pwc-archive/papers-with-abstracts huggingface dataset:
Localized Trajectories for 2D and 3D Action Recognition
Authors: Konstantinos Papadopoulos, Girum Demisse, Enjie Ghorbel, Michel Antunes, Djamila Aouada, Björn Ottersten
Date: 2019-04-10 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Temporal Action Localization

The Dense Trajectories concept is one of the most successful approaches in
action recognition, suitable for scenarios involving a significant amount of
motion. However, due to noise and background motion, many generated
trajectories are irrelevant to the actual human activity and can potentially
lead to performance degradation. In this paper, we propose Localized
Trajectories as an improved version of Dense Trajectories where motion
trajectories are clustered around human body joints provided by RGB-D cameras
and then encoded by local Bag-of-Words. As a result, the Localized Trajectories
concept provides a more discriminative representation of actions as compared to
Dense Trajectories. Moreover, we generalize Localized Trajectories to 3D by
using the modalities offered by RGB-D cameras. One of the main advantages of
using RGB-D data to generate trajectories is that they include radial
displacements that are perpendicular to the image plane. Extensive experiments
and analysis are carried out on five different datasets.
Curriculum semi-supervised segmentation
Authors: Hoel Kervadec, Jose Dolz, Eric Granger, Ismail Ben Ayed
Date: 2019-04-10 00:00:00
Tasks: Left Ventricle Segmentation, regression, Segmentation, Semantic Segmentation, Semi-Supervised Semantic Segmentation

This study investigates a curriculum-style strategy for semi-supervised CNN segmentation, which devises a regression network to learn image-level information such as the size of a target region. These regressions are used to effectively regularize the segmentation network, constraining softmax predictions of the unlabeled images to match the inferred label distributions. Our framework is based on inequality constraints that tolerate uncertainties with inferred knowledge, e.g., regressed region size, and can be employed for a large variety of region attributes. We evaluated our proposed strategy for left ventricle segmentation in magnetic resonance images (MRI), and compared it to standard proposal-based semi-supervision strategies. Our strategy leverages unlabeled data in more efficiently, and achieves very competitive results, approaching the performance of full-supervision.
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes
Authors: Alexey Romanov, Maria De-Arteaga, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Tauman Kalai
Date: 2019-04-10 00:00:00
Proceeding: NAACL 2019 6
Tasks: Word Embeddings

There is a growing body of work that proposes methods for mitigating bias in
machine learning systems. These methods typically rely on access to protected
attributes such as race, gender, or age. However, this raises two significant
challenges: (1) protected attributes may not be available or it may not be
legal to use them, and (2) it is often desirable to simultaneously consider
multiple protected attributes, as well as their intersections. In the context
of mitigating bias in occupation classification, we propose a method for
discouraging correlation between the predicted probability of an individual's
true occupation and a word embedding of their name. This method leverages the
societal biases that are encoded in word embeddings, eliminating the need for
access to protected attributes. Crucially, it only requires access to
individuals' names at training time and not at deployment time. We evaluate two
variations of our proposed method using a large-scale dataset of online
biographies. We find that both variations simultaneously reduce race and gender
biases, with almost no reduction in the classifier's overall true positive
rate.
Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process
Authors: Jaza M. Abdullah, Tarik A. Rashid
Date: 2019-04-10 00:00:00
Tasks: Decision Making

