pwc-archive/papers-with-abstracts huggingface dataset:
Unified Graph based Multi-Cue Feature Fusion for Robust Visual Tracking
Authors: Kapil Sharma, Himanshu Ahuja, Ashish Kumar, Nipun Bansal, Gurjit Singh Walia
Date: 2018-12-16 00:00:00
Tasks: Object, Object Tracking, Outlier Detection, Visual Tracking

Visual Tracking is a complex problem due to unconstrained appearance variations and dynamic environment. Extraction of complementary information from the object environment via multiple features and adaption to the target's appearance variations are the key problems of this work. To this end, we propose a robust object tracking framework based on Unified Graph Fusion (UGF) of multi-cue to adapt to the object's appearance. The proposed cross-diffusion of sparse and dense features not only suppresses the individual feature deficiencies but also extracts the complementary information from multi-cue. This iterative process builds robust unified features which are invariant to object deformations, fast motion, and occlusion. Robustness of the unified feature also enables the random forest classifier to precisely distinguish the foreground from the background, adding resilience to background clutter. In addition, we present a novel kernel-based adaptation strategy using outlier detection and a transductive reliability metric.
What's to know? Uncertainty as a Guide to Asking Goal-oriented Questions
Authors: Ehsan Abbasnejad, Qi Wu, Javen Shi, Anton Van Den Hengel
Date: 2018-12-16 00:00:00
Proceeding: CVPR 2019 6
Tasks: Visual Dialog

One of the core challenges in Visual Dialogue problems is asking the question
that will provide the most useful information towards achieving the required
objective. Encouraging an agent to ask the right questions is difficult because
we don't know a-priori what information the agent will need to achieve its
task, and we don't have an explicit model of what it knows already. We propose
a solution to this problem based on a Bayesian model of the uncertainty in the
implicit model maintained by the visual dialogue agent, and in the function
used to select an appropriate output. By selecting the question that minimises
the predicted regret with respect to this implicit model the agent actively
reduces ambiguity. The Bayesian model of uncertainty also enables a principled
method for identifying when enough information has been acquired, and an action
should be selected. We evaluate our approach on two goal-oriented dialogue
datasets, one for visual-based collaboration task and the other for a
negotiation-based task. Our uncertainty-aware information-seeking model
outperforms its counterparts in these two challenging problems.
Gold Seeker: Information Gain from Policy Distributions for Goal-oriented Vision-and-Langauge Reasoning
Authors: Ehsan Abbasnejad, Iman Abbasnejad, Qi Wu, Javen Shi, Anton Van Den Hengel
Date: 2018-12-16 00:00:00
Proceeding: CVPR 2020 6
Tasks: Reinforcement Learning, Visual Dialog

As Computer Vision moves from a passive analysis of pixels to active analysis of semantics, the breadth of information algorithms need to reason over has expanded significantly. One of the key challenges in this vein is the ability to identify the information required to make a decision, and select an action that will recover it. We propose a reinforcement-learning approach that maintains a distribution over its internal information, thus explicitly representing the ambiguity in what it knows, and needs to know, towards achieving its goal. Potential actions are then generated according to this distribution. For each potential action a distribution of the expected outcomes is calculated, and the value of the potential information gain assessed. The action taken is that which maximizes the potential information gain. We demonstrate this approach applied to two vision-and-language problems that have attracted significant recent interest, visual dialog and visual query generation. In both cases, the method actively selects actions that will best reduce its internal uncertainty and outperforms its competitors in achieving the goal of the challenge.
Connecting Spectral Clustering to Maximum Margins and Level Sets
Authors: David P. Hofmeyr
Date: 2018-12-16 00:00:00
Tasks: Clustering

We study the connections between spectral clustering and the problems of
maximum margin clustering, and estimation of the components of level sets of a
density function. Specifically, we obtain bounds on the eigenvectors of graph
Laplacian matrices in terms of the between cluster separation, and within
cluster connectivity. These bounds ensure that the spectral clustering solution
converges to the maximum margin clustering solution as the scaling parameter is
reduced towards zero. The sensitivity of maximum margin clustering solutions to
outlying points is well known, but can be mitigated by first removing such
outliers, and applying maximum margin clustering to the remaining points. If
outliers are identified using an estimate of the underlying probability
density, then the remaining points may be seen as an estimate of a level set of
this density function. We show that such an approach can be used to
consistently estimate the components of the level sets of a density function
under very mild assumptions.
PAC Learning Guarantees Under Covariate Shift
Authors: Artidoro Pagnoni, Stefan Gramatovici, Samuel Liu
Date: 2018-12-16 00:00:00
Tasks: Domain Adaptation, PAC learning, Transfer Learning

We consider the Domain Adaptation problem, also known as the covariate shift
problem, where the distributions that generate the training and test data
differ while retaining the same labeling function. This problem occurs across a
large range of practical applications, and is related to the more general
challenge of transfer learning. Most recent work on the topic focuses on
optimization techniques that are specific to an algorithm or practical use case
rather than a more general approach. The sparse literature attempting to
provide general bounds seems to suggest that efficient learning even under
strong assumptions is not possible for covariate shift. Our main contribution
is to recontextualize these results by showing that any Probably Approximately
Correct (PAC) learnable concept class is still PAC learnable under covariate
shift conditions with only a polynomial increase in the number of training
samples. This approach essentially demonstrates that the Domain Adaptation
learning problem is as hard as the underlying PAC learning problem, provided
some conditions over the training and test distributions. We also present
bounds for the rejection sampling algorithm, justifying it as a solution to the
Domain Adaptation problem in certain scenarios.
Fast MVAE: Joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier
Authors: Li Li, Hirokazu Kameoka, Shoji Makino
Date: 2018-12-16 00:00:00
Tasks: Classification, General Classification

This paper proposes an alternative algorithm for multichannel variational
autoencoder (MVAE), a recently proposed multichannel source separation
approach. While MVAE is notable in its impressive source separation
performance, the convergence-guaranteed optimization algorithm and that it
allows us to estimate source-class labels simultaneously with source
separation, there are still two major drawbacks, i.e., the high computational
complexity and unsatisfactory source classification accuracy. To overcome these
drawbacks, the proposed method employs an auxiliary classifier VAE, an
information-theoretic extension of the conditional VAE, for learning the
generative model of the source spectrograms. Furthermore, with the trained
auxiliary classifier, we introduce a novel algorithm for the optimization that
is able to not only reduce the computational time but also improve the source
classification performance. We call the proposed method "fast MVAE (fMVAE)".
Experimental evaluations revealed that fMVAE achieved comparative source
separation performance to MVAE and about 80% source classification accuracy
rate while it reduced about 93% computational time.
Pre-Trained Convolutional Neural Network Features for Facial Expression Recognition
Authors: Aravind Ravi
Date: 2018-12-16 00:00:00
Tasks: Classification, Facial Expression Recognition, Facial Expression Recognition (FER), General Classification, image-classification, Image Classification, Object Recognition

Facial expression recognition has been an active area in computer vision with
application areas including animation, social robots, personalized banking,
etc. In this study, we explore the problem of image classification for
detecting facial expressions based on features extracted from pre-trained
convolutional neural networks trained on ImageNet database. Features are
extracted and transferred to a Linear Support Vector Machine for
classