pwc-archive/papers-with-abstracts huggingface dataset:
Video Object Segmentation Without Temporal Information
Authors: Kevis-Kokitsi Maninis, Sergi Caelles, Yu-Hua Chen, Jordi Pont-Tuset, Laura Leal-Taixé, Daniel Cremers, Luc van Gool
Date: 2017-09-18 00:00:00
Tasks: Foreground Segmentation, Object, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Segmentation, Video Semantic Segmentation

Video Object Segmentation, and video processing in general, has been
historically dominated by methods that rely on the temporal consistency and
redundancy in consecutive video frames. When the temporal smoothness is
suddenly broken, such as when an object is occluded, or some frames are missing
in a sequence, the result of these methods can deteriorate significantly or
they may not even produce any result at all. This paper explores the orthogonal
approach of processing each frame independently, i.e disregarding the temporal
information. In particular, it tackles the task of semi-supervised video object
segmentation: the separation of an object from the background in a video, given
its mask in the first frame. We present Semantic One-Shot Video Object
Segmentation (OSVOS-S), based on a fully-convolutional neural network
architecture that is able to successively transfer generic semantic
information, learned on ImageNet, to the task of foreground segmentation, and
finally to learning the appearance of a single annotated object of the test
sequence (hence one shot). We show that instance level semantic information,
when combined effectively, can dramatically improve the results of our previous
method, OSVOS. We perform experiments on two recent video segmentation
databases, which show that OSVOS-S is both the fastest and most accurate method
in the state of the art.
Image Classification Based on Quantum KNN Algorithm
Authors: Yijie Dang, Nan Jiang, Hao Hu, Zhuoxiao Ji, Wenyin Zhang
Date: 2018-05-16 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Image classification is an important task in the field of machine learning
and image processing. However, the usually used classification method --- the K
Nearest-Neighbor algorithm has high complexity, because its two main processes:
similarity computing and searching are time-consuming. Especially in the era of
big data, the problem is prominent when the amount of images to be classified
is large. In this paper, we try to use the powerful parallel computing ability
of quantum computers to optimize the efficiency of image classification. The
scheme is based on quantum K Nearest-Neighbor algorithm. Firstly, the feature
vectors of images are extracted on classical computers. Then the feature
vectors are inputted into a quantum superposition state, which is used to
achieve parallel computing of similarity. Next, the quantum minimum search
algorithm is used to speed up searching process for similarity. Finally, the
image is classified by quantum measurement. The complexity of the quantum
algorithm is only O((kM)^(1/2)), which is superior to the classical algorithms.
Moreover, the measurement step is executed only once to ensure the validity of
the scheme. The experimental results show that, the classification accuracy is
83.1% on Graz-01 dataset and 78% on Caltech-101 dataset, which is close to
existing classical algorithms. Hence, our quantum scheme has a good
classification performance while greatly improving the efficiency.
Structured nonlinear variable selection
Authors: Magda Gregorová, Alexandros Kalousis, Stéphane Marchand-Maillet
Date: 2018-05-16 00:00:00
Tasks: Additive models, Variable Selection

We investigate structured sparsity methods for variable selection in
regression problems where the target depends nonlinearly on the inputs. We
focus on general nonlinear functions not limiting a priori the function space
to additive models. We propose two new regularizers based on partial
derivatives as nonlinear equivalents of group lasso and elastic net. We
formulate the problem within the framework of learning in reproducing kernel
Hilbert spaces and show how the variational problem can be reformulated into a
more practical finite dimensional equivalent. We develop a new algorithm
derived from the ADMM principles that relies solely on closed forms of the
proximal operators. We explore the empirical properties of our new algorithm
for Nonlinear Variable Selection based on Derivatives (NVSD) on a set of
experiments and confirm favourable properties of our structured-sparsity models
and the algorithm in terms of both prediction and variable selection accuracy.
Learning Representations of Spatial Displacement through Sensorimotor Prediction
Authors: Michael Garcia Ortiz, Alban Laflaquière
Date: 2018-05-16 00:00:00
Tasks: Prediction

Robots act in their environment through sequences of continuous motor
commands. Because of the dimensionality of the motor space, as well as the
infinite possible combinations of successive motor commands, agents need
compact representations that capture the structure of the resulting
displacements. In the case of an autonomous agent with no a priori knowledge
about its sensorimotor apparatus, this compression has to be learned. We
propose to use Recurrent Neural Networks to encode motor sequences into a
compact representation, which is used to predict the consequence of motor
sequences in term of sensory changes. We show that sensory prediction can
successfully guide the compression of motor sequences into representations that
are organized topologically in term of spatial displacement.
Modeling Human Inference of Others' Intentions in Complex Situations with Plan Predictability Bias
Authors: Ryo Nakahashi, Seiji Yamada
Date: 2018-05-16 00:00:00

A recent approach based on Bayesian inverse planning for the "theory of mind" has shown good performance in modeling human cognition. However, perfect inverse planning differs from human cognition during one kind of complex tasks due to human bounded rationality. One example is an environment in which there are many available plans for achieving a specific goal. We propose a "plan predictability oriented model" as a model of inferring other peoples' goals in complex environments. This model adds the bias that people prefer predictable plans. This bias is calculated with simple plan prediction. We tested this model with a behavioral experiment in which humans observed the partial path of goal-directed actions. Our model had a higher correlation with human inference. We also confirmed the robustness of our model with complex tasks and determined that it can be improved by taking account of individual differences in "bounded rationality".
Direction-aware Spatial Context Features for Shadow Detection
Authors: Xiaowei Hu, Lei Zhu, Chi-Wing Fu, Jing Qin, Pheng-Ann Heng
Date: 2017-12-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: Detecting Shadows, Shadow Detection

Shadow detection is a fundamental and challenging task, since it requires an
understanding of global image semantics and there are various backgrounds
around shadows. This paper presents a novel network for shadow detection by
analyzing image context in a direction-aware manner. To achieve this, we first
formulate the direction-aware attention mechanism in a spatial recurrent neural
network (RNN) by introducing attention weights when aggregating spatial context
features in the RNN. By learning these weights through training, we can recover
direction-aware spatial context (DSC) for detecting shadows. This design is
developed into the DSC module and embedded in a CNN to learn DSC features at
different levels. Moreover, a weighted cross entropy loss is designed to make
the training more effective. We employ two common shadow detection benchmark
datasets and perform various experiments to evaluate our network. Experimental
results show that our network outperforms state-of-the-art methods and achieves
97% accuracy and 38% reduction on balance error rate.
Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks
Authors: Chandrakant Bothe, Sven Magg, Cornelius Weber, Stefan Wermter
Date: 2018-05-16 00:00:00
Tasks: Dialog Act Classification, Dialogue Act Classification

Recent approaches for dialogue act recognition have shown that context from
preceding utterances is important to classify the subsequent one. It was shown
that the performance improves rapidly when the context is taken into account.
We propose an utterance-level attention-based bidirectional recurrent neural
network (Utt-Att-BiRNN) model to analyze the importance of preceding utterances
to classify the current one. In our setup, the BiRNN is given the input set of
current and preceding utterances. Our model outperforms previous models that
use only preceding utterances as context on the used corpus. Another
contribution of the article is to discover the amount of information in each
utterance to classify the subsequent one and to show that context-based
learning not only improves the performance but also achieves higher confidence
in the classification. We use character- and word-level features to represent
the utterances. The results are presented for character and word feature
representations and as an ensemble model of both representations. We found that
when classifying short utterances, the closest preceding utterances contributes
to a higher degree.
Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models
Authors: Jacob Kauffmann, Klaus-Robert Müller, Grégoire Montavon
Date: 2018-05-16 00:00:00
Tasks: Edge Detection

A common machine learning task is to discriminate between normal and
anomalous data points. In practice, it is not always sufficient to reach high
accuracy at this task, one also would like to understand why a given data point
has been predicted in a certain way. We present a new principled approach for
one-class SVMs that decomposes outlier predictions in terms of input variables.
The method first recomposes the one-class model as a neural network with
distance functions and min-pooling, and then performs a deep Taylor
decomposition (DTD) of the model output. The proposed One-Class DTD is
applicable to a number of common distance-based SVM kernels and is able to
reliably explain a wide set of data anomalies. Furthermore, it outperforms
baselines such as sensitivity analysis, nearest neighbor, or simple edge
detection.
Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions
Authors: Stefan Klus, Sebastian Peitz, Ingmar Schuster
Date: 2018-05-16 00:00:00
Tasks: Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

Kernel transfer operators, which can be regarded as approximations of
transfer operators such as the Perron-Frobenius or Koopman operator in
reproducing kernel Hilbert spaces, are defined in terms of covariance and
cross-covariance operators and have been shown to be closely related to the
conditional mean embedding framework developed by the machine learning
community. The goal of this paper is to show how the dominant eigenfunctions of
these operators in combination with gradient-based optimization techniques can
be used to detect long-lived coherent patterns in high-dimensional time-series
data. The results will be illustrated using video data and a fluid flow
example.
Adversarial Training for Patient-Independent Feature Learning with IVOCT Data for Plaque Classification
Authors: Nils Gessert, Markus Heyder, Sarah Latus, David M. Leistner, Youssef S. Abdelwahed, Matthias Lutz, Alexander Schlaefer
Date: 2018-05-16 00:00:00
Tasks: Data Augmentation, General Classification

Deep learning methods have shown impressive results for a variety of medical
problems over the last few years. However, datasets tend to be small due to
time-consuming annotation. As datasets with different patients are often very
heterogeneous generalization to new patients can be difficult. This is
complicated further if large differences in image acquisition can occur, which
is common during intravascular optical coherence tomography for coronary plaque
imaging. We address this problem with an adversarial training strategy where we
force a part of a deep neural network to learn features that are independent of
patient- or acquisitionspecific characteristics. We compare our regularization
method to typical data augmentation strategies and show that our approach
improves performance for a small medical dataset.
CDM: Compound dissimilarity measure and an application to fingerprinting-based positioning
Authors: Caifa Zhou, Andreas Wieser
Date: 2018-05-16 00:00:00
Tasks: Attribute

A non-vector-based dissimilarity measure is proposed by combining
vector-based distance metrics and set operations. This proposed compound
dissimilarity measure (CDM) is applicable to quantify similarity of collections
of attribute/feature pairs where not all attributes are present in all
collections. This is a typical challenge in the context of e.g.,
fingerprinting-based positioning (FbP). Compared to vector-based distance
metrics (e.g., Minkowski), the merits of the proposed CDM are i) the data do
not need to be converted to vectors of equal dimension, ii) shared and unshared
attributes can be weighted differently within the assessment, and iii)
additional degrees of freedom within the measure allow to adapt its properties
to application needs in a data-driven way. We indicate the validity of the
proposed CDM by demonstrating the improvements of the positioning performance
of fingerprinting-based WLAN indoor positioning using four different datasets,
three of them publicly available. When processing these datasets using CDM
instead of conventional distance metrics the accuracy of identifying buildings
and floors improves by about 5% on average. The 2d positioning errors in terms
of root mean squared error (RMSE) are reduced by a factor of two, and the
percentage of position solutions with less than 2m error improves by over 10%.
Predicting the Next Best View for 3D Mesh Refinement
Authors: Luca Morreale, Andrea Romanoni, Matteo Matteucci
Date: 2018-05-16 00:00:00
Tasks: 3D Reconstruction, Robot Navigation

3D reconstruction is a core task in many applications such as robot
navigation or sites inspections. Finding the best poses to capture part of the
scene is one of the most challenging topic that goes under the name of Next
Best View. Recently, many volumetric methods have been proposed; they choose
the Next Best View by reasoning over a 3D voxelized space and by finding which
pose minimizes the uncertainty decoded into the voxels. Such methods are
effective, but they do not scale well since the underlaying representation
requires a huge amount of memory. In this paper we propose a novel mesh-based
approach which focuses on the worst reconstructed region of the environment
mesh. We define a photo-consistent index to evaluate the 3D mesh accuracy, and
an energy function over the worst regions of the mesh which takes into account
the mutual parallax with respect to the previous cameras, the angle of
incidence of the viewing ray to the surface and the visibility of the region.
We test our approach over a well known dataset and achieve state-of-the-art
results.
Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations
Authors: Sosuke Kobayashi
Date: 2018-05-16 00:00:00
Proceeding: NAACL 2018 6
Tasks: Data Augmentation, General Classification, Language Modeling, Language Modelling, Text Augmentation, text-classification, Text Classification

We propose a novel data augmentation for labeled sentences called contextual
augmentation. We assume an invariance that sentences are natural even if the
words in the sentences are replaced with other words with paradigmatic
relations. We stochastically replace words with other words that are predicted
by a bi-directional language model at the word positions. Words predicted
according to a context are numerous but appropriate for the augmentation of the
original words. Furthermore, we retrofit a language model with a
label-conditional architecture, which allows the model to augment sentences
without breaking the label-compatibility. Through the experiments for six
various different text classification tasks, we demonstrate that the proposed
method improves classifiers based on the convolutional or recurrent neural
networks.
SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
Authors: Xiaowei Hu, Xuemiao Xu, Yongjie Xiao, Hao Chen, Shengfeng He, Jing Qin, Pheng-Ann Heng
Date: 2018-04-02 00:00:00
Tasks: Fast Vehicle Detection, object-detection, Object Detection, vehicle detection

Vision-based vehicle detection approaches achieve incredible success in
recent years with the development of deep convolutional neural network (CNN).
However, existing CNN based algorithms suffer from the problem that the
convolutional features are scale-sensitive in object detection task but it is
common that traffic images and videos contain vehicles with a large variance of
scales. In this paper, we delve into the source of scale sensitivity, and
reveal two key issues: 1) existing RoI pooling destroys the structure of small
scale objects, 2) the large intra-class distance for a large variance of scales
exceeds the representation capability of a single network. Based on these
findings, we present a scale-insensitive convolutional neural network (SINet)
for fast detecting vehicles with a large variance of scales. First, we present
a context-aware RoI pooling to maintain the contextual information and original
structure of small scale objects. Second, we present a multi-branch decision
network to minimize the intra-class distance of features. These lightweight
techniques bring zero extra time complexity but prominent detection accuracy
improvement. The proposed techniques can be equipped with any deep network
architectures and keep them trained end-to-end. Our SINet achieves
state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on
the KITTI benchmark and a new highway dataset, which contains a large variance
of scales and extremely small objects.
PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition
Authors: Mikaela Angelina Uy, Gim Hee Lee
Date: 2018-04-10 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Place Recognition, Point Cloud Retrieval, Retrieval, Triplet, Visual Localization, Visual Place Recognition

Unlike its image based counterpart, point cloud based retrieval for place
recognition has remained as an unexplored and unsolved problem. This is largely
due to the difficulty in extracting local feature descriptors from a point
cloud that can subsequently be encoded into a global descriptor for the
retrieval task. In this paper, we propose the PointNetVLAD where we leverage on
the recent success of deep networks to solve point cloud based retrieval for
place recognition. Specifically, our PointNetVLAD is a combination/modification
of the existing PointNet and NetVLAD, which allows end-to-end training and
inference to extract the global descriptor from a given 3D point cloud.
Furthermore, we propose the "lazy triplet and quadruplet" loss functions that
can achieve more discriminative and generalizable global descriptors to tackle
the retrieval task. We create benchmark datasets for point cloud based
retrieval for place recognition, and the experimental results on these datasets
show the feasibility of our PointNetVLAD. Our code and the link for the
benchmark dataset downloads are available in our project website.
http://github.com/mikacuy/pointnetvlad/
Attention-Aware Compositional Network for Person Re-identification
Authors: Jing Xu, Rui Zhao, Feng Zhu, Huaming Wang, Wanli Ouyang
Date: 2018-05-09 00:00:00
Proceeding: CVPR 2018 6
Tasks: Person Re-Identification, Pose Estimation

Person re-identification (ReID) is to identify pedestrians observed from
different camera views based on visual appearance. It is a challenging task due
to large pose variations, complex background clutters and severe occlusions.
Recently, human pose estimation by predicting joint locations was largely
improved in accuracy. It is reasonable to use pose estimation results for
handling pose variations and background clutters, and such attempts have
obtained great improvement in ReID performance. However, we argue that the pose
information was not well utilized and hasn't yet been fully exploited for
person ReID.
  In this work, we introduce a novel framework called Attention-Aware
Compositional Network (AACN) for person ReID. AACN consists of two main
components: Pose-guided Part Attention (PPA) and Attention-aware Feature
Composition (AFC). PPA is learned and applied to mask out undesirable
background features in pedestrian feature maps. Furthermore, pose-guided
visibility scores are estimated for body parts to deal with part occlusion in
the proposed AFC module. Extensive experiments with ablation analysis show the
effectiveness of our method, and state-of-the-art results are achieved on
several public datasets, including Market-1501, CUHK03, CUHK01, SenseReID,
CUHK03-NP and DukeMTMC-reID.
Towards CNN map representation and compression for camera relocalisation
Authors: Luis Contreras, Walterio Mayol-Cuevas
Date: 2017-09-15 00:00:00
Tasks: Camera Relocalization

This paper presents a study on the use of Convolutional Neural Networks for
camera relocalisation and its application to map compression. We follow state
of the art visual relocalisation results and evaluate the response to different
data inputs. We use a CNN map representation and introduce the notion of map
compression under this paradigm by using smaller CNN architectures without
sacrificing relocalisation performance. We evaluate this approach in a series
of publicly available datasets over a number of CNN architectures with
different sizes, both in complexity and number of layers. This formulation
allows us to improve relocalisation accuracy by increasing the number of
training trajectories while maintaining a constant-size CNN.
Lightweight Pyramid Networks for Image Deraining
Authors: Xueyang Fu, Borong Liang, Yue Huang, Xinghao Ding, John Paisley
Date: 2018-05-16 00:00:00
Tasks: 8k, Rain Removal, Single Image Deraining

Existing deep convolutional neural networks have found major success in image
deraining, but at the expense of an enormous number of parameters. This limits
their potential application, for example in mobile devices. In this paper, we
propose a lightweight pyramid of networks (LPNet) for single image deraining.
Instead of designing a complex network structures, we use domain-specific
knowledge to simplify the learning process. Specifically, we find that by
introducing the mature Gaussian-Laplacian image pyramid decomposition
technology to the neural network, the learning problem at each pyramid level is
greatly simplified and can be handled by a relatively shallow network with few
parameters. We adopt recursive and residual network structures to build the
proposed LPNet, which has less than 8K parameters while still achieving
state-of-the-art performance on rain removal. We also discuss the potential
value of LPNet for other low- and high-level vision tasks.