pwc-archive/papers-with-abstracts huggingface dataset:
A Hybrid Data Association Framework for Robust Online Multi-Object Tracking
Authors: Min Yang, Yuwei Wu, Yunde Jia
Date: 2017-03-31 00:00:00
Tasks: global-optimization, Multi-Object Tracking, Object Tracking, Online Multi-Object Tracking

Global optimization algorithms have shown impressive performance in
data-association based multi-object tracking, but handling online data remains
a difficult hurdle to overcome. In this paper, we present a hybrid data
association framework with a min-cost multi-commodity network flow for robust
online multi-object tracking. We build local target-specific models interleaved
with global optimization of the optimal data association over multiple video
frames. More specifically, in the min-cost multi-commodity network flow, the
target-specific similarities are online learned to enforce the local
consistency for reducing the complexity of the global data association.
Meanwhile, the global data association taking multiple video frames into
account alleviates irrecoverable errors caused by the local data association
between adjacent frames. To ensure the efficiency of online tracking, we give
an efficient near-optimal solution to the proposed min-cost multi-commodity
flow problem, and provide the empirical proof of its sub-optimality. The
comprehensive experiments on real data demonstrate the superior tracking
performance of our approach in various challenging situations.
Deceiving Google's Cloud Video Intelligence API Built for Summarizing Videos
Authors: Hossein Hosseini, Baicen Xiao, Radha Poovendran
Date: 2017-03-26 00:00:00
Tasks: image-classification, Image Classification

Despite the rapid progress of the techniques for image classification, video
annotation has remained a challenging task. Automated video annotation would be
a breakthrough technology, enabling users to search within the videos.
Recently, Google introduced the Cloud Video Intelligence API for video
analysis. As per the website, the system can be used to "separate signal from
noise, by retrieving relevant information at the video, shot or per frame"
level. A demonstration website has been also launched, which allows anyone to
select a video for annotation. The API then detects the video labels (objects
within the video) as well as shot labels (description of the video events over
time). In this paper, we examine the usability of the Google's Cloud Video
Intelligence API in adversarial environments. In particular, we investigate
whether an adversary can subtly manipulate a video in such a way that the API
will return only the adversary-desired labels. For this, we select an image,
which is different from the video content, and insert it, periodically and at a
very low rate, into the video. We found that if we insert one image every two
seconds, the API is deceived into annotating the video as if it only contained
the inserted image. Note that the modification to the video is hardly
noticeable as, for instance, for a typical frame rate of 25, we insert only one
image per 50 video frames. We also found that, by inserting one image per
second, all the shot labels returned by the API are related to the inserted
image. We perform the experiments on the sample videos provided by the API
demonstration website and show that our attack is successful with different
videos and images.
Spectral Methods for Nonparametric Models
Authors: Hsiao-Yu Fish Tung, Chao-yuan Wu, Manzil Zaheer, Alexander J. Smola
Date: 2017-03-31 00:00:00

Nonparametric models are versatile, albeit computationally expensive, tool
for modeling mixture models. In this paper, we introduce spectral methods for
the two most popular nonparametric models: the Indian Buffet Process (IBP) and
the Hierarchical Dirichlet Process (HDP). We show that using spectral methods
for the inference of nonparametric models are computationally and statistically
efficient. In particular, we derive the lower-order moments of the IBP and the
HDP, propose spectral algorithms for both models, and provide reconstruction
guarantees for the algorithms. For the HDP, we further show that applying
hierarchical models on dataset with hierarchical structure, which can be solved
with the generalized spectral HDP, produces better solutions to that of flat
models regarding likelihood performance.
Fundamental Conditions for Low-CP-Rank Tensor Completion
Authors: Morteza Ashraphijuo, Xiaodong Wang
Date: 2017-03-31 00:00:00
Tasks: Matrix Completion

