pwc-archive/papers-with-abstracts huggingface dataset:
Random Polyhedral Scenes: An Image Generator for Active Vision System Experiments
Authors: Markus D. Solbach, Stephen Voland, Jeff Edmonds, John K. Tsotsos
Date: 2018-03-27 00:00:00

We present a Polyhedral Scene Generator system which creates a random scene
based on a few user parameters, renders the scene from random view points and
creates a dataset containing the renderings and corresponding annotation files.
We hope that this generator will enable research on how a program could parse a
scene if it had multiple viewpoints to consider. For ambiguous scenes,
typically people move their head or change their position to see the scene from
different angles as well as seeing how it changes while they move; this
research field is called active perception. The random scene generator
presented is designed to support research in this field by generating images of
scenes with known complexity characteristics and with verifiable properties
with respect to the distribution of features across a population. Thus, it is
well-suited for research in active perception without the requirement of a live
3D environment and mobile sensing agent, including comparative performance
evaluations. The system is publicly available at
https://polyhedral.eecs.yorku.ca.
A New Target-specific Object Proposal Generation Method for Visual Tracking
Authors: Guanjun Guo, Hanzi Wang, Yan Yan, Hong-Yuan Mark Liao, Bo Li
Date: 2018-03-27 00:00:00
Tasks: Object, Object Proposal Generation, Visual Tracking

Object proposal generation methods have been widely applied to many computer
vision tasks. However, existing object proposal generation methods often suffer
from the problems of motion blur, low contrast, deformation, etc., when they
are applied to video related tasks. In this paper, we propose an effective and
highly accurate target-specific object proposal generation (TOPG) method, which
takes full advantage of the context information of a video to alleviate these
problems. Specifically, we propose to generate target-specific object proposals
by integrating the information of two important objectness cues: colors and
edges, which are complementary to each other for different challenging
environments in the process of generating object proposals. As a result, the
recall of the proposed TOPG method is significantly increased. Furthermore, we
propose an object proposal ranking strategy to increase the rank accuracy of
the generated object proposals. The proposed TOPG method has yielded
significant recall gain (about 20%-60% higher) compared with several
state-of-the-art object proposal methods on several challenging visual tracking
datasets. Then, we apply the proposed TOPG method to the task of visual
tracking and propose a TOPG-based tracker (called as TOPGT), where TOPG is used
as a sample selection strategy to select a small number of high-quality target
candidates from the generated object proposals. Since the object proposals
generated by the proposed TOPG cover many hard negative samples and positive
samples, these object proposals can not only be used for training an effective
classifier, but also be used as target candidates for visual tracking.
Experimental results show the superior performance of TOPGT for visual tracking
compared with several other state-of-the-art visual trackers (about 3%-11%
higher than the winner of the VOT2015 challenge in term of distance precision).
Point Convolutional Neural Networks by Extension Operators
Authors: Matan Atzmon, Haggai Maron, Yaron Lipman
Date: 2018-03-27 00:00:00
Tasks: 3D Part Segmentation, 3D Point Cloud Classification, Classify 3D Point Clouds, Translation

This paper presents Point Convolutional Neural Networks (PCNN): a novel
framework for applying convolutional neural networks to point clouds. The
framework consists of two operators: extension and restriction, mapping point
cloud functions to volumetric functions and vise-versa. A point cloud
convolution is defined by pull-back of the Euclidean volumetric convolution via
an extension-restriction mechanism.
  The point cloud convolution is computationally efficient, invariant to the
order of points in the point cloud, robust to different samplings and varying
densities, and translation invariant, that is the same convolution kernel is
used at all points. PCNN generalizes image CNNs and allows readily adapting
their architectures to the point cloud setting.
  Evaluation of PCNN on three central point cloud learning benchmarks
convincingly outperform competing point cloud learning methods, and the vast
majority of methods working with more informative shape representations such as
surfaces and/or normals.
Analyzing DNA Hybridization via machine learning
Authors: Weijun Zhu
Date: 2018-03-27 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

In DNA computing, it is impossible to decide whether a specific hybridization
among complex DNA molecules is effective or not within acceptable time. In
order to address this common problem, we introduce a new method based on the
machine learning technique. First, a sample set is employed to train the
Boosted Tree (BT) algorithm, and the corresponding model is obtained. Second,
this model is used to predict classification results of molecular
hybridizations. The experiments show that the average accuracy of the new
method is over 94.2%, and its average efficiency is over 90839 times higher
than that of the existing method. These results indicate that the new method
can quickly and accurately determine the biological effectiveness of molecular
hybridization for a given DNA design.
Efficient parametrization of multi-domain deep neural networks
Authors: Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi
Date: 2018-03-27 00:00:00
Proceeding: CVPR 2018 6
Tasks: Continual Learning, Transfer Learning

A practical limitation of deep neural networks is their high degree of
specialization to a single task and visual domain. Recently, inspired by the
successes of transfer learning, several authors have proposed to learn instead
universal, fixed feature extractors that, used as the first stage of any deep
network, work well for several tasks and domains simultaneously. Nevertheless,
such universal features are still somewhat inferior to specialized networks.
  To overcome this limitation, in this paper we propose to consider instead
universal parametric families of neural networks, which still contain
specialized problem-specific models, but differing only by a small number of
parameters. We study different designs for such parametrizations, including
series and parallel residual adapters, joint adapter compression, and parameter
allocations, and empirically identify the ones that yield the highest
compression. We show that, in order to maximize performance, it is necessary to
adapt both shallow and deep layers of a deep network, but the required changes
are very small. We also show that these universal parametrization are very
effective for transfer learning, where they outperform traditional fine-tuning
techniques.
DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation
Authors: Bharath Bhushan Damodaran, Benjamin Kellenberger, Rémi Flamary, Devis Tuia, Nicolas Courty
Date: 2018-03-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

In computer vision, one is often confronted with problems of domain shifts,
which occur when one applies a classifier trained on a source dataset to target
data sharing similar characteristics (e.g. same classes), but also different
latent data structures (e.g. different acquisition conditions). In such a
situation, the model will perform poorly on the new data, since the classifier
is specialized to recognize visual cues specific to the source domain. In this
work we explore a solution, named DeepJDOT, to tackle this problem: through a
measure of discrepancy on joint deep representations/labels based on optimal
transport, we not only learn new data representations aligned between the
source and target domain, but also simultaneously preserve the discriminative
information used by the classifier. We applied DeepJDOT to a series of visual
recognition tasks, where it compares favorably against state-of-the-art deep
domain adaptation methods.
Learning to Segment Every Thing
Authors: Ronghang Hu, Piotr Dollár, Kaiming He, Trevor Darrell, Ross Girshick
Date: 2017-11-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

Most methods for object instance segmentation require all training examples
to be labeled with segmentation masks. This requirement makes it expensive to
annotate new categories and has restricted instance segmentation models to ~100
well-annotated classes. The goal of this paper is to propose a new partially
supervised training paradigm, together with a novel weight transfer function,
that enables training instance segmentation models on a large set of categories
all of which have box annotations, but only a small fraction of which have mask
annotations. These contributions allow us to train Mask R-CNN to detect and
segment 3000 visual concepts using box annotations from the Visual Genome
dataset and mask annotations from the 80 classes in the COCO dataset. We
evaluate our approach in a controlled study on the COCO dataset. This work is a
first step towards instance segmentation models that have broad comprehension
of the visual world.
Consensus-Based Transfer Linear Support Vector Machines for Decentralized Multi-Task Multi-Agent Learning
Authors: Rui Zhang, Quanyan Zhu
Date: 2017-06-15 00:00:00
Tasks: Transfer Learning

Transfer learning has been developed to improve the performances of different
but related tasks in machine learning. However, such processes become less
efficient with the increase of the size of training data and the number of
tasks. Moreover, privacy can be violated as some tasks may contain sensitive
and private data, which are communicated between nodes and tasks. We propose a
consensus-based distributed transfer learning framework, where several tasks
aim to find the best linear support vector machine (SVM) classifiers in a
distributed network. With alternating direction method of multipliers, tasks
can achieve better classification accuracies more efficiently and privately, as
each node and each task train with their own data, and only decision variables
are transferred between different tasks and nodes. Numerical experiments on
MNIST datasets show that the knowledge transferred from the source tasks can be
used to decrease the risks of the target tasks that lack training data or have
unbalanced training labels. We show that the risks of the target tasks in the
nodes without the data of the source tasks can also be reduced using the
information transferred from the nodes who contain the data of the source
tasks. We also show that the target tasks can enter and leave in real-time
without rerunning the whole algorithm.
Objects as context for detecting their semantic parts
Authors: Abel Gonzalez-Garcia, Davide Modolo, Vittorio Ferrari
Date: 2017-03-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object, Semantic Part Detection

We present a semantic part detection approach that effectively leverages
object information.We use the object appearance and its class as indicators of
what parts to expect. We also model the expected relative location of parts
inside the objects based on their appearance. We achieve this with a new
network module, called OffsetNet, that efficiently predicts a variable number
of part locations within a given object. Our model incorporates all these cues
to detect parts in the context of their objects. This leads to considerably
higher performance for the challenging task of part detection compared to using
part appearance alone (+5 mAP on the PASCAL-Part dataset). We also compare to
other part detection methods on both PASCAL-Part and CUB200-2011 datasets.
Joint Attention in Driver-Pedestrian Interaction: from Theory to Practice
Authors: Amir Rasouli, John K. Tsotsos
Date: 2018-02-07 00:00:00
Tasks: Autonomous Vehicles

