an efficient, robust and effective method for domain
adaptation.
Temporal Interpolation via Motion Field Prediction
Authors: Lin Zhang, Neerav Karani, Christine Tanner, Ender Konukoglu
Date: 2018-04-12 00:00:00
Tasks: 4D reconstruction, Prediction, Specificity

Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging enables high
contrast 4D MR imaging during free breathing and provides in-vivo observations
for treatment planning and guidance. Navigator slices are vital for
retrospective stacking of 2D data slices in this method. However, they also
prolong the acquisition sessions. Temporal interpolation of navigator slices an
be used to reduce the number of navigator acquisitions without degrading
specificity in stacking. In this work, we propose a convolutional neural
network (CNN) based method for temporal interpolation via motion field
prediction. The proposed formulation incorporates the prior knowledge that a
motion field underlies changes in the image intensities over time. Previous
approaches that interpolate directly in the intensity space are prone to
produce blurry images or even remove structures in the images. Our method
avoids such problems and faithfully preserves the information in the image.
Further, an important advantage of our formulation is that it provides an
unsupervised estimation of bi-directional motion fields. We show that these
motion fields can be used to halve the number of registrations required during
4D reconstruction, thus substantially reducing the reconstruction time.
Successor Features for Transfer in Reinforcement Learning
Authors: André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, Hado van Hasselt, David Silver
Date: 2016-06-16 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Transfer in reinforcement learning refers to the notion that generalization
should occur not only within a task but also across tasks. We propose a
transfer framework for the scenario where the reward function changes between
tasks but the environment's dynamics remain the same. Our approach rests on two
key ideas: "successor features", a value function representation that decouples
the dynamics of the environment from the rewards, and "generalized policy
improvement", a generalization of dynamic programming's policy improvement
operation that considers a set of policies rather than a single one. Put
together, the two ideas lead to an approach that integrates seamlessly within
the reinforcement learning framework and allows the free exchange of
information across tasks. The proposed method also provides performance
guarantees for the transferred policy even before any learning has taken place.
We derive two theorems that set our approach in firm theoretical ground and
present experiments that show that it successfully promotes transfer in
practice, significantly outperforming alternative methods in a sequence of
navigation tasks and in the control of a simulated robotic arm.