We consider the problem of low canonical polyadic (CP) rank tensor
completion. A completion is a tensor whose entries agree with the observed
entries and its rank matches the given CP rank. We analyze the manifold
structure corresponding to the tensors with the given rank and define a set of
polynomials based on the sampling pattern and CP decomposition. Then, we show
that finite completability of the sampled tensor is equivalent to having a
certain number of algebraically independent polynomials among the defined
polynomials. Our proposed approach results in characterizing the maximum number
of algebraically independent polynomials in terms of a simple geometric
structure of the sampling pattern, and therefore we obtain the deterministic
necessary and sufficient condition on the sampling pattern for finite
completability of the sampled tensor. Moreover, assuming that the entries of
the tensor are sampled independently with probability $p$ and using the
mentioned deterministic analysis, we propose a combinatorial method to derive a
lower bound on the sampling probability $p$, or equivalently, the number of
sampled entries that guarantees finite completability with high probability. We
also show that the existing result for the matrix completion problem can be
used to obtain a loose lower bound on the sampling probability $p$. In
addition, we obtain deterministic and probabilistic conditions for unique
completability. It is seen that the number of samples required for finite or
unique completability obtained by the proposed analysis on the CP manifold is
orders-of-magnitude lower than that is obtained by the existing analysis on the
Grassmannian manifold.
Deep Domain Adaptation Based Video Smoke Detection using Synthetic Smoke Images
Authors: Gao Xu, Yongming Zhang, Qixing Zhang, Gaohua Lin, Jinjun Wang
Date: 2017-03-31 00:00:00
Tasks: Domain Adaptation

In this paper, a deep domain adaptation based method for video smoke
detection is proposed to extract a powerful feature representation of smoke.
Due to the smoke image samples limited in scale and diversity for deep CNN
training, we systematically produced adequate synthetic smoke images with a
wide variation in the smoke shape, background and lighting conditions.
Considering that the appearance gap (dataset bias) between synthetic and real
smoke images degrades significantly the performance of the trained model on the
test set composed fully of real images, we build deep architectures based on
domain adaptation to confuse the distributions of features extracted from
synthetic and real smoke images. This approach expands the domain-invariant
feature space for smoke image samples. With their approximate feature
distribution off non-smoke images, the recognition rate of the trained model is
improved significantly compared to the model trained directly on mixed dataset
of synthetic and real images. Experimentally, several deep architectures with
different design choices are applied to the smoke detector. The ultimate
framework can get a satisfactory result on the test set. We believe that our
approach is a start in the direction of utilizing deep neural networks enhanced
with synthetic smoke images for video smoke detection.
Finding Mirror Symmetry via Registration
Authors: Marcelo Cicconet, David G. C. Hildebrand, Hunter Elliott
Date: 2016-11-18 00:00:00
Tasks: Line Detection, Object Recognition

Symmetry is prevalent in nature and a common theme in man-made designs. Both
the human visual system and computer vision algorithms can use symmetry to
facilitate object recognition and other tasks. Detecting mirror symmetry in
images and data is, therefore, useful for a number of applications. Here, we
demonstrate that the problem of fitting a plane of mirror symmetry to data in
any Euclidian space can be reduced to the problem of registering two datasets.
The exactness of the resulting solution depends entirely on the registration
accuracy. This new Mirror Symmetry via Registration (MSR) framework involves
(1) data reflection with respect to an arbitrary plane, (2) registration of
original and reflected datasets, and (3) calculation of the eigenvector of
eigenvalue -1 for the transformation matrix representing the reflection and
registration mappings. To support MSR, we also introduce a novel 2D
registration method based on random sample consensus of an ensemble of
normalized cross-correlation matches. With this as its registration back-end,
MSR achieves state-of-the-art performance for symmetry line detection in two
independent 2D testing databases. We further demonstrate the generality of MSR
by testing it on a database of 3D shapes with an iterative closest point
registration back-end. Finally, we explore its applicability to examining
symmetry in natural systems by assessing the degree of symmetry present in
myelinated axon reconstructions from a larval zebrafish.
Deep 3D Face Identification
Authors: Donghyun Kim, Matthias Hernandez, Jongmoo Choi, Gerard Medioni
Date: 2017-03-30 00:00:00
Tasks: Face Identification, Face Recognition, Transfer Learning

