pwc-archive/papers-with-abstracts huggingface dataset:
HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning
Authors: Thomas Robert, Nicolas Thome, Matthieu Cord
Date: 2018-07-30 00:00:00
Proceeding: ECCV 2018 9
Tasks: Classification, Decoder, General Classification, Image Classification

In this paper, we introduce a new model for leveraging unlabeled data to
improve generalization performances of image classifiers: a two-branch
encoder-decoder architecture called HybridNet. The first branch receives
supervision signal and is dedicated to the extraction of invariant
class-related representations. The second branch is fully unsupervised and
dedicated to model information discarded by the first branch to reconstruct
input data. To further support the expected behavior of our model, we propose
an original training objective. It favors stability in the discriminative
branch and complementarity between the learned representations in the two
branches. HybridNet is able to outperform state-of-the-art results on CIFAR-10,
SVHN and STL-10 in various semi-supervised settings. In addition,
visualizations and ablation studies validate our contributions and the behavior
of the model on both CIFAR-10 and STL-10 datasets.
Preference-based Online Learning with Dueling Bandits: A Survey
Authors: Viktor Bengs, Robert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier
Date: 2018-07-30 00:00:00
Tasks: Multi-Armed Bandits, Survey

In machine learning, the notion of multi-armed bandits refers to a class of online learning problems, in which an agent is supposed to simultaneously explore and exploit a given set of choice alternatives in the course of a sequential decision process. In the standard setting, the agent learns from stochastic feedback in the form of real-valued rewards. In many applications, however, numerical reward signals are not readily available -- instead, only weaker information is provided, in particular relative preferences in the form of qualitative comparisons between pairs of alternatives. This observation has motivated the study of variants of the multi-armed bandit problem, in which more general representations are used both for the type of feedback to learn from and the target of prediction. The aim of this paper is to provide a survey of the state of the art in this field, referred to as preference-based multi-armed bandits or dueling bandits. To this end, we provide an overview of problems that have been considered in the literature as well as methods for tackling them. Our taxonomy is mainly based on the assumptions made by these methods about the data-generating process and, related to this, the properties of the preference-based feedback.
Making Classifier Chains Resilient to Class Imbalance
Authors: Bin Liu, Grigorios Tsoumakas
Date: 2018-07-30 00:00:00
Tasks: Multi-Label Learning

Class imbalance is an intrinsic characteristic of multi-label data. Most of
the labels in multi-label data sets are associated with a small number of
training examples, much smaller compared to the size of the data set. Class
imbalance poses a key challenge that plagues most multi-label learning methods.
Ensemble of Classifier Chains (ECC), one of the most prominent multi-label
learning methods, is no exception to this rule, as each of the binary models it
builds is trained from all positive and negative examples of a label. To make
ECC resilient to class imbalance, we first couple it with random undersampling.
We then present two extensions of this basic approach, where we build a varying
number of binary models per label and construct chains of different sizes, in
order to improve the exploitation of majority examples with approximately the
same computational budget. Experimental results on 16 multi-label datasets
demonstrate the effectiveness of the proposed approaches in a variety of
evaluation metrics.
Multi-bin Trainable Linear Unit for Fast Image Restoration Networks
Authors: Shuhang Gu, Radu Timofte, Luc van Gool
Date: 2018-07-30 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration, Super-Resolution

Tremendous advances in image restoration tasks such as denoising and
super-resolution have been achieved using neural networks. Such approaches
generally employ very deep architectures, large number of parameters, large
receptive fields and high nonlinear modeling capacity. In order to obtain
efficient and fast image restoration networks one should improve upon the above
mentioned requirements.
  In this paper we propose a novel activation function, the multi-bin trainable
linear unit (MTLU), for increasing the nonlinear modeling capacity together
with lighter and shallower networks. We validate the proposed fast image
restoration networks for image denoising (FDnet) and super-resolution (FSRnet)
on standard benchmarks. We achieve large improvements in both memory and
runtime over current state-of-the-art for comparable or better PSNR accuracies.
Small Organ Segmentation in Whole-body MRI using a Two-stage FCN and Weighting Schemes
Authors: Vanya V. Valindria, Ioannis Lavdas, Juan Cerrolaza, Eric O. Aboagye, Andrea G. Rockall, Daniel Rueckert, Ben Glocker
Date: 2018-07-30 00:00:00
Tasks: Organ Segmentation, Segmentation

