pwc-archive/papers-with-abstracts huggingface dataset:
Domain Adaptation on Graphs by Learning Aligned Graph Bases
Authors: Mehmet Pilanci, Elif Vural
Date: 2018-03-14 00:00:00
Tasks: Domain Adaptation, General Classification

A common assumption in semi-supervised learning with graph models is that the class label function varies smoothly on the data graph, resulting in the rather strict prior that the label function has low-frequency content. Meanwhile, in many classification problems, the label function may vary abruptly in certain graph regions, resulting in high-frequency components. Although the semi-supervised estimation of class labels is an ill-posed problem in general, in several applications it is possible to find a source graph on which the label function has similar frequency content to that on the target graph where the actual classification problem is defined. In this paper, we propose a method for domain adaptation on graphs motivated by these observations. Our algorithm is based on learning the spectrum of the label function in a source graph with many labeled nodes, and transferring the information of the spectrum to the target graph with fewer labeled nodes. While the frequency content of the class label function can be identified through the graph Fourier transform, it is not easy to transfer the Fourier coefficients directly between the two graphs, since no one-to-one match exists between the Fourier basis vectors of independently constructed graphs in the domain adaptation setting. We solve this problem by learning a transformation between the Fourier bases of the two graphs that flexibly ``aligns'' them. The unknown class label function on the target graph is then reconstructed such that its spectrum matches that on the source graph while also ensuring the consistency with the available labels. The proposed method is tested in the classification of image, online product review, and social network data sets. Comparative experiments suggest that the proposed algorithm performs better than recent domain adaptation methods in the literature in most settings.
MPC-Inspired Neural Network Policies for Sequential Decision Making
Authors: Marcus Pereira, David D. Fan, Gabriel Nakajima An, Evangelos Theodorou
Date: 2018-02-15 00:00:00
Tasks: Decision Making, Sequential Decision Making

In this paper we investigate the use of MPC-inspired neural network policies
for sequential decision making. We introduce an extension to the DAgger
algorithm for training such policies and show how they have improved training
performance and generalization capabilities. We take advantage of this
extension to show scalable and efficient training of complex planning policy
architectures in continuous state and action spaces. We provide an extensive
comparison of neural network policies by considering feed forward policies,
recurrent policies, and recurrent policies with planning structure inspired by
the Path Integral control framework. Our results suggest that MPC-type
recurrent policies have better robustness to disturbances and modeling error.
D-PCN: Parallel Convolutional Networks for Image Recognition via a Discriminator
Authors: Shiqi Yang, Gang Peng
Date: 2017-11-12 00:00:00

In this paper, we introduce a simple but quite effective recognition
framework dubbed D-PCN, aiming at enhancing feature extracting ability of CNN.
The framework consists of two parallel CNNs, a discriminator and an extra
classifier which takes integrated features from parallel networks and gives
final prediction. The discriminator is core which drives parallel networks to
focus on different regions and learn complementary representations. The
corresponding joint training strategy is introduced which ensures the
utilization of discriminator. We validate D-PCN with several CNN models on two
benchmark datasets: CIFAR-100 and ImageNet32x32, D-PCN enhances all models. In
particular it yields state of the art performance on CIFAR-100 compared with
related works. We also conduct visualization experiment on fine-grained
Stanford Dogs dataset and verify our motivation. Additionally, we apply D-PCN
for segmentation on PASCAL VOC 2012 and also find promotion.
Approximate Method of Variational Bayesian Matrix Factorization/Completion with Sparse Prior
Authors: Ryota Kawasumi, Koujin Takeda
Date: 2018-03-14 00:00:00
Tasks: Matrix Completion

We derive analytical expression of matrix factorization/completion solution
by variational Bayes method, under the assumption that observed matrix is
originally the product of low-rank dense and sparse matrices with additive
noise. We assume the prior of sparse matrix is Laplace distribution by taking
matrix sparsity into consideration. Then we use several approximations for
derivation of matrix factorization/completion solution. By our solution, we
also numerically evaluate the performance of sparse matrix reconstruction in
matrix factorization, and completion of missing matrix element in matrix
completion.
Learning Representative Temporal Features for Action Recognition
Authors: Ali Javidani, Ahmad Mahmoudi-Aznaveh
Date: 2018-02-19 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization, Time Series, Time Series Analysis, Video Classification

In this paper, a novel video classification method is presented that aims to recognize different categories of third-person videos efficiently. Our motivation is to achieve a light model that could be trained with insufficient training data. With this intuition, the processing of the 3-dimensional video input is broken to 1D in temporal dimension on top of the 2D in spatial. The processes related to 2D spatial frames are being done by utilizing pre-trained networks with no training phase. The only step which involves training is to classify the 1D time series resulted from the description of the 2D signals. As a matter of fact, optical flow images are first calculated from consecutive frames and described by pre-trained CNN networks. Their dimension is then reduced using PCA. By stacking the description vectors beside each other, a multi-channel time series is created for each video. Each channel of the time series represents a specific feature and follows it over time. The main focus of the proposed method is to classify the obtained time series effectively. Towards this, the idea is to let the machine learn temporal features. This is done by training a multi-channel one dimensional Convolutional Neural Network (1D-CNN). The 1D-CNN learns the features along the only temporal dimension. Hence, the number of training parameters decreases significantly which would result in the trainability of the method on even smaller datasets. It is illustrated that the proposed method could reach the state-of-the-art results on two public datasets UCF11, jHMDB and competitive results on HMDB51.
Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning
Authors: David Mascharka, Philip Tran, Ryan Soklaski, Arjun Majumdar
Date: 2018-03-14 00:00:00
Proceeding: CVPR 2018 6
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA), Visual Question Answering (VQA) Split A, Visual Question Answering (VQA) Split B, Visual Reasoning

Visual question answering requires high-order reasoning about an image, which
is a fundamental capability needed by machine systems to follow complex
directives. Recently, modular networks have been shown to be an effective
framework for performing visual reasoning tasks. While modular networks were
initially designed with a degree of model transparency, their performance on
complex visual reasoning benchmarks was lacking. Current state-of-the-art
approaches do not provide an effective mechanism for understanding the
reasoning process. In this paper, we close the performance gap between
interpretable models and state-of-the-art visual reasoning methods. We propose
a set of visual-reasoning primitives which, when composed, manifest as a model
capable of performing complex reasoning tasks in an explicitly-interpretable
manner. The fidelity and interpretability of the primitives' outputs enable an
unparalleled ability to diagnose the strengths and weaknesses of the resulting
model. Critically, we show that these primitives are highly performant,
achieving state-of-the-art accuracy of 99.1% on the CLEVR dataset. We also show
that our model is able to effectively learn generalized representations when
provided a small amount of data containing novel object attributes. Using the
CoGenT generalization task, we show more than a 20 percentage point improvement
over the current state of the art.
Rotation-Sensitive Regression for Oriented Scene Text Detection
Authors: Minghui Liao, Zhen Zhu, Baoguang Shi, Gui-Song Xia, Xiang Bai
Date: 2018-03-14 00:00:00
Proceeding: CVPR 2018 6
Tasks: Classification, General Classification, object-detection, Object Detection, Oriented Object Detection, regression, Scene Text Detection, Text Detection

Text in natural images is of arbitrary orientations, requiring detection in
terms of oriented bounding boxes. Normally, a multi-oriented text detector
often involves two key tasks: 1) text presence detection, which is a
classification problem disregarding text orientation; 2) oriented bounding box
regression, which concerns about text orientation. Previous methods rely on
shared features for both tasks, resulting in degraded performance due to the
incompatibility of the two tasks. To address this issue, we propose to perform
classification and regression on features of different characteristics,
extracted by two network branches of different designs. Concretely, the
regression branch extracts rotation-sensitive features by actively rotating the
convolutional filters, while the classification branch extracts
rotation-invariant features by pooling the rotation-sensitive features. The
proposed method named Rotation-sensitive Regression Detector (RRD) achieves
state-of-the-art performance on three oriented scene text benchmark datasets,
including ICDAR 2015, MSRA-TD500, RCTW-17 and COCO-Text. Furthermore, RRD
achieves a significant improvement on a ship collection dataset, demonstrating
its generality on oriented object detection.
Face-MagNet: Magnifying Feature Maps to Detect Small Faces
Authors: Pouya Samangouei, Mahyar Najibi, Larry Davis, Rama Chellappa
Date: 2018-03-14 00:00:00
Tasks: Face Detection, Region Proposal

In this paper, we introduce the Face Magnifier Network (Face-MageNet), a face
detector based on the Faster-RCNN framework which enables the flow of
discriminative information of small scale faces to the classifier without any
skip or residual connections. To achieve this, Face-MagNet deploys a set of
ConvTranspose, also known as deconvolution, layers in the Region Proposal
Network (RPN) and another set before the Region of Interest (RoI) pooling layer
to facilitate detection of finer faces. In addition, we also design, train, and
evaluate three other well-tuned architectures that represent the conventional
solutions to the scale problem: context pooling, skip connections, and scale
partitioning. Each of these three networks achieves comparable results to the
state-of-the-art face detectors. With extensive experiments, we show that
Face-MagNet based on a VGG16 architecture achieves better results than the
recently proposed ResNet101-based HR method on the task of face detection on
WIDER dataset and also achieves similar results on the hard set as our other
method SSH.
Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation
Authors: Xiaoxiao Li, Chen Change Loy
Date: 2018-03-12 00:00:00
Proceeding: ECCV 2018 9
Tasks: Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

The problem of video object segmentation can become extremely challenging
when multiple instances co-exist. While each instance may exhibit large scale
and pose variations, the problem is compounded when instances occlude each
other causing failures in tracking. In this study, we formulate a deep
recurrent network that is capable of segmenting and tracking objects in video
simultaneously by their temporal continuity, yet able to re-identify them when
they re-appear after a prolonged occlusion. We combine both temporal
propagation and re-identification functionalities into a single framework that
can be trained end-to-end. In particular, we present a re-identification module
with template expansion to retrieve missing objects despite their large
appearance changes. In addition, we contribute a new attention-based recurrent
mask propagation approach that is robust to distractors not belonging to the
target segment. Our approach achieves a new state-of-the-art global mean
(Region Jaccard and Boundary F measure) of 68.2 on the challenging DAVIS 2017
benchmark (test-dev set), outperforming the winning solution which achieves a
global mean of 66.1 on the same partition.
Adaptive Graph-based Total Variation for Tomographic Reconstructions
Authors: Faisal Mahmood, Nauman Shahid, Ulf Skoglund, Pierre Vandergheynst
Date: 2016-10-04 00:00:00
Tasks: Image Reconstruction, Tomographic Reconstructions

Sparsity exploiting image reconstruction (SER) methods have been extensively
used with Total Variation (TV) regularization for tomographic reconstructions.
Local TV methods fail to preserve texture details and often create additional
artefacts due to over-smoothing. Non-Local TV (NLTV) methods have been proposed
as a solution to this but they either lack continuous updates due to
computational constraints or limit the locality to a small region. In this
paper, we propose Adaptive Graph-based TV (AGTV). The proposed method goes
beyond spatial similarity between different regions of an image being
reconstructed by establishing a connection between similar regions in the
entire image regardless of spatial distance. As compared to NLTV the proposed
method is computationally efficient and involves updating the graph prior
during every iteration making the connection between similar regions stronger.
Moreover, it promotes sparsity in the wavelet and graph gradient domains. Since
TV is a special case of graph TV the proposed method can also be seen as a
generalization of SER and TV methods.
Pseudo-labels for Supervised Learning on Dynamic Vision Sensor Data, Applied to Object Detection under Ego-motion
Authors: Nicholas F. Y. Chen
Date: 2017-09-27 00:00:00
Tasks: object-detection, Object Detection, Self-Driving Cars

