pwc-archive/papers-with-abstracts huggingface dataset:
Facial Landmark Machines: A Backbone-Branches Architecture with Progressive Representation Learning
Authors: Lingbo Liu, Guanbin Li, Yuan Xie, Yizhou Yu, Qing Wang, Liang Lin
Date: 2018-12-10 00:00:00
Tasks: Face Alignment, Face Detection, Face Recognition, Representation Learning

Facial landmark localization plays a critical role in face recognition and
analysis. In this paper, we propose a novel cascaded backbone-branches fully
convolutional neural network~(BB-FCN) for rapidly and accurately localizing
facial landmarks in unconstrained and cluttered settings. Our proposed BB-FCN
generates facial landmark response maps directly from raw images without any
preprocessing. BB-FCN follows a coarse-to-fine cascaded pipeline, which
consists of a backbone network for roughly detecting the locations of all
facial landmarks and one branch network for each type of detected landmark for
further refining their locations. Furthermore, to facilitate the facial
landmark localization under unconstrained settings, we propose a large-scale
benchmark named SYSU16K, which contains 16000 faces with large variations in
pose, expression, illumination and resolution. Extensive experimental
evaluations demonstrate that our proposed BB-FCN can significantly outperform
the state-of-the-art under both constrained (i.e., within detected facial
regions only) and unconstrained settings. We further confirm that high-quality
facial landmarks localized with our proposed network can also improve the
precision and recall of face detection.
Automatic Classification of Knee Rehabilitation Exercises Using a Single Inertial Sensor: a Case Study
Authors: Antonio Bevilacqua, Bingquan Huang, Rob Argent, Brian Caulfield, Tahar Kechadi
Date: 2018-12-10 00:00:00
Tasks: General Classification

Inertial measurement units have the ability to accurately record the
acceleration and angular velocity of human limb segments during discrete joint
movements. These movements are commonly used in exercise rehabilitation
programmes following orthopaedic surgery such as total knee replacement. This
provides the potential for a biofeedback system with data mining technique for
patients undertaking exercises at home without physician supervision. We
propose to use machine learning techniques to automatically analyse inertial
measurement unit data collected during these exercises, and then assess whether
each repetition of the exercise was executed correctly or not. Our approach
consists of two main phases: signal segmentation, and segment classification.
Accurate pre-processing and feature extraction are paramount topics in order
for the technique to work. In this paper, we present a classification method
for unsupervised rehabilitation exercises, based on a segmentation process that
extracts repetitions from a longer signal activity. The results obtained from
experimental datasets of both clinical and healthy subjects, for a set of 4
knee exercises commonly used in rehabilitation, are very promising.
Weakly Supervised Dense Event Captioning in Videos
Authors: Xuguang Duan, Wenbing Huang, Chuang Gan, Jingdong Wang, Wenwu Zhu, Junzhou Huang
Date: 2018-12-10 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Sentence

Dense event captioning aims to detect and describe all events of interest
contained in a video. Despite the advanced development in this area, existing
methods tackle this task by making use of dense temporal annotations, which is
dramatically source-consuming. This paper formulates a new problem: weakly
supervised dense event captioning, which does not require temporal segment
annotations for model training. Our solution is based on the one-to-one
correspondence assumption, each caption describes one temporal segment, and
each temporal segment has one caption, which holds in current benchmark
datasets and most real-world cases. We decompose the problem into a pair of
dual problems: event captioning and sentence localization and present a cycle
system to train our model. Extensive experimental results are provided to
demonstrate the ability of our model on both dense event captioning and
sentence localization in videos.
Occupancy Networks: Learning 3D Reconstruction in Function Space
Authors: Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, Andreas Geiger
Date: 2018-12-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D geometry, 3D Reconstruction, 3D Shape Representation

With the advent of deep neural networks, learning-based approaches for 3D
reconstruction have gained popularity. However, unlike for images, in 3D there
is no canonical representation which is both computationally and memory
efficient yet allows for representing high-resolution geometry of arbitrary
topology. Many of the state-of-the-art learning-based 3D reconstruction
approaches can hence only represent very coarse 3D geometry or are limited to a
restricted domain. In this paper, we propose Occupancy Networks, a new
representation for learning-based 3D reconstruction methods. Occupancy networks
implicitly represent the 3D surface as the continuous decision boundary of a
deep neural network classifier. In contrast to existing approaches, our
representation encodes a description of the 3D output at infinite resolution
without excessive memory footprint. We validate that our representation can
efficiently encode 3D structure and can be inferred from various kinds of
input. Our experiments demonstrate competitive results, both qualitatively and
quantitatively, for the challenging tasks of 3D reconstruction from single
images, noisy point clouds and coarse discrete voxel grids. We believe that
occupancy networks will become a useful tool in a wide variety of
learning-based 3D tasks.
Learning to Drive from Simulation without Real World Labels
Authors: Alex Bewley, Jessica Rigley, Yuxuan Liu, Jeffrey Hawke, Richard Shen, Vinh-Dieu Lam, Alex Kendall
Date: 2018-12-10 00:00:00
Tasks: Image-to-Image Translation, Translation

Simulation can be a powerful tool for understanding machine learning systems
and designing methods to solve real-world problems. Training and evaluating
methods purely in simulation is often "doomed to succeed" at the desired task
in a simulated environment, but the resulting models are incapable of operation
in the real world. Here we present and evaluate a method for transferring a
vision-based lane following driving policy from simulation to operation on a
rural road without any real-world labels. Our approach leverages recent
advances in image-to-image translation to achieve domain transfer while jointly
learning a single-camera control policy from simulation control labels. We
assess the driving performance of this method using both open-loop regression
metrics, and closed-loop performance operating an autonomous vehicle on rural
and urban roads.
Mapping, Localization and Path Planning for Image-based Navigation using Visual Features and Map
Authors: Janine Thoma, Danda Pani Paudel, Ajad Chhatkuli, Thomas Probst, Luc van Gool
Date: 2018-12-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image-Based Localization, Image Retrieval, Retrieval

Building on progress in feature representations for image retrieval, image-based localization has seen a surge of research interest. Image-based localization has the advantage of being inexpensive and efficient, often avoiding the use of 3D metric maps altogether. That said, the need to maintain a large number of reference images as an effective support of localization in a scene, nonetheless calls for them to be organized in a map structure of some kind. The problem of localization often arises as part of a navigation process. We are, therefore, interested in summarizing the reference images as a set of landmarks, which meet the requirements for image-based navigation. A contribution of this paper is to formulate such a set of requirements for the two sub-tasks involved: map construction and self-localization. These requirements are then exploited for compact map representation and accurate self-localization, using the framework of a network flow problem. During this process, we formulate the map construction and self-localization problems as convex quadratic and second-order cone programs, respectively. We evaluate our methods on publicly available indoor and outdoor datasets, where they outperform existing methods significantly.
Unsupervised Deep Learning for Structured Shape Matching
Authors: Jean-Michel Roufosse, Abhishek Sharma, Maks Ovsjanikov
Date: 2018-12-10 00:00:00
Proceeding: ICCV 2019 10
Tasks: Deep Learning

We present a novel method for computing correspondences across 3D shapes using unsupervised learning. Our method computes a non-linear transformation of given descriptor functions, while optimizing for global structural properties of the resulting maps, such as their bijectivity or approximate isometry. To this end, we use the functional maps framework, and build upon the recent FMNet architecture for descriptor learning. Unlike that approach, however, we show that learning can be done in a purely \emph{unsupervised setting}, without having access to any ground truth correspondences. This results in a very general shape matching method that we call SURFMNet for Spectral Unsupervised FMNet, and which can be used to establish correspondences within 3D shape collections without any prior information. We demonstrate on a wide range of challenging benchmarks, that our approach leads to state-of-the-art results compared to the existing unsupervised methods and achieves results that are comparable even to the supervised learning techniques. Moreover, our framework is an order of magnitude faster, and does not rely on geodesic distance computation or expensive post-processing.
Abstracting Causal Models
Authors: Sander Beckers, Joseph Y. Halpern
Date: 2018-12-10 00:00:00

We consider a sequence of successively more restrictive definitions of abstraction for causal models, starting with a notion introduced by Rubenstein et al. (2017) called exact transformation that applies to probabilistic causal models, moving to a notion of uniform transformation that applies to deterministic causal models and does not allow differences to be hidden by the "right" choice of distribution, and then to abstraction, where the interventions of interest are determined by the map from low-level states to high-level states, and strong abstraction, which takes more seriously all potential interventions in a model, not just the allowed interventions. We show that procedures for combining micro-variables into macro-variables are instances of our notion of strong abstraction, as are all the examples considered by Rubenstein et al.
Inflo: News Categorization and Keyphrase Extraction for Implementation in an Aggregation System
Authors: Pranav A, Nick Sukiennik, Pan Hui
Date: 2018-12-10 00:00:00
Tasks: Articles, Keyphrase Extraction

The work herein describes a system for automatic news category and keyphrase
labeling, presented in the context of our motivation to improve the speed at
which a user can find relevant and interesting content within an aggregation
platform. A set of 12 discrete categories were applied to over 500,000 news
articles for training a neural network, to be used to facilitate the more
in-depth task of extracting the most significant keyphrases. The latter was
done using three methods: statistical, graphical and numerical, using the
pre-identified category label to improve relevance of extracted phrases. The
results are presented in a demo in which the articles are pre-populated via
News API, and upon being selected, the category and keyphrase labels will be
computed via the methods explained herein.
Can we learn where people go?
Authors: Marion Gödel, Gerta Köster, Daniel Lehmberg, Manfred Gruber, Angelika Kneidl, Florian Sesser
Date: 2018-12-10 00:00:00
Tasks: Navigate

In most agent-based simulators, pedestrians navigate from origins to
destinations. Consequently, destinations are essential input parameters to the
simulation. While many other relevant parameters as positions, speeds and
densities can be obtained from sensors, like cameras, destinations cannot be
observed directly. Our research question is: Can we obtain this information
from video data using machine learning methods? We use density heatmaps, which
indicate the pedestrian density within a given camera cutout, as input to
predict the destination distributions. For our proof of concept, we train a
Random Forest predictor on an exemplary data set generated with the Vadere
microscopic simulator. The scenario is a crossroad where pedestrians can head
left, straight or right. In addition, we gain first insights on suitable
placement of the camera. The results motivate an in-depth analysis of the
methodology.
Modelling trait dependent speciation with Approximate Bayesian Computation
Authors: Krzysztof Bartoszek, Pietro Liò
Date: 2018-12-10 00:00:00

Phylogeny is the field of modelling the temporal discrete dynamics of
speciation. Complex models can nowadays be studied using the Approximate
Bayesian Computation approach which avoids likelihood calculations. The field's
progression is hampered by the lack of robust software to estimate the numerous
parameters of the speciation process. In this work we present an R package,
pcmabc, based on Approximate Bayesian Computations, that implements three novel
phylogenetic algorithms for trait-dependent speciation modelling. Our
phylogenetic comparative methodology takes into account both the simulated
traits and phylogeny, attempting to estimate the parameters of the processes
generating the phenotype and the trait. The user is not restricted to a
predefined set of models and can specify a variety of evolutionary and
branching models. We illustrate the software with a simulation-reestimation
study focused around the branching Ornstein-Uhlenbeck process, where the
branching rate depends non-linearly on the value of the driving
Ornstein-Uhlenbeck process. Included in this work is a tutorial on how to use
the software.
Ramp-based Twin Support Vector Clustering
Authors: Zhen Wang, Xu Chen, Chun-Na Li, Yuan-Hai Shao
Date: 2018-12-10 00:00:00
Tasks: Clustering