Today, one of the major challenges that autonomous vehicles are facing is the
ability to drive in urban environments. Such a task requires communication
between autonomous vehicles and other road users in order to resolve various
traffic ambiguities. The interaction between road users is a form of
negotiation in which the parties involved have to share their attention
regarding a common objective or a goal (e.g. crossing an intersection), and
coordinate their actions in order to accomplish it. In this literature review
we aim to address the interaction problem between pedestrians and drivers (or
vehicles) from joint attention point of view. More specifically, we will
discuss the theoretical background behind joint attention, its application to
traffic interaction and practical approaches to implementing joint attention
for autonomous vehicles.
Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs
Authors: Adrian Bulat, Georgios Tzimiropoulos
Date: 2017-12-07 00:00:00
Proceeding: CVPR 2018 6
Tasks: Face Alignment, Face Hallucination, Image Super-Resolution, Super-Resolution

This paper addresses 2 challenging tasks: improving the quality of low
resolution facial images and accurately locating the facial landmarks on such
poor resolution images. To this end, we make the following 5 contributions: (a)
we propose Super-FAN: the very first end-to-end system that addresses both
tasks simultaneously, i.e. both improves face resolution and detects the facial
landmarks. The novelty or Super-FAN lies in incorporating structural
information in a GAN-based super-resolution algorithm via integrating a
sub-network for face alignment through heatmap regression and optimizing a
novel heatmap loss. (b) We illustrate the benefit of training the two networks
jointly by reporting good results not only on frontal images (as in prior work)
but on the whole spectrum of facial poses, and not only on synthetic low
resolution images (as in prior work) but also on real-world images. (c) We
improve upon the state-of-the-art in face super-resolution by proposing a new
residual-based architecture. (d) Quantitatively, we show large improvement over
the state-of-the-art for both face super-resolution and alignment. (e)
Qualitatively, we show for the first time good results on real-world low
resolution images.
Tensor graph convolutional neural network
Authors: Tong Zhang, Wenming Zheng, Zhen Cui, Yang Li
Date: 2018-03-27 00:00:00
Tasks: Attribute, Matrix Completion

In this paper, we propose a novel tensor graph convolutional neural network
(TGCNN) to conduct convolution on factorizable graphs, for which here two types
of problems are focused, one is sequential dynamic graphs and the other is
cross-attribute graphs. Especially, we propose a graph preserving layer to
memorize salient nodes of those factorized subgraphs, i.e. cross graph
convolution and graph pooling. For cross graph convolution, a parameterized
Kronecker sum operation is proposed to generate a conjunctive adjacency matrix
characterizing the relationship between every pair of nodes across two
subgraphs. Taking this operation, then general graph convolution may be
efficiently performed followed by the composition of small matrices, which thus
reduces high memory and computational burden. Encapsuling sequence graphs into
a recursive learning, the dynamics of graphs can be efficiently encoded as well
as the spatial layout of graphs. To validate the proposed TGCNN, experiments
are conducted on skeleton action datasets as well as matrix completion dataset.
The experiment results demonstrate that our method can achieve more competitive
performance with the state-of-the-art methods.
Learning Less is More - 6D Camera Localization via 3D Surface Regression
Authors: Eric Brachmann, Carsten Rother
Date: 2017-11-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Camera Localization, regression, Visual Localization

Popular research areas like autonomous driving and augmented reality have
renewed the interest in image-based camera localization. In this work, we
address the task of predicting the 6D camera pose from a single RGB image in a
given 3D environment. With the advent of neural networks, previous works have
either learned the entire camera localization process, or multiple components
of a camera localization pipeline. Our key contribution is to demonstrate and
explain that learning a single component of this pipeline is sufficient. This
component is a fully convolutional neural network for densely regressing
so-called scene coordinates, defining the correspondence between the input
image and the 3D scene space. The neural network is prepended to a new
end-to-end trainable pipeline. Our system is efficient, highly accurate, robust
in training, and exhibits outstanding generalization capabilities. It exceeds
state-of-the-art consistently on indoor and outdoor datasets. Interestingly,
our approach surpasses existing techniques even without utilizing a 3D model of
the scene during training, since the network is able to discover 3D scene
geometry automatically, solely from single-view constraints.
A Recorded Debating Dataset
Authors: Shachar Mirkin, Michal Jacovi, Tamar Lavee, Hong-Kwang Kuo, Samuel Thomas, Leslie Sager, Lili Kotlerman, Elad Venezian, Noam Slonim
Date: 2017-09-19 00:00:00
Proceeding: LREC 2018 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper describes an English audio and textual dataset of debating
speeches, a unique resource for the growing research field of computational
argumentation and debating technologies. We detail the process of speech
recording by professional debaters, the transcription of the speeches with an
Automatic Speech Recognition (ASR) system, their consequent automatic
processing to produce a text that is more "NLP-friendly", and in parallel --
the manual transcription of the speeches in order to produce gold-standard
"reference" transcripts. We release 60 speeches on various controversial
topics, each in five formats corresponding to the different stages in the
production of the data. The intention is to allow utilizing this resource for
multiple research purposes, be it the addition of in-domain training data for a
debate-specific ASR system, or applying argumentation mining on either noisy or
clean debate transcripts. We intend to make further releases of this data in
the future.
Fast Parametric Learning with Activation Memorization
Authors: Jack W. Rae, Chris Dyer, Peter Dayan, Timothy P. Lillicrap
Date: 2018-03-27 00:00:00
Proceeding: ICML 2018 7
Tasks: Articles, image-classification, Image Classification, Language Modelling, Memorization