We propose a novel 3D face recognition algorithm using a deep convolutional
neural network (DCNN) and a 3D augmentation technique. The performance of 2D
face recognition algorithms has significantly increased by leveraging the
representational power of deep neural networks and the use of large-scale
labeled training data. As opposed to 2D face recognition, training
discriminative deep features for 3D face recognition is very difficult due to
the lack of large-scale 3D face datasets. In this paper, we show that transfer
learning from a CNN trained on 2D face images can effectively work for 3D face
recognition by fine-tuning the CNN with a relatively small number of 3D facial
scans. We also propose a 3D face augmentation technique which synthesizes a
number of different facial expressions from a single 3D face scan. Our proposed
method shows excellent recognition results on Bosphorus, BU-3DFE, and 3D-TEC
datasets, without using hand-crafted features. The 3D identification using our
deep features also scales well for large databases.
Learning End-to-End Goal-Oriented Dialog
Authors: Antoine Bordes, Y-Lan Boureau, Jason Weston
Date: 2016-05-24 00:00:00
Tasks: dialog state tracking, Goal-Oriented Dialog, Slot Filling

Traditional dialog systems used in goal-oriented applications require a lot
of domain-specific handcrafting, which hinders scaling up to new domains.
End-to-end dialog systems, in which all components are trained from the dialogs
themselves, escape this limitation. But the encouraging success recently
obtained in chit-chat dialog may not carry over to goal-oriented settings. This
paper proposes a testbed to break down the strengths and shortcomings of
end-to-end dialog systems in goal-oriented applications. Set in the context of
restaurant reservation, our tasks require manipulating sentences and symbols,
so as to properly conduct conversations, issue API calls and use the outputs of
such calls. We show that an end-to-end dialog system based on Memory Networks
can reach promising, yet imperfect, performance and learn to perform
non-trivial operations. We confirm those results by comparing our system to a
hand-crafted slot-filling baseline on data from the second Dialog State
Tracking Challenge (Henderson et al., 2014a). We show similar result patterns
on data extracted from an online concierge service.
SE3-Nets: Learning Rigid Body Motion using Deep Neural Networks
Authors: Arunkumar Byravan, Dieter Fox
Date: 2016-06-08 00:00:00

We introduce SE3-Nets, which are deep neural networks designed to model and
learn rigid body motion from raw point cloud data. Based only on sequences of
depth images along with action vectors and point wise data associations,
SE3-Nets learn to segment effected object parts and predict their motion
resulting from the applied force. Rather than learning point wise flow vectors,
SE3-Nets predict SE3 transformations for different parts of the scene. Using
simulated depth data of a table top scene and a robot manipulator, we show that
the structure underlying SE3-Nets enables them to generate a far more
consistent prediction of object motion than traditional flow based networks.
Additional experiments with a depth camera observing a Baxter robot pushing
objects on a table show that SE3-Nets also work well on real data.
Neutral evolution and turnover over centuries of English word popularity
Authors: Damian Ruck, R. Alexander Bentley, Alberto Acerbi, Philip Garnett, Daniel J. Hruschka
Date: 2017-03-30 00:00:00

Here we test Neutral models against the evolution of English word frequency
and vocabulary at the population scale, as recorded in annual word frequencies
from three centuries of English language books. Against these data, we test
both static and dynamic predictions of two neutral models, including the
relation between corpus size and vocabulary size, frequency distributions, and
turnover within those frequency distributions. Although a commonly used Neutral
model fails to replicate all these emergent properties at once, we find that
modified two-stage Neutral model does replicate the static and dynamic
properties of the corpus data. This two-stage model is meant to represent a
relatively small corpus (population) of English books, analogous to a `canon',
sampled by an exponentially increasing corpus of books in the wider population
of authors. More broadly, this mode -- a smaller neutral model within a larger
neutral model -- could represent more broadly those situations where mass
attention is focused on a small subset of the cultural variants.
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
Authors: Chih-Yao Ma, Min-Hung Chen, Zsolt Kira, Ghassan AlRegib
Date: 2017-03-30 00:00:00
Tasks: Action Classification, Action Recognition, Activity Recognition, Temporal Action Localization, Video Classification, Video Understanding