Traditional plane-based clustering methods measure the cost of within-cluster
and between-cluster by quadratic, linear or some other unbounded functions,
which may amplify the impact of cost. This letter introduces a ramp cost
function into the plane-based clustering to propose a new clustering method,
called ramp-based twin support vector clustering (RampTWSVC). RampTWSVC is more
robust because of its boundness, and thus it is more easier to find the
intrinsic clusters than other plane-based clustering methods. The non-convex
programming problem in RampTWSVC is solved efficiently through an alternating
iteration algorithm, and its local solution can be obtained in a finite number
of iterations theoretically. In addition, the nonlinear manifold-based
formation of RampTWSVC is also proposed by kernel trick. Experimental results
on several benchmark datasets show the better performance of our RampTWSVC
compared with other plane-based clustering methods.
Improving Nighttime Retrieval-Based Localization
Authors: Hugo Germain, Guillaume Bourmaud, Vincent Lepetit
Date: 2018-12-10 00:00:00
Tasks: Image Retrieval, Retrieval, Visual Localization

Outdoor visual localization is a crucial component to many computer vision
systems. We propose an approach to localization from images that is designed to
explicitly handle the strong variations in appearance happening between daytime
and nighttime. As revealed by recent long-term localization benchmarks, both
traditional feature-based and retrieval-based approaches still struggle to
handle such changes. Our novel localization method combines a state-of-the-art
image retrieval architecture with condition-specific sub-networks allowing the
computation of global image descriptors that are explicitly dependent of the
capturing conditions. We show that our approach improves localization by a
factor of almost 300\% compared to the popular VLAD-based methods on nighttime
localization.
Defending Against Universal Perturbations With Shared Adversarial Training
Authors: Chaithanya Kumar Mummadi, Thomas Brox, Jan Hendrik Metzen
Date: 2018-12-10 00:00:00
Proceeding: ICCV 2019 10
Tasks: image-classification, Image Classification, Semantic Segmentation

Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space. While adversarial training improves the robustness of image classifiers against such adversarial perturbations, it leaves them sensitive to perturbations on a non-negligible fraction of the inputs. In this work, we show that adversarial training is more effective in preventing universal perturbations, where the same perturbation needs to fool a classifier on many inputs. Moreover, we investigate the trade-off between robustness against universal perturbations and performance on unperturbed data and propose an extension of adversarial training that handles this trade-off more gracefully. We present results for image classification and semantic segmentation to showcase that universal perturbations that fool a model hardened with adversarial training become clearly perceptible and show patterns of the target scene.
SMIT: Stochastic Multi-Label Image-to-Image Translation
Authors: Andrés Romero, Pablo Arbeláez, Luc van Gool, Radu Timofte
Date: 2018-12-10 00:00:00
Tasks: Image-to-Image Translation, Translation

Cross-domain mapping has been a very active topic in recent years. Given one image, its main purpose is to translate it to the desired target domain, or multiple domains in the case of multiple labels. This problem is highly challenging due to three main reasons: (i) unpaired datasets, (ii) multiple attributes, and (iii) the multimodality (e.g., style) associated with the translation. Most of the existing state-of-the-art has focused only on two reasons, i.e. either on (i) and (ii), or (i) and (iii). In this work, we propose a joint framework (i, ii, iii) of diversity and multi-mapping image-to-image translations, using a single generator to conditionally produce countless and unique fake images that hold the underlying characteristics of the source image. Our system does not use style regularization, instead, it uses an embedding representation that we call domain embedding for both domain and style. Extensive experiments over different datasets demonstrate the effectiveness of our proposed approach in comparison with the state-of-the-art in both multi-label and multimodal problems. Additionally, our method is able to generalize under different scenarios: continuous style interpolation, continuous label interpolation, and fine-grained mapping. Code and pretrained models are available at https://github.com/BCV-Uniandes/SMIT.
Taxi Demand-Supply Forecasting: Impact of Spatial Partitioning on the Performance of Neural Networks
Authors: Neema Davis, Gaurav Raina, Krishna Jagannathan
Date: 2018-12-10 00:00:00

In this paper, we investigate the significance of choosing an appropriate
tessellation strategy for a spatio-temporal taxi demand-supply modeling
framework. Our study compares (i) the variable-sized polygon based Voronoi
tessellation, and (ii) the fixed-sized grid based Geohash tessellation, using
taxi demand-supply GPS data for the cities of Bengaluru, India and New York,
USA. Long Short-Term Memory (LSTM) networks are used for modeling and
incorporating information from spatial neighbors into the model. We find that
the LSTM model based on input features extracted from a variable-sized polygon
tessellation yields superior performance over the LSTM model based on
fixed-sized grid tessellation. Our study highlights the need to explore
multiple spatial partitioning techniques for improving the prediction
performance in neural network models.
Guided Graph Spectral Embedding: Application to the C. elegans Connectome
Authors: Miljan Petrović, Thomas A. W. Bolton, Maria Giulia Preti, Raphaël Liégeois, Dimitri Van De Ville
Date: 2018-12-10 00:00:00

Graph spectral analysis can yield meaningful embeddings of graphs by
providing insight into distributed features not directly accessible in nodal
domain. Recent efforts in graph signal processing have proposed new
decompositions-e.g., based on wavelets and Slepians-that can be applied to
filter signals defined on the graph. In this work, we take inspiration from
these constructions to define a new guided spectral embedding that combines
maximizing energy concentration with minimizing modified embedded distance for
a given importance weighting of the nodes. We show these optimization goals are
intrinsically opposite, leading to a well-defined and stable spectral
decomposition. The importance weighting allows to put the focus on particular
nodes and tune the trade-off between global and local effects. Following the
derivation of our new optimization criterion and its linear approximation, we
exemplify the methodology on the C. elegans structural connectome. The results
of our analyses confirm known observations on the nematode's neural network in
terms of functionality and importance of cells. Compared to Laplacian
embedding, the guided approach, focused on a certain class of cells (sensory,
inter- and motoneurons), provides more biological insights, such as the
distinction between somatic positions of cells, and their involvement in low or
high order processing functions.
Neural Probabilistic System for Text Recognition
Authors: Najoua Rahal, Maroua Tounsi, Adel M. ALIMI
Date: 2018-12-10 00:00:00
Tasks: Optical Character Recognition (OCR)

Unconstrained text recognition is a stimulating field in the branch of pattern recognition. This field is still an open search due to the unlimited vocabulary, multi styles, mixed-font and their great morphological variability. Recent trends show a potential improvement of recognition by adoption a novel representation of extracted features. In the present paper, we propose a novel feature extraction model by learning a Bag of Features Framework for text recognition based on Sparse Auto-Encoder. The Hidden Markov Models are then used for sequences modeling. For features learned quality evaluation, our proposed system was tested on two printed text datasets PKHATT text line images and APTI word images benchmark. Our method achieves promising recognition on both datasets.
Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions
Authors: Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, Fei Sha
Date: 2018-12-10 00:00:00
Proceeding: CVPR 2020 6
Tasks: Few-Shot Image Classification, Few-Shot Learning, General Classification, Generalized Few-Shot Learning, Transfer Learning

Learning with limited data is a key challenge for visual recognition. Many few-shot learning methods address this challenge by learning an instance embedding function from seen classes and apply the function to instances from unseen classes with limited labels. This style of transfer learning is task-agnostic: the embedding function is not learned optimally discriminative with respect to the unseen classes, where discerning among them leads to the target task. In this paper, we propose a novel approach to adapt the instance embeddings to the target classification task with a set-to-set function, yielding embeddings that are task-specific and are discriminative. We empirically investigated various instantiations of such set-to-set functions and observed the Transformer is most effective -- as it naturally satisfies key properties of our desired model. We denote this model as FEAT (few-shot embedding adaptation w/ Transformer) and validate it on both the standard few-shot classification benchmark and four extended few-shot learning settings with essential use cases, i.e., cross-domain, transductive, generalized few-shot learning, and low-shot learning. It archived consistent improvements over baseline models as well as previous methods and established the new state-of-the-art results on two benchmarks.