In this paper, a novel swarm intelligent algorithm is proposed, known as the
fitness dependent optimizer (FDO). The bee swarming reproductive process and
their collective decision-making have inspired this algorithm; it has no
algorithmic connection with the honey bee algorithm or the artificial bee
colony algorithm. It is worth mentioning that FDO is considered a particle
swarm optimization (PSO)-based algorithm that updates the search agent position
by adding velocity (pace). However, FDO calculates velocity differently; it
uses the problem fitness function value to produce weights, and these weights
guide the search agents during both the exploration and exploitation phases.
Throughout the paper, the FDO algorithm is presented, and the motivation behind
the idea is explained. Moreover, FDO is tested on a group of 19 classical
benchmark test functions, and the results are compared with three well-known
algorithms: PSO, the genetic algorithm (GA), and the dragonfly algorithm (DA),
additionally, FDO is tested on IEEE Congress of Evolutionary Computation
Benchmark Test Functions (CEC-C06, 2019 Competition) [1]. The results are
compared with three modern algorithms: (DA), the whale optimization algorithm
(WOA), and the salp swarm algorithm (SSA). The FDO results show better
performance in most cases and comparative results in other cases. Furthermore,
the results are statistically tested with the Wilcoxon rank-sum test to show
the significance of the results. Likewise, FDO stability in both the
exploration and exploitation phases is verified and performance-proofed using
different standard measurements. Finally, FDO is applied to real-world
applications as evidence of its feasibility.
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features
Authors: Arno Solin, Manon Kok
Date: 2019-04-10 00:00:00
Tasks: Gaussian Processes, General Classification, regression

Gaussian processes (GPs) provide a powerful framework for extrapolation,
interpolation, and noise removal in regression and classification. This paper
considers constraining GPs to arbitrarily-shaped domains with boundary
conditions. We solve a Fourier-like generalised harmonic feature representation
of the GP prior in the domain of interest, which both constrains the GP and
attains a low-rank representation that is used for speeding up inference. The
method scales as $\mathcal{O}(nm^2)$ in prediction and $\mathcal{O}(m^3)$ in
hyperparameter learning for regression, where $n$ is the number of data points
and $m$ the number of features. Furthermore, we make use of the variational
approach to allow the method to deal with non-Gaussian likelihoods. The
experiments cover both simulated and empirical data in which the boundary
conditions allow for inclusion of additional physical information.
Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events
Authors: Hongwei Song, Jiqing Han, Shiwen Deng, Zhihao Du
Date: 2019-04-10 00:00:00
Tasks: Acoustic Scene Classification, Classification, General Classification, Scene Classification

In this paper, we propose a new strategy for acoustic scene classification
(ASC) , namely recognizing acoustic scenes through identifying distinct sound
events. This differs from existing strategies, which focus on characterizing
global acoustical distributions of audio or the temporal evolution of
short-term audio features, without analysis down to the level of sound events.
To identify distinct sound events for each scene, we formulate ASC in a
multi-instance learning (MIL) framework, where each audio recording is mapped
into a bag-of-instances representation. Here, instances can be seen as
high-level representations for sound events inside a scene. We also propose a
MIL neural networks model, which implicitly identifies distinct instances
(i.e., sound events). Furthermore, we propose two specially designed modules
that model the multi-temporal scale and multi-modal natures of the sound events
respectively. The experiments were conducted on the official development set of
the DCASE2018 Task1 Subtask B, and our best-performing model improves over the
official baseline by 9.4% (68.3% vs 58.9%) in terms of classification accuracy.
This study indicates that recognizing acoustic scenes by identifying distinct
sound events is effective and paves the way for future studies that combine
this strategy with previous ones.
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification
Authors: Cheng Deng, Xianglong Liu, Chao Li, DaCheng Tao
Date: 2019-04-10 00:00:00
Tasks: Active Learning, Classification, Domain Adaptation, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Recent years have witnessed the quick progress of the hyperspectral images
(HSI) classification. Most of existing studies either heavily rely on the
expensive label information using the supervised learning or can hardly exploit
the discriminative information borrowed from related domains. To address this
issues, in this paper we show a novel framework addressing HSI classification
based on the domain adaptation (DA) with active learning (AL). The main idea of
our method is to retrain the multi-kernel classifier by utilizing the available
labeled samples from source domain, and adding minimum number of the most
informative samples with active queries in the target domain. The proposed
method adaptively combines multiple kernels, forming a DA classifier that
minimizes the bias between the source and target domains. Further equipped with
the nested actively updating process, it sequentially expands the training set
and gradually converges to a satisfying level of classification performance. We
study this active adaptation framework with the Margin Sampling (MS) strategy
in the HSI classification task. Our experimental results on two popular HSI
datasets demonstrate its effectiveness.
Weakly-Supervised White and Grey Matter Segmentation in 3D Brain Ultrasound
Authors: Beatrice Demiray, Julia Rackerseder, Stevica Bozhinoski, Nassir Navab
Date: 2019-04-10 00:00:00
Tasks: Transfer Learning