Recent two-stream deep Convolutional Neural Networks (ConvNets) have made
significant progress in recognizing human actions in videos. Despite their
success, methods extending the basic two-stream ConvNet have not systematically
explored possible network architectures to further exploit spatiotemporal
dynamics within video sequences. Further, such networks often use different
baseline two-stream networks. Therefore, the differences and the distinguishing
factors between various methods using Recurrent Neural Networks (RNN) or
convolutional networks on temporally-constructed feature vectors
(Temporal-ConvNet) are unclear. In this work, we first demonstrate a strong
baseline two-stream ConvNet using ResNet-101. We use this baseline to
thoroughly examine the use of both RNNs and Temporal-ConvNets for extracting
spatiotemporal information. Building upon our experimental results, we then
propose and investigate two different networks to further integrate
spatiotemporal information: 1) temporal segment RNN and 2) Inception-style
Temporal-ConvNet. We demonstrate that using both RNNs (using LSTMs) and
Temporal-ConvNets on spatiotemporal feature matrices are able to exploit
spatiotemporal dynamics to improve the overall performance. However, each of
these methods require proper care to achieve state-of-the-art performance; for
example, LSTMs require pre-segmented data or else they cannot fully exploit
temporal information. Our analysis identifies specific limitations for each
method that could form the basis of future work. Our experimental results on
UCF101 and HMDB51 datasets achieve state-of-the-art performances, 94.1% and
69.0%, respectively, without requiring extensive temporal augmentation.
Near Perfect Protein Multi-Label Classification with Deep Neural Networks
Authors: Balazs Szalkai, Vince Grolmusz
Date: 2017-03-30 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Artificial neural networks (ANNs) have gained a well-deserved popularity
among machine learning tools upon their recent successful applications in
image- and sound processing and classification problems. ANNs have also been
applied for predicting the family or function of a protein, knowing its residue
sequence. Here we present two new ANNs with multi-label classification ability,
showing impressive accuracy when classifying protein sequences into 698 UniProt
families (AUC=99.99%) and 983 Gene Ontology classes (AUC=99.45%).
Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks
Authors: Arash Ardakani, Carlo Condo, Warren J. Gross
Date: 2016-11-04 00:00:00

Recently deep neural networks have received considerable attention due to
their ability to extract and represent high-level abstractions in data sets.
Deep neural networks such as fully-connected and convolutional neural networks
have shown excellent performance on a wide range of recognition and
classification tasks. However, their hardware implementations currently suffer
from large silicon area and high power consumption due to the their high degree
of complexity. The power/energy consumption of neural networks is dominated by
memory accesses, the majority of which occur in fully-connected networks. In
fact, they contain most of the deep neural network parameters. In this paper,
we propose sparsely-connected networks, by showing that the number of
connections in fully-connected networks can be reduced by up to 90% while
improving the accuracy performance on three popular datasets (MNIST, CIFAR10
and SVHN). We then propose an efficient hardware architecture based on
linear-feedback shift registers to reduce the memory requirements of the
proposed sparsely-connected networks. The proposed architecture can save up to
90% of memory compared to the conventional implementations of fully-connected
neural networks. Moreover, implementation results show up to 84% reduction in
the energy consumption of a single neuron of the proposed sparsely-connected
networks compared to a single neuron of fully-connected neural networks.
Relevance Subject Machine: A Novel Person Re-identification Framework
Authors: Igor Fedorov, Ritwik Giri, Bhaskar D. Rao, Truong Q. Nguyen
Date: 2017-03-30 00:00:00
Tasks: Person Re-Identification