In recent years, dynamic vision sensors (DVS), also known as event-based
cameras or neuromorphic sensors, have seen increased use due to various
advantages over conventional frame-based cameras. Using principles inspired by
the retina, its high temporal resolution overcomes motion blurring, its high
dynamic range overcomes extreme illumination conditions and its low power
consumption makes it ideal for embedded systems on platforms such as drones and
self-driving cars. However, event-based data sets are scarce and labels are
even rarer for tasks such as object detection. We transferred discriminative
knowledge from a state-of-the-art frame-based convolutional neural network
(CNN) to the event-based modality via intermediate pseudo-labels, which are
used as targets for supervised learning. We show, for the first time,
event-based car detection under ego-motion in a real environment at 100 frames
per second with a test average precision of 40.3% relative to our annotated
ground truth. The event-based car detector handles motion blur and poor
illumination conditions despite not explicitly trained to do so, and even
complements frame-based CNN detectors, suggesting that it has learnt
generalized visual representations.
MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge
Authors: Simon Ostermann, Ashutosh Modi, Michael Roth, Stefan Thater, Manfred Pinkal
Date: 2018-03-14 00:00:00
Proceeding: LREC 2018 5
Tasks: Natural Language Understanding, Reading Comprehension

We introduce a large dataset of narrative texts and questions about these
texts, intended to be used in a machine comprehension task that requires
reasoning using commonsense knowledge. Our dataset complements similar datasets
in that we focus on stories about everyday activities, such as going to the
movies or working in the garden, and that the questions require commonsense
knowledge, or more specifically, script knowledge, to be answered. We show that
our mode of data collection via crowdsourcing results in a substantial amount
of such inference questions. The dataset forms the basis of a shared task on
commonsense and script knowledge organized at SemEval 2018 and provides
challenging test cases for the broader natural language understanding
community.
LivDet 2017 Fingerprint Liveness Detection Competition 2017
Authors: Valerio Mura, Giulia Orrù, Roberto Casula, Alessandra Sibiriu, Giulia Loi, Pierluigi Tuveri, Luca Ghiani, Gian Luca Marcialis
Date: 2018-03-14 00:00:00

Fingerprint Presentation Attack Detection (FPAD) deals with distinguishing
images coming from artificial replicas of the fingerprint characteristic, made
up of materials like silicone, gelatine or latex, and images coming from alive
fingerprints. Images are captured by modern scanners, typically relying on
solid-state or optical technologies. Since from 2009, the Fingerprint Liveness
Detection Competition (LivDet) aims to assess the performance of the
state-of-the-art algorithms according to a rigorous experimental protocol and,
at the same time, a simple overview of the basic achievements. The competition
is open to all academics research centers and all companies that work in this
field. The positive, increasing trend of the participants number, which
supports the success of this initiative, is confirmed even this year: 17
algorithms were submitted to the competition, with a larger involvement of
companies and academies. This means that the topic is relevant for both sides,
and points out that a lot of work must be done in terms of fundamental and
applied research.
Building Sparse Deep Feedforward Networks using Tree Receptive Fields
Authors: Xiaopeng Li, Zhourong Chen, Nevin L. Zhang
Date: 2018-03-14 00:00:00

Sparse connectivity is an important factor behind the success of
convolutional neural networks and recurrent neural networks. In this paper, we
consider the problem of learning sparse connectivity for feedforward neural
networks (FNNs). The key idea is that a unit should be connected to a small
number of units at the next level below that are strongly correlated. We use
Chow-Liu's algorithm to learn a tree-structured probabilistic model for the
units at the current level, use the tree to identify subsets of units that are
strongly correlated, and introduce a new unit with receptive field over the
subsets. The procedure is repeated on the new units to build multiple layers of
hidden units. The resulting model is called a TRF-net. Empirical results show
that, when compared to dense FNNs, TRF-net achieves better or comparable
classification performance with much fewer parameters and sparser structures.
They are also more interpretable.
Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering
Authors: Xiaopeng Li, Zhourong Chen, Leonard K. M. Poon, Nevin L. Zhang
Date: 2018-03-14 00:00:00
Proceeding: ICLR 2019 5
Tasks: Clustering

We investigate a variant of variational autoencoders where there is a
superstructure of discrete latent variables on top of the latent features. In
general, our superstructure is a tree structure of multiple super latent
variables and it is automatically learned from data. When there is only one
latent variable in the superstructure, our model reduces to one that assumes
the latent features to be generated from a Gaussian mixture model. We call our
model the latent tree variational autoencoder (LTVAE). Whereas previous deep
learning methods for clustering produce only one partition of data, LTVAE
produces multiple partitions of data, each being given by one super latent
variable. This is desirable because high dimensional data usually have many
different natural facets and can be meaningfully partitioned in multiple ways.
Robust Maximization of Non-Submodular Objectives
Authors: Ilija Bogunovic, Junyao Zhao, Volkan Cevher
Date: 2018-02-20 00:00:00

We study the problem of maximizing a monotone set function subject to a cardinality constraint $k$ in the setting where some number of elements $\tau$ is deleted from the returned set. The focus of this work is on the worst-case adversarial setting. While there exist constant-factor guarantees when the function is submodular, there are no guarantees for non-submodular objectives. In this work, we present a new algorithm Oblivious-Greedy and prove the first constant-factor approximation guarantees for a wider class of non-submodular objectives. The obtained theoretical bounds are the first constant-factor bounds that also hold in the linear regime, i.e. when the number of deletions $\tau$ is linear in $k$. Our bounds depend on established parameters such as the submodularity ratio and some novel ones such as the inverse curvature. We bound these parameters for two important objectives including support selection and variance reduction. Finally, we numerically demonstrate the robust performance of Oblivious-Greedy for these two objectives on various datasets.
Combining Multi-level Contexts of Superpixel using Convolutional Neural Networks to perform Natural Scene Labeling
Authors: Aritra Das, Swarnendu Ghosh, Ritesh Sarkhel, Sandipan Choudhuri, Nibaran Das, Mita Nasipuri
Date: 2018-03-14 00:00:00
Tasks: Image Segmentation, Scene Labeling, Segmentation, Semantic Segmentation, Superpixels

Modern deep learning algorithms have triggered various image segmentation
approaches. However most of them deal with pixel based segmentation. However,
superpixels provide a certain degree of contextual information while reducing
computation cost. In our approach, we have performed superpixel level semantic
segmentation considering 3 various levels as neighbours for semantic contexts.
Furthermore, we have enlisted a number of ensemble approaches like max-voting
and weighted-average. We have also used the Dempster-Shafer theory of
uncertainty to analyze confusion among various classes. Our method has proved
to be superior to a number of different modern approaches on the same dataset.
EdgeStereo: A Context Integrated Residual Pyramid Network for Stereo Matching
Authors: Xiao Song, Xu Zhao, Hanwen Hu, Liangji Fang
Date: 2018-03-14 00:00:00
Tasks: Disparity Estimation, Edge Detection, Stereo Matching, Stereo Matching Hand

Recent convolutional neural networks, especially end-to-end disparity
estimation models, achieve remarkable performance on stereo matching task.
However, existed methods, even with the complicated cascade structure, may fail
in the regions of non-textures, boundaries and tiny details. Focus on these
problems, we propose a multi-task network EdgeStereo that is composed of a
backbone disparity network and an edge sub-network. Given a binocular image
pair, our model enables end-to-end prediction of both disparity map and edge
map. Basically, we design a context pyramid to encode multi-scale context
information in disparity branch, followed by a compact residual pyramid for
cascaded refinement. To further preserve subtle details, our EdgeStereo model
integrates edge cues by feature embedding and edge-aware smoothness loss
regularization. Comparative results demonstrates that stereo matching and edge
detection can help each other in the unified model. Furthermore, our method
achieves state-of-art performance on both KITTI Stereo and Scene Flow
benchmarks, which proves the effectiveness of our design.