Although the segmentation of brain structures in ultrasound helps initialize
image based registration, assist brain shift compensation, and provides
interventional decision support, the task of segmenting grey and white matter
in cranial ultrasound is very challenging and has not been addressed yet. We
train a multi-scale fully convolutional neural network simultaneously for two
classes in order to segment real clinical 3D ultrasound data. Parallel pathways
working at different levels of resolution account for high frequency speckle
noise and global 3D image features. To ensure reproducibility, the publicly
available RESECT dataset is utilized for training and cross-validation. Due to
the absence of a ground truth, we train with weakly annotated label. We
implement label transfer from MRI to US, which is prone to a residual but
inevitable registration error. To further improve results, we perform transfer
learning using synthetic US data. The resulting method leads to excellent Dice
scores of 0.7080, 0.8402 and 0.9315 for grey matter, white matter and
background. Our proposed methodology sets an unparalleled standard for white
and grey matter segmentation in 3D intracranial ultrasound.
A Reproducing Kernel Hilbert Space log-rank test for the two-sample problem
Authors: Tamara Fernandez, Nicolas Rivera
Date: 2019-04-10 00:00:00

Weighted log-rank tests are arguably the most widely used tests by practitioners for the two-sample problem in the context of right-censored data. Many approaches have been considered to make weighted log-rank tests more robust against a broader family of alternatives, among them, considering linear combinations of weighted log-rank tests, and taking the maximum among a finite collection of them. In this paper, we propose as test statistic the supremum of a collection of (potentially infinite) weight-indexed log-rank tests where the index space is the unit ball in a reproducing kernel Hilbert space (RKHS). By using some desirable properties of RKHSs we provide an exact and simple evaluation of the test statistic and establish connections with previous tests in the literature. Additionally, we show that for a special family of RKHSs, the proposed test is omnibus. We finalise by performing an empirical evaluation of the proposed methodology and show an application to a real data scenario. Our theoretical results are proved using techniques for double integrals with respect to martingales that may be of independent interest.
Black-box Adversarial Attacks on Video Recognition Models
Authors: Linxi Jiang, Xingjun Ma, Shaoxiang Chen, James Bailey, Yu-Gang Jiang
Date: 2019-04-10 00:00:00
Tasks: Video Recognition

Deep neural networks (DNNs) are known for their vulnerability to adversarial examples. These are examples that have undergone small, carefully crafted perturbations, and which can easily fool a DNN into making misclassifications at test time. Thus far, the field of adversarial research has mainly focused on image models, under either a white-box setting, where an adversary has full access to model parameters, or a black-box setting where an adversary can only query the target model for probabilities or labels. Whilst several white-box attacks have been proposed for video models, black-box video attacks are still unexplored. To close this gap, we propose the first black-box video attack framework, called V-BAD. V-BAD utilizes tentative perturbations transferred from image models, and partition-based rectifications found by the NES on partitions (patches) of tentative perturbations, to obtain good adversarial gradient estimates with fewer queries to the target model. V-BAD is equivalent to estimating the projection of an adversarial gradient on a selected subspace. Using three benchmark video datasets, we demonstrate that V-BAD can craft both untargeted and targeted attacks to fool two state-of-the-art deep video recognition models. For the targeted attack, it achieves $>$93\% success rate using only an average of $3.4 \sim 8.4 \times 10^4$ queries, a similar number of queries to state-of-the-art black-box image attacks. This is despite the fact that videos often have two orders of magnitude higher dimensionality than static images. We believe that V-BAD is a promising new tool to evaluate and improve the robustness of video recognition models to black-box adversarial attacks.
Causal Embeddings for Recommendation: An Extended Abstract
Authors: Stephen Bonner, Flavian vasile
Date: 2019-04-10 00:00:00
Tasks: Domain Adaptation

