pwc-archive/papers-with-abstracts huggingface dataset:
The Verbal and Non Verbal Signals of Depression -- Combining Acoustics, Text and Visuals for Estimating Depression Level
Authors: Syed Arbaaz Qureshi, Mohammed Hasanuzzaman, Sriparna Saha, Gaël Dias
Date: 2019-04-02 00:00:00

Depression is a serious medical condition that is suffered by a large number
of people around the world. It significantly affects the way one feels, causing
a persistent lowering of mood. In this paper, we propose a novel
attention-based deep neural network which facilitates the fusion of various
modalities. We use this network to regress the depression level. Acoustic, text
and visual modalities have been used to train our proposed network. Various
experiments have been carried out on the benchmark dataset, namely, Distress
Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). From the results, we
empirically justify that the fusion of all three modalities helps in giving the
most accurate estimation of depression level. Our proposed approach outperforms
the state-of-the-art by 7.17% on root mean squared error (RMSE) and 8.08% on
mean absolute error (MAE).
Topological based classification of paper domains using graph convolutional networks
Authors: Idan Benami, Keren Cohen, Oved Nagar, yoram louzoun
Date: 2019-04-10 00:00:00
Tasks: Classification, General Classification, Node Classification

The main approaches for node classification in graphs are information
propagation and the association of the class of the node with external
information. State of the art methods merge these approaches through Graph
Convolutional Networks. We here use the association of topological features of
the nodes with their class to predict this class. Moreover, combining
topological information with information propagation improves classification
accuracy on the standard CiteSeer and Cora paper classification task.
Topological features and information propagation produce results almost as good
as text-based classification, without no textual or content information. We
propose to represent the topology and information propagation through a GCN
with the neighboring training node classification as an input and the current
node classification as output. Such a formalism outperforms state of the art
methods.
A Single Attention-Based Combination of CNN and RNN for Relation Classification
Authors: XIAOYU GUO1, HUI ZHANG1, 2, HAIJUN YANG 3, LIANYUAN XU4, AND ZHIWEN YE1
Date: 2019-02-06 00:00:00
Proceeding: IEEE Access 7: 12467-12475 (2019) 2019 2
Tasks: General Classification, Relation, Relation Classification, Sentence

As a vital task in natural language processing, relation classification aims to identify relation types between entities from texts. In this paper, we propose a novel Att-RCNN model to extract text features and classify relations by combining recurrent neural network (RNN) and convolutional neural network (CNN). This network structure utilizes RNN to extract higher level contextual representations of words and CNN to obtain sentence features for the relation classification task. In addition to this network structure, both word-level and sentence-level attention mechanisms are employed in Att-RCNN to strengthen critical words and features to promote the model performance. Moreover, we conduct experiments on four distinct datasets: SemEval-2010 task 8, SemEval-2018 task 7 (two subtask datasets), and KBP37 dataset. Compared with the previous public models, Att-RCNN has the overall best performance and achieves the highest F1 score, especially on the KBP37 dataset.
Convolutional Neural Networks (CNNs) for power system big data analysis
Authors: Siby Jose Plathottam, Hossein Salehfar, Prakash Ranganathan
Date: 2017-09-17 00:00:00
Proceeding: 2017 North American Power Symposium (NAPS) 2017 9
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

The concept of automated power system data analysis using Deep Neural Networks (as part of the routine tasks normally performed by Independent System Operators) is explored and developed in this paper. Specifically, we propose to use the widely-used Deep neural network architecture known as Convolutional Neural Networks (CNNs). To this end, a 2-D representation of power system data is developed and proposed. To show the relevance of the proposed concept, a multi-class multi-label classification problem is presented as an application example. Midcontinent ISO (MISO) data sets on wind power and load is used for this purpose. TensorFlow, an open source machine learning platform is used to construct the CNN and train the network. The results are discussed and compared with those from standard Feed Forward Networks for the same data.
The Utility of Phase Models in Studying Neural Synchronization
Authors: Youngmin Park, Stewart Heitmann, G. Bard Ermentrout
Date: 2017-07-18 00:00:00

Synchronized neural spiking is associated with many cognitive functions and
thus, merits study for its own sake. The analysis of neural synchronization
naturally leads to the study of repetitive spiking and consequently to the
analysis of coupled neural oscillators. Coupled oscillator theory thus informs
the synchronization of spiking neuronal networks. A crucial aspect of coupled
oscillator theory is the phase response curve (PRC), which describes the impact
of a perturbation to the phase of an oscillator. In neural terms, the
perturbation represents an incoming synaptic potential which may either advance
or retard the timing of the next spike. The phase response curves and the form
of coupling between reciprocally coupled oscillators defines the phase
interaction function, which in turn predicts the synchronization outcome
(in-phase versus anti-phase) and the rate of convergence. We review the two
classes of PRC and demonstrate the utility of the phase model in predicting
synchronization in reciprocally coupled neural models. In addition, we compare
the rate of convergence for all combinations of reciprocally coupled Class I
and Class II oscillators. These findings predict the general synchronization
outcomes of broad classes of neurons under both inhibitory and excitatory
reciprocal coupling.
REPAIR: Removing Representation Bias by Dataset Resampling
Authors: Yi Li, Nuno Vasconcelos
Date: 2019-04-16 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Recognition, Temporal Action Localization

Modern machine learning datasets can have biases for certain representations
that are leveraged by algorithms to achieve high performance without learning
to solve the underlying task. This problem is referred to as "representation
bias". The question of how to reduce the representation biases of a dataset is
investigated and a new dataset REPresentAtion bIas Removal (REPAIR) procedure
is proposed. This formulates bias minimization as an optimization problem,
seeking a weight distribution that penalizes examples easy for a classifier
built on a given feature representation. Bias reduction is then equated to
maximizing the ratio between the classification loss on the reweighted dataset
and the uncertainty of the ground-truth class labels. This is a minimax problem
that REPAIR solves by alternatingly updating classifier parameters and dataset
resampling weights, using stochastic gradient descent. An experimental set-up
is also introduced to measure the bias of any dataset for a given
representation, and the impact of this bias on the performance of recognition
models. Experiments with synthetic and action recognition data show that
dataset REPAIR can significantly reduce representation bias, and lead to
improved generalization of models trained on REPAIRed datasets. The tools used
for characterizing representation bias, and the proposed dataset REPAIR
algorithm, are available at https://github.com/JerryYLi/Dataset-REPAIR/.
Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations
Authors: David Acuna, Amlan Kar, Sanja Fidler
Date: 2019-04-16 00:00:00
Proceeding: CVPR 2019 6
Tasks: Semantic Segmentation

We tackle the problem of semantic boundary prediction, which aims to identify pixels that belong to object(class) boundaries. We notice that relevant datasets consist of a significant level of label noise, reflecting the fact that precise annotations are laborious to get and thus annotators trade-off quality with efficiency. We aim to learn sharp and precise semantic boundaries by explicitly reasoning about annotation noise during training. We propose a simple new layer and loss that can be used with existing learning-based boundary detectors. Our layer/loss enforces the detector to predict a maximum response along the normal direction at an edge, while also regularizing its direction. We further reason about true object boundaries during training using a level set formulation, which allows the network to learn from misaligned labels in an end-to-end fashion. Experiments show that we improve over the CASENet backbone network by more than 4% in terms of MF(ODS) and 18.61% in terms of AP, outperforming all current state-of-the-art methods including those that deal with alignment. Furthermore, we show that our learned network can be used to significantly improve coarse segmentation labels, lending itself as an efficient way to label new data.
A-CNN: Annularly Convolutional Neural Networks on Point Clouds
Authors: Artem Komarichev, Zichun Zhong, Jing Hua
Date: 2019-04-16 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Point Cloud Classification, Segmentation, Semantic Segmentation

Analyzing the geometric and semantic properties of 3D point clouds through
the deep networks is still challenging due to the irregularity and sparsity of
samplings of their geometric structures. This paper presents a new method to
define and compute convolution directly on 3D point clouds by the proposed
annular convolution. This new convolution operator can better capture the local
neighborhood geometry of each point by specifying the (regular and dilated)
ring-shaped structures and directions in the computation. It can adapt to the
geometric variability and scalability at the signal processing level. We apply
it to the developed hierarchical neural networks for object classification,
part segmentation, and semantic segmentation in large-scale scenes. The
extensive experiments and comparisons demonstrate that our approach outperforms
the state-of-the-art methods on a variety of standard benchmark datasets (e.g.,
ModelNet10, ModelNet40, ShapeNet-part, S3DIS, and ScanNet).
Multi-Scale Geometric Consistency Guided Multi-View Stereo
Authors: Qingshan Xu, Wenbing Tao
Date: 2019-04-17 00:00:00
Proceeding: CVPR 2019 6
Tasks: Depth Estimation, Multi-View 3D Reconstruction, Point Clouds