We propose a novel method called the Relevance Subject Machine (RSM) to solve
the person re-identification (re-id) problem. RSM falls under the category of
Bayesian sparse recovery algorithms and uses the sparse representation of the
input video under a pre-defined dictionary to identify the subject in the
video. Our approach focuses on the multi-shot re-id problem, which is the
prevalent problem in many video analytics applications. RSM captures the
essence of the multi-shot re-id problem by constraining the support of the
sparse codes for each input video frame to be the same. Our proposed approach
is also robust enough to deal with time varying outliers and occlusions by
introducing a sparse, non-stationary noise term in the model error. We provide
a novel Variational Bayesian based inference procedure along with an intuitive
interpretation of the proposed update rules. We evaluate our approach over
several commonly used re-id datasets and show superior performance over current
state-of-the-art algorithms. Specifically, for ILIDS-VID, a recent large scale
re-id dataset, RSM shows significant improvement over all published approaches,
achieving an 11.5% (absolute) improvement in rank 1 accuracy over the closest
competing algorithm considered.
Deep Neural Network Optimized to Resistive Memory with Nonlinear Current-Voltage Characteristics
Authors: Hyungjun Kim, Taesu Kim, Jinseok Kim, Jae-Joon Kim
Date: 2017-03-30 00:00:00

Artificial Neural Network computation relies on intensive vector-matrix
multiplications. Recently, the emerging nonvolatile memory (NVM) crossbar array
showed a feasibility of implementing such operations with high energy
efficiency, thus there are many works on efficiently utilizing emerging NVM
crossbar array as analog vector-matrix multiplier. However, its nonlinear I-V
characteristics restrain critical design parameters, such as the read voltage
and weight range, resulting in substantial accuracy loss. In this paper,
instead of optimizing hardware parameters to a given neural network, we propose
a methodology of reconstructing a neural network itself optimized to resistive
memory crossbar arrays. To verify the validity of the proposed method, we
simulated various neural network with MNIST and CIFAR-10 dataset using two
different specific Resistive Random Access Memory (RRAM) model. Simulation
results show that our proposed neural network produces significantly higher
inference accuracies than conventional neural network when the synapse devices
have nonlinear I-V characteristics.
Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention
Authors: Jinkyu Kim, John Canny
Date: 2017-03-30 00:00:00
Proceeding: ICCV 2017 10
Tasks: Self-Driving Cars, Steering Control

Deep neural perception and control networks are likely to be a key component
of self-driving vehicles. These models need to be explainable - they should
provide easy-to-interpret rationales for their behavior - so that passengers,
insurance companies, law enforcement, developers etc., can understand what
triggered a particular behavior. Here we explore the use of visual
explanations. These explanations take the form of real-time highlighted regions
of an image that causally influence the network's output (steering control).
Our approach is two-stage. In the first stage, we use a visual attention model
to train a convolution network end-to-end from images to steering angle. The
attention model highlights image regions that potentially influence the
network's output. Some of these are true influences, but some are spurious. We
then apply a causal filtering step to determine which input regions actually
influence the output. This produces more succinct visual explanations and more
accurately exposes the network's behavior. We demonstrate the effectiveness of
our model on three datasets totaling 16 hours of driving. We first show that
training with attention does not degrade the performance of the end-to-end
network. Then we show that the network causally cues on a variety of features
that are used by humans while driving.
LSTM with Working Memory
Authors: Andrew Pulver, Siwei Lyu
Date: 2016-05-06 00:00:00

Previous RNN architectures have largely been superseded by LSTM, or "Long
Short-Term Memory". Since its introduction, there have been many variations on
this simple design. However, it is still widely used and we are not aware of a
gated-RNN architecture that outperforms LSTM in a broad sense while still being
as simple and efficient. In this paper we propose a modified LSTM-like
architecture. Our architecture is still simple and achieves better performance
on the tasks that we tested on. We also introduce a new RNN performance
benchmark that uses the handwritten digits and stresses several important
network capabilities.