Recommendations are commonly used to modify user's natural behavior, for example, increasing product sales or the time spent on a website. This results in a gap between the ultimate business objective and the classical setup where recommendations are optimized to be coherent with past user behavior. To bridge this gap, we propose a new learning setup for recommendation that optimizes for the Incremental Treatment Effect (ITE) of the policy. We show this is equivalent to learning to predict recommendation outcomes under a fully random recommendation policy and propose a new domain adaptation algorithm that learns from logged data containing outcomes from a biased recommendation policy and predicts recommendation outcomes according to random exposure. We compare our method against state-of-the-art factorization methods, in addition to new approaches of causal recommendation and show significant improvements.
Large-Scale Long-Tailed Recognition in an Open World
Authors: Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, Stella X. Yu
Date: 2019-04-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Classification, Few-Shot Learning, General Classification, imbalanced classification, Long-tail Learning, Long-tail learning with class descriptors, Open Set Learning

Real world data often have a long-tailed and open-ended distribution. A
practical recognition system must classify among majority and minority classes,
generalize from a few known instances, and acknowledge novelty upon a never
seen instance. We define Open Long-Tailed Recognition (OLTR) as learning from
such naturally distributed data and optimizing the classification accuracy over
a balanced test set which include head, tail, and open classes. OLTR must
handle imbalanced classification, few-shot learning, and open-set recognition
in one integrated algorithm, whereas existing classification approaches focus
only on one aspect and deliver poorly over the entire class spectrum. The key
challenges are how to share visual knowledge between head and tail classes and
how to reduce confusion between tail and open classes. We develop an integrated
OLTR algorithm that maps an image to a feature space such that visual concepts
can easily relate to each other based on a learned metric that respects the
closed-world classification while acknowledging the novelty of the open world.
Our so-called dynamic meta-embedding combines a direct image feature and an
associated memory feature, with the feature norm indicating the familiarity to
known classes. On three large-scale OLTR datasets we curate from object-centric
ImageNet, scene-centric Places, and face-centric MS1M data, our method
consistently outperforms the state-of-the-art. Our code, datasets, and models
enable future OLTR research and are publicly available at
https://liuziwei7.github.io/projects/LongTail.html.
Joint Manifold Diffusion for Combining Predictions on Decoupled Observations
Authors: Kwang In Kim, Hyung Jin Chang
Date: 2019-04-10 00:00:00
Proceeding: CVPR 2019 6

We present a new predictor combination algorithm that improves a given task
predictor based on potentially relevant reference predictors. Existing
approaches are limited in that, to discover the underlying task dependence,
they either require known parametric forms of all predictors or access to a
single fixed dataset on which all predictors are jointly evaluated. To overcome
these limitations, we design a new non-parametric task dependence estimation
procedure that automatically aligns evaluations of heterogeneous predictors
across disjoint feature sets. Our algorithm is instantiated as a robust
manifold diffusion process that jointly refines the estimated predictor
alignments and the corresponding task dependence. We apply this algorithm to
the relative attributes ranking problem and demonstrate that it not only
broadens the application range of predictor combination approaches but also
outperforms existing methods even when applied to classical predictor
combination settings.
NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier
Authors: Alessandro Seganti, Helena Sobol, Iryna Orlova, Hannam Kim, Jakub Staniszewski, Tymoteusz Krumholc, Krystian Koziel
Date: 2019-04-10 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Position, Sentence

The paper presents a system developed for the SemEval-2019 competition Task 5
hat-Eval Basile et al. (2019) (team name: LU Team) and Task 6 OffensEval
Zampieri et al. (2019b) (team name: NLPR@SRPOL), where we achieved 2nd position
in Subtask C. The system combines in an ensemble several models (LSTM,
Transformer, OpenAI's GPT, Random forest, SVM) with various embeddings (custom,
ELMo, fastText, Universal Encoder) together with additional linguistic features
(number of blacklisted words, special characters, etc.). The system works with
a multi-tier blacklist and a large corpus of crawled data, annotated for
general offensiveness. In the paper we do an extensive analysis of our results
and show how the combination of features and embedding affect the performance
of the models.
Actor-Critic Instance Segmentation
Authors: Nikita Araslanov, Constantin Rothkopf, Stefan Roth
Date: 2019-04-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Instance Segmentation, Reinforcement Learning, Segmentation, Semantic Segmentation

Most approaches to visual scene analysis have emphasised parallel processing
of the image elements. However, one area in which the sequential nature of
vision is apparent, is that of segmenting multiple, potentially similar and
partially occluded objects in a scene. In this work, we revisit the recurrent
formulation of this challenging problem in the context of reinforcement
learning. Motivated by the limitations of the global max-matching assignment of
the ground-truth segments to the recurrent states, we develop an actor-critic
approach in which the actor recurrently predicts one instance mask at a time
and utilises the gradient from a concurrently trained critic network. We
formulate the state, action, and the reward such as to let the critic model
long-term effects of the current prediction and incorporate this information
into the gradient signal. Furthermore, to enable effective exploration in the
inherently high-dimensional action space of instance masks, we learn a compact
representation using a conditional variational auto-encoder. We show that our
actor-critic model consistently provides accuracy benefits over the recurrent
baseline on standard instance segmentation benchmarks.
Predicting Novel Views Using Generative Adversarial Query Network
Authors: Phong Nguyen-Ha, Lam Huynh, Esa Rahtu, Janne Heikkila
Date: 2019-04-10 00:00:00
Tasks: Decoder, Novel View Synthesis

The problem of predicting a novel view of the scene using an arbitrary number
of observations is a challenging problem for computers as well as for humans.
This paper introduces the Generative Adversarial Query Network (GAQN), a
general learning framework for novel view synthesis that combines Generative
Query Network (GQN) and Generative Adversarial Networks (GANs). The
conventional GQN encodes input views into a latent representation that is used
to generate a new view through a recurrent variational decoder. The proposed
GAQN builds on this work by adding two novel aspects: First, we extend the
current GQN architecture with an adversarial loss function for improving the
visual quality and convergence speed. Second, we introduce a feature-matching
loss function for stabilizing the training procedure. The experiments
demonstrate that GAQN is able to produce high-quality results and faster
convergence compared to the conventional approach.
Text Guided Person Image Synthesis
Authors: Xingran Zhou, Siyu Huang, Bin Li, Yingming Li, Jiachen Li, Zhongfei Zhang
Date: 2019-04-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Attribute, Image Generation, Visual Question Answering (VQA)

This paper presents a novel method to manipulate the visual appearance (pose
and attribute) of a person image according to natural language descriptions.
Our method can be boiled down to two stages: 1) text guided pose generation and
2) visual appearance transferred image synthesis. In the first stage, our
method infers a reasonable target human pose based on the text. In the second
stage, our method synthesizes a realistic and appearance transferred person
image according to the text in conjunction with the target pose. Our method
extracts sufficient information from the text and establishes a mapping between
the image space and the language space, making generating and editing images
corresponding to the description possible. We conduct extensive experiments to
reveal the effectiveness of our method, as well as using the VQA Perceptual
Score as a metric for evaluating the method. It shows for the first time that
we can automatically edit the person image from the natural language
descriptions.