Accurate and robust segmentation of small organs in whole-body MRI is
difficult due to anatomical variation and class imbalance. Recent deep network
based approaches have demonstrated promising performance on abdominal
multi-organ segmentations. However, the performance on small organs is still
suboptimal as these occupy only small regions of the whole-body volumes with
unclear boundaries and variable shapes. A coarse-to-fine, hierarchical strategy
is a common approach to alleviate this problem, however, this might miss useful
contextual information. We propose a two-stage approach with weighting schemes
based on auto-context and spatial atlas priors. Our experiments show that the
proposed approach can boost the segmentation accuracy of multiple small organs
in whole-body MRI scans.
Fairly Allocating Many Goods with Few Queries
Authors: Hoon Oh, Ariel D. Procaccia, Warut Suksompong
Date: 2018-07-30 00:00:00

We investigate the query complexity of the fair allocation of indivisible goods. For two agents with arbitrary monotonic utilities, we design an algorithm that computes an allocation satisfying envy-freeness up to one good (EF1), a relaxation of envy-freeness, using a logarithmic number of queries. We show that the logarithmic query complexity bound also holds for three agents with additive utilities, and that a polylogarithmic bound holds for three agents with monotonic utilities. These results suggest that it is possible to fairly allocate goods in practice even when the number of goods is extremely large. By contrast, we prove that computing an allocation satisfying envy-freeness and another of its relaxations, envy-freeness up to any good (EFX), requires a linear number of queries even when there are only two agents with identical additive utilities.
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking
Authors: Tianyang Xu, Zhen-Hua Feng, Xiao-Jun Wu, Josef Kittler
Date: 2018-07-30 00:00:00
Tasks: Benchmarking, feature selection, Object Tracking, Video Object Tracking, Visual Tracking

With efficient appearance learning models, Discriminative Correlation Filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions. However, the existing DCF paradigm suffers from two major issues, i.e., spatial boundary effect and temporal filter degradation. To mitigate these challenges, we propose a new DCF-based tracking method. The key innovations of the proposed method include adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning in a lower dimensional discriminative manifold. More specifically, we apply structured spatial sparsity constraints to multi-channel filers. Consequently, the process of learning spatial filters can be approximated by the lasso regularisation. To encourage temporal consistency, the filter model is restricted to lie around its historical value and updated locally to preserve the global structure in the manifold. Last, a unified optimisation framework is proposed to jointly select temporal consistency preserving spatial features and learn discriminative filters with the augmented Lagrangian method. Qualitative and quantitative evaluations have been conducted on a number of well-known benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123 and VOT2018. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art approaches.
Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators
Authors: Gonçalo Mordido, Haojin Yang, Christoph Meinel
Date: 2018-07-30 00:00:00
Tasks: Diversity

We propose to incorporate adversarial dropout in generative multi-adversarial networks, by omitting or dropping out, the feedback of each discriminator in the framework with some probability at the end of each batch. Our approach forces the single generator not to constrain its output to satisfy a single discriminator, but, instead, to satisfy a dynamic ensemble of discriminators. We show that this leads to a more generalized generator, promoting variety in the generated samples and avoiding the common mode collapse problem commonly experienced with generative adversarial networks (GANs). We further provide evidence that the proposed framework, named Dropout-GAN, promotes sample diversity both within and across epochs, eliminating mode collapse and stabilizing training.
Unsupervised Domain Adaptive Re-Identification: Theory and Practice
Authors: Liangchen Song, Cheng Wang, Lefei Zhang, Bo Du, Qian Zhang, Chang Huang, Xinggang Wang
Date: 2018-07-30 00:00:00
Tasks: General Classification, Unsupervised Domain Adaptation