In this paper, we propose an efficient multi-scale geometric consistency
guided multi-view stereo method for accurate and complete depth map estimation.
We first present our basic multi-view stereo method with Adaptive Checkerboard
sampling and Multi-Hypothesis joint view selection (ACMH). It leverages
structured region information to sample better candidate hypotheses for
propagation and infer the aggregation view subset at each pixel. For the depth
estimation of low-textured areas, we further propose to combine ACMH with
multi-scale geometric consistency guidance (ACMM) to obtain the reliable depth
estimates for low-textured areas at coarser scales and guarantee that they can
be propagated to finer scales. To correct the erroneous estimates propagated
from the coarser scales, we present a novel detail restorer. Experiments on
extensive datasets show our method achieves state-of-the-art performance,
recovering the depth estimation not only in low-textured areas but also in
details.
DistanceNet: Estimating Traveled Distance from Monocular Images using a Recurrent Convolutional Neural Network
Authors: Robin Kreuzig, Matthias Ochs, Rudolf Mester
Date: 2019-04-17 00:00:00

Classical monocular vSLAM/VO methods suffer from the scale ambiguity problem.
Hybrid approaches solve this problem by adding deep learning methods, for
example by using depth maps which are predicted by a CNN. We suggest that it is
better to base scale estimation on estimating the traveled distance for a set
of subsequent images. In this paper, we propose a novel end-to-end many-to-one
traveled distance estimator. By using a deep recurrent convolutional neural
network (RCNN), the traveled distance between the first and last image of a set
of consecutive frames is estimated by our DistanceNet. Geometric features are
learned in the CNN part of our model, which are subsequently used by the RNN to
learn dynamics and temporal information. Moreover, we exploit the natural order
of distances by using ordinal regression to predict the distance. The
evaluation on the KITTI dataset shows that our approach outperforms current
state-of-the-art deep learning pose estimators and classical mono vSLAM/VO
methods in terms of distance prediction. Thus, our DistanceNet can be used as a
component to solve the scale problem and help improve current and future
classical mono vSLAM/VO methods.
MHP-VOS: Multiple Hypotheses Propagation for Video Object Segmentation
Authors: Shuangjie Xu, Daizong Liu, Linchao Bao, Wei Liu, Pan Zhou
Date: 2019-04-17 00:00:00
Proceeding: CVPR 2019 6
Tasks: Decision Making, Object, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

We address the problem of semi-supervised video object segmentation (VOS),
where the masks of objects of interests are given in the first frame of an
input video. To deal with challenging cases where objects are occluded or
missing, previous work relies on greedy data association strategies that make
decisions for each frame individually. In this paper, we propose a novel
approach to defer the decision making for a target object in each frame, until
a global view can be established with the entire video being taken into
consideration. Our approach is in the same spirit as Multiple Hypotheses
Tracking (MHT) methods, making several critical adaptations for the VOS
problem. We employ the bounding box (bbox) hypothesis for tracking tree
formation, and the multiple hypotheses are spawned by propagating the preceding
bbox into the detected bbox proposals within a gated region starting from the
initial object mask in the first frame. The gated region is determined by a
gating scheme which takes into account a more comprehensive motion model rather
than the simple Kalman filtering model in traditional MHT. To further design
more customized algorithms tailored for VOS, we develop a novel mask
propagation score instead of the appearance similarity score that could be
brittle due to large deformations. The mask propagation score, together with
the motion score, determines the affinity between the hypotheses during tree
pruning. Finally, a novel mask merging strategy is employed to handle mask
conflicts between objects. Extensive experiments on challenging datasets
demonstrate the effectiveness of the proposed method, especially in the case of
object missing.
Guided Anisotropic Diffusion and Iterative Learning for Weakly Supervised Change Detection
Authors: Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau
Date: 2019-04-17 00:00:00
Tasks: Change Detection, Semantic Segmentation, Weakly-supervised Learning

Large scale datasets created from user labels or openly available data have
become crucial to provide training data for large scale learning algorithms.
While these datasets are easier to acquire, the data are frequently noisy and
unreliable, which is motivating research on weakly supervised learning
techniques. In this paper we propose an iterative learning method that extracts
the useful information from a large scale change detection dataset generated
from open vector data to train a fully convolutional network which surpasses
the performance obtained by naive supervised learning. We also propose the
guided anisotropic diffusion algorithm, which improves semantic segmentation
results using the input images as guides to perform edge preserving filtering,
and is used in conjunction with the iterative training method to improve
results.
Deep Anomaly Detection for Generalized Face Anti-Spoofing
Authors: Daniel Pérez-Cabo, David Jiménez-Cabello, Artur Costa-Pazo, Roberto J. López-Sastre
Date: 2019-04-17 00:00:00
Tasks: Anomaly Detection, Face Anti-Spoofing, Face Recognition, Metric Learning, Triplet

Face recognition has achieved unprecedented results, surpassing human
capabilities in certain scenarios. However, these automatic solutions are not
ready for production because they can be easily fooled by simple identity
impersonation attacks. And although much effort has been devoted to develop
face anti-spoofing models, their generalization capacity still remains a
challenge in real scenarios. In this paper, we introduce a novel approach that
reformulates the Generalized Presentation Attack Detection (GPAD) problem from
an anomaly detection perspective. Technically, a deep metric learning model is
proposed, where a triplet focal loss is used as a regularization for a novel
loss coined "metric-softmax", which is in charge of guiding the learning
process towards more discriminative feature representations in an embedding
space. Finally, we demonstrate the benefits of our deep anomaly detection
architecture, by introducing a few-shot a posteriori probability estimation
that does not need any classifier to be trained on the learned features. We
conduct extensive experiments using the GRAD-GPAD framework that provides the
largest aggregated dataset for face GPAD. Results confirm that our approach is
able to outperform all the state-of-the-art methods by a considerable margin.
LO-Net: Deep Real-time Lidar Odometry
Authors: Qing Li, Shaoyang Chen, Cheng Wang, Xin Li, Chenglu Wen, Ming Cheng, Jonathan Li
Date: 2019-04-17 00:00:00
Proceeding: CVPR 2019 6
Tasks: feature selection, Pose Estimation

We present a novel deep convolutional network pipeline, LO-Net, for real-time lidar odometry estimation. Unlike most existing lidar odometry (LO) estimations that go through individually designed feature selection, feature matching, and pose estimation pipeline, LO-Net can be trained in an end-to-end manner. With a new mask-weighted geometric constraint loss, LO-Net can effectively learn feature representation for LO estimation, and can implicitly exploit the sequential dependencies and dynamics in the data. We also design a scan-to-map module, which uses the geometric and semantic information learned in LO-Net, to improve the estimation accuracy. Experiments on benchmark datasets demonstrate that LO-Net outperforms existing learning based approaches and has similar accuracy with the state-of-the-art geometry-based approach, LOAM.
Events-to-Video: Bringing Modern Computer Vision to Event Cameras
Authors: Henri Rebecq, René Ranftl, Vladlen Koltun, Davide Scaramuzza
Date: 2019-04-17 00:00:00
Proceeding: CVPR 2019 6

Event cameras are novel sensors that report brightness changes in the form of
asynchronous "events" instead of intensity frames. They have significant
advantages over conventional cameras: high temporal resolution, high dynamic
range, and no motion blur. Since the output of event cameras is fundamentally
different from conventional cameras, it is commonly accepted that they require
the development of specialized algorithms to accommodate the particular nature
of events. In this work, we take a different view and propose to apply
existing, mature computer vision techniques to videos reconstructed from event
data. We propose a novel recurrent network to reconstruct videos from a stream
of events, and train it on a large amount of simulated event data. Our
experiments show that our approach surpasses state-of-the-art reconstruction
methods by a large margin (> 20%) in terms of image quality. We further apply
off-the-shelf computer vision algorithms to videos reconstructed from event
data on tasks such as object classification and visual-inertial odometry, and
show that this strategy consistently outperforms algorithms that were
specifically designed for event data. We believe that our approach opens the
door to bringing the outstanding properties of event cameras to an entirely new
range of tasks. A video of the experiments is available at
https://youtu.be/IdYrC4cUO0I
Event-based Vision: A Survey
Authors: Guillermo Gallego, Tobi Delbruck, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew Davison, Joerg Conradt, Kostas Daniilidis, Davide Scaramuzza
Date: 2019-04-17 00:00:00
Tasks: Event-based vision, Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world.
DocBERT: BERT for Document Classification
Authors: Ashutosh Adhikari, Achyudh Ram, Raphael Tang, Jimmy Lin
Date: 2019-04-17 00:00:00
Tasks: Classification, Document Classification, General Classification, Sentiment Analysis, Text Classification

We present, to our knowledge, the first application of BERT to document classification. A few characteristics of the task might lead one to think that BERT is not the most appropriate model: syntactic structures matter less for content categories, documents can often be longer than typical BERT input, and documents often have multiple labels. Nevertheless, we show that a straightforward classification model using BERT is able to achieve the state of the art across four popular datasets. To address the computational expense associated with BERT inference, we distill knowledge from BERT-large to small bidirectional LSTMs, reaching BERT-base parity on multiple datasets using 30x fewer parameters. The primary contribution of our paper is improved baselines that can provide the foundation for future work.