Neural networks trained with backpropagation often struggle to identify
classes that have been observed a small number of times. In applications where
most class labels are rare, such as language modelling, this can become a
performance bottleneck. One potential remedy is to augment the network with a
fast-learning non-parametric model which stores recent activations and class
labels into an external memory. We explore a simplified architecture where we
treat a subset of the model parameters as fast memory stores. This can help
retain information over longer time intervals than a traditional memory, and
does not require additional space or compute. In the case of image
classification, we display faster binding of novel classes on an Omniglot image
curriculum task. We also show improved performance for word-based language
models on news reports (GigaWord), books (Project Gutenberg) and Wikipedia
articles (WikiText-103) --- the latter achieving a state-of-the-art perplexity
of 29.2.
Kinetic Compressive Sensing
Authors: Michele Scipioni, Maria F. Santarelli, Luigi Landini, Ciprian Catana, Douglas N. Greve, Julie C. Price, Stefano Pedemonte
Date: 2018-03-27 00:00:00
Tasks: Compressive Sensing, GPU, Time Series, Time Series Analysis

Parametric images provide insight into the spatial distribution of
physiological parameters, but they are often extremely noisy, due to low SNR of
tomographic data. Direct estimation from projections allows accurate noise
modeling, improving the results of post-reconstruction fitting. We propose a
method, which we name kinetic compressive sensing (KCS), based on a
hierarchical Bayesian model and on a novel reconstruction algorithm, that
encodes sparsity of kinetic parameters. Parametric maps are reconstructed by
maximizing the joint probability, with an Iterated Conditional Modes (ICM)
approach, alternating the optimization of activity time series (OS-MAP-OSL),
and kinetic parameters (MAP-LM). We evaluated the proposed algorithm on a
simulated dynamic phantom: a bias/variance study confirmed how direct estimates
can improve the quality of parametric maps over a post-reconstruction fitting,
and showed how the novel sparsity prior can further reduce their variance,
without affecting bias. Real FDG PET human brain data (Siemens mMR, 40min)
images were also processed. Results enforced how the proposed KCS-regularized
direct method can produce spatially coherent images and parametric maps, with
lower spatial noise and better tissue contrast. A GPU-based open source
implementation of the algorithm is provided.
Learning Depth from Single Images with Deep Neural Network Embedding Focal Length
Authors: Lei He, Guanghui Wang, Zhanyi Hu
Date: 2018-03-27 00:00:00
Tasks: Depth Estimation, Network Embedding, Scene Understanding

Learning depth from a single image, as an important issue in scene
understanding, has attracted a lot of attention in the past decade. The
accuracy of the depth estimation has been improved from conditional Markov
random fields, non-parametric methods, to deep convolutional neural networks
most recently. However, there exist inherent ambiguities in recovering 3D from
a single 2D image. In this paper, we first prove the ambiguity between the
focal length and monocular depth learning, and verify the result using
experiments, showing that the focal length has a great influence on accurate
depth recovery. In order to learn monocular depth by embedding the focal
length, we propose a method to generate synthetic varying-focal-length dataset
from fixed-focal-length datasets, and a simple and effective method is
implemented to fill the holes in the newly generated images. For the sake of
accurate depth recovery, we propose a novel deep neural network to infer depth
through effectively fusing the middle-level information on the
fixed-focal-length dataset, which outperforms the state-of-the-art methods
built on pre-trained VGG. Furthermore, the newly generated varying-focal-length
dataset is taken as input to the proposed network in both learning and
inference phases. Extensive experiments on the fixed- and varying-focal-length
datasets demonstrate that the learned monocular depth with embedded focal
length is significantly improved compared to that without embedding the focal
length information.