We study the problem of unsupervised domain adaptive re-identification
(re-ID) which is an active topic in computer vision but lacks a theoretical
foundation. We first extend existing unsupervised domain adaptive
classification theories to re-ID tasks. Concretely, we introduce some
assumptions on the extracted feature space and then derive several loss
functions guided by these assumptions. To optimize them, a novel self-training
scheme for unsupervised domain adaptive re-ID tasks is proposed. It iteratively
makes guesses for unlabeled target data based on an encoder and trains the
encoder based on the guessed labels. Extensive experiments on unsupervised
domain adaptive person re-ID and vehicle re-ID tasks with comparisons to the
state-of-the-arts confirm the effectiveness of the proposed theories and
self-training framework. Our code is available at
\url{https://github.com/LcDog/DomainAdaptiveReID}.
Action Detection from a Robot-Car Perspective
Authors: Valentina Fontana, Gurkirt Singh, Stephen Akrigg, Manuele Di Maio, Suman Saha, Fabio Cuzzolin
Date: 2018-07-30 00:00:00
Tasks: Action Detection, Activity Detection, Autonomous Driving, BIG-bench Machine Learning, Decision Making

We present the new Road Event and Activity Detection (READ) dataset, designed
and created from an autonomous vehicle perspective to take action detection
challenges to autonomous driving. READ will give scholars in computer vision,
smart cars and machine learning at large the opportunity to conduct research
into exciting new problems such as understanding complex (road) activities,
discerning the behaviour of sentient agents, and predicting both the label and
the location of future actions and events, with the final goal of supporting
autonomous decision making.
Kernel Density Estimation-Based Markov Models with Hidden State
Authors: Gustav Eje Henter, Arne Leijon, W. Bastiaan Kleijn
Date: 2018-07-30 00:00:00
Tasks: Density Estimation, Time Series Analysis

We consider Markov models of stochastic processes where the next-step
conditional distribution is defined by a kernel density estimator (KDE),
similar to Markov forecast densities and certain time-series bootstrap schemes.
The KDE Markov models (KDE-MMs) we discuss are nonlinear, nonparametric, fully
probabilistic representations of stationary processes, based on techniques with
strong asymptotic consistency properties. The models generate new data by
concatenating points from the training data sequences in a context-sensitive
manner, together with some additive driving noise. We present novel EM-type
maximum-likelihood algorithms for data-driven bandwidth selection in KDE-MMs.
Additionally, we augment the KDE-MMs with a hidden state, yielding a new model
class, KDE-HMMs. The added state variable captures non-Markovian long memory
and signal structure (e.g., slow oscillations), complementing the short-range
dependences described by the Markov process. The resulting joint Markov and
hidden-Markov structure is appealing for modelling complex real-world processes
such as speech signals. We present guaranteed-ascent EM-update equations for
model parameters in the case of Gaussian kernels, as well as relaxed update
formulas that greatly accelerate training in practice. Experiments demonstrate
increased held-out set probability for KDE-HMMs on several challenging natural
and synthetic data series, compared to traditional techniques such as
autoregressive models, HMMs, and their combinations.
Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning
Authors: Uta Büchler, Biagio Brattoli, Björn Ommer
Date: 2018-07-30 00:00:00
Proceeding: ECCV 2018 9
Tasks: Deep Reinforcement Learning, General Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval, Self-Supervised Learning, Transfer Learning, Video Classification

Self-supervised learning of convolutional neural networks can harness large
amounts of cheap unlabeled data to train powerful feature representations. As
surrogate task, we jointly address ordering of visual data in the spatial and
temporal domain. The permutations of training samples, which are at the core of
self-supervision by ordering, have so far been sampled randomly from a fixed
preselected set. Based on deep reinforcement learning we propose a sampling
policy that adapts to the state of the network, which is being trained.
Therefore, new permutations are sampled according to their expected utility for
updating the convolutional feature representation. Experimental evaluation on
unsupervised and transfer learning tasks demonstrates competitive performance
on standard benchmarks for image and video classification and nearest neighbor
retrieval.
Unsupervised Domain Adaptation by Adversarial Learning for Robust Speech Recognition
Authors: Pavel Denisov, Ngoc Thang Vu, Marc Ferras Font
Date: 2018-07-30 00:00:00
Tasks: Domain Adaptation, Robust Speech Recognition, speech-recognition, Speech Recognition, Unsupervised Domain Adaptation

In this paper, we investigate the use of adversarial learning for
unsupervised adaptation to unseen recording conditions, more specifically,
single microphone far-field speech. We adapt neural networks based acoustic
models trained with close-talk clean speech to the new recording conditions
using untranscribed adaptation data. Our experimental results on Italian
SPEECON data set show that our proposed method achieves 19.8% relative word
error rate (WER) reduction compared to the unadapted models. Furthermore, this
adaptation method is beneficial even when performed on data from another
language (i.e. French) giving 12.6% relative WER reduction.
Self-Calibration of Cameras with Euclidean Image Plane in Case of Two Views and Known Relative Rotation Angle
Authors: Evgeniy Martyushev
Date: 2018-07-30 00:00:00
Proceeding: ECCV 2018 9

The internal calibration of a pinhole camera is given by five parameters that
are combined into an upper-triangular $3\times 3$ calibration matrix. If the
skew parameter is zero and the aspect ratio is equal to one, then the camera is
said to have Euclidean image plane. In this paper, we propose a non-iterative
self-calibration algorithm for a camera with Euclidean image plane in case the
remaining three internal parameters --- the focal length and the principal
point coordinates --- are fixed but unknown. The algorithm requires a set of $N
\geq 7$ point correspondences in two views and also the measured relative
rotation angle between the views. We show that the problem generically has six
solutions (including complex ones).
  The algorithm has been implemented and tested both on synthetic data and on
publicly available real dataset. The experiments demonstrate that the method is
correct, numerically stable and robust.
Graphene: Semantically-Linked Propositions in Open Information Extraction
Authors: Matthias Cetto, Christina Niklaus, André Freitas, Siegfried Handschuh
Date: 2018-07-30 00:00:00
Proceeding: COLING 2018 8
Tasks: Open Information Extraction

We present an Open Information Extraction (IE) approach that uses a
two-layered transformation stage consisting of a clausal disembedding layer and
a phrasal disembedding layer, together with rhetorical relation identification.
In that way, we convert sentences that present a complex linguistic structure
into simplified, syntactically sound sentences, from which we can extract
propositions that are represented in a two-layered hierarchy in the form of
core relational tuples and accompanying contextual information which are
semantically linked via rhetorical relations. In a comparative evaluation, we
demonstrate that our reference implementation Graphene outperforms
state-of-the-art Open IE systems in the construction of correct n-ary
predicate-argument structures. Moreover, we show that existing Open IE
approaches can benefit from the transformation process of our framework.
Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors
Authors: Katarína Tóthová, Sarah Parisot, Matthew C. H. Lee, Esther Puyol-Antón, Lisa M. Koch, Andrew P. King, Ender Konukoglu, Marc Pollefeys
Date: 2018-07-30 00:00:00
Tasks: Medical Image Analysis, Probabilistic Deep Learning, Surface Reconstruction, Uncertainty Quantification

Surface reconstruction is a vital tool in a wide range of areas of medical
image analysis and clinical research. Despite the fact that many methods have
proposed solutions to the reconstruction problem, most, due to their
deterministic nature, do not directly address the issue of quantifying
uncertainty associated with their predictions. We remedy this by proposing a
novel probabilistic deep learning approach capable of simultaneous surface
reconstruction and associated uncertainty prediction. The method incorporates
prior shape information in the form of a principal component analysis (PCA)
model. Experiments using the UK Biobank data show that our probabilistic
approach outperforms an analogous deterministic PCA-based method in the task of
2D organ delineation and quantifies uncertainty by formulating distributions
over predicted surface vertex positions.
Extreme Network Compression via Filter Group Approximation
Authors: Bo Peng, Wenming Tan, Zheyang Li, Shun Zhang, Di Xie, ShiLiang Pu
Date: 2018-07-30 00:00:00
Proceeding: ECCV 2018 9
Tasks: General Classification, image-classification, Image Classification

In this paper we propose a novel decomposition method based on filter group
approximation, which can significantly reduce the redundancy of deep
convolutional neural networks (CNNs) while maintaining the majority of feature
representation. Unlike other low-rank decomposition algorithms which operate on
spatial or channel dimension of filters, our proposed method mainly focuses on
exploiting the filter group structure for each layer. For several commonly used
CNN models, including VGG and ResNet, our method can reduce over 80%
floating-point operations (FLOPs) with less accuracy drop than state-of-the-art
methods on various image classification datasets. Besides, experiments
demonstrate that our method is conducive to alleviating degeneracy of the
compressed network, which hurts the convergence and performance of the network.
Modular Sensor Fusion for Semantic Segmentation
Authors: Hermann Blum, Abel Gawel, Roland Siegwart, Cesar Cadena
Date: 2018-07-30 00:00:00
Tasks: Segmentation, Semantic Segmentation, Sensor Fusion

Sensor fusion is a fundamental process in robotic systems as it extends the
perceptual range and increases robustness in real-world operations. Current
multi-sensor deep learning based semantic segmentation approaches do not
provide robustness to under-performing classes in one modality, or require a
specific architecture with access to the full aligned multi-sensor training
data. In this work, we analyze statistical fusion approaches for semantic
segmentation that overcome these drawbacks while keeping a competitive
performance. The studied approaches are modular by construction, allowing to
have different training sets per modality and only a much smaller subset is
needed to calibrate the statistical models. We evaluate a range of statistical
fusion approaches and report their performance against state-of-the-art
baselines on both real-world and simulated data. In our experiments, the
approach improves performance in IoU over the best single modality segmentation
results by up to 5%. We make all implementations and configurations publicly
available.
Recurrently Exploring Class-wise Attention in A Hybrid Convolutional and Bidirectional LSTM Network for Multi-label Aerial Image Classification
Authors: Yuansheng Hua, Lichao Mou, Xiao Xiang Zhu
Date: 2018-07-30 00:00:00
Tasks: General Classification, image-classification, Image Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Aerial image classification is of great significance in remote sensing community, and many researches have been conducted over the past few years. Among these studies, most of them focus on categorizing an image into one semantic label, while in the real world, an aerial image is often associated with multiple labels, e.g., multiple object-level labels in our case. Besides, a comprehensive picture of present objects in a given high resolution aerial image can provide more in-depth understanding of the studied region. For these reasons, aerial image multi-label classification has been attracting increasing attention. However, one common limitation shared by existing methods in the community is that the co-occurrence relationship of various classes, so called class dependency, is underexplored and leads to an inconsiderate decision. In this paper, we propose a novel end-to-end network, namely class-wise attention-based convolutional and bidirectional LSTM network (CA-Conv-BiLSTM), for this task. The proposed network consists of three indispensable components: 1) a feature extraction module, 2) a class attention learning layer, and 3) a bidirectional LSTM-based sub-network. Particularly, the feature extraction module is designed for extracting fine-grained semantic feature maps, while the class attention learning layer aims at capturing discriminative class-specific features. As the most important part, the bidirectional LSTM-based sub-network models the underlying class dependency in both directions and produce structured multiple object labels. Experimental results on UCM multi-label dataset and DFC15 multi-label dataset validate the effectiveness of our model quantitatively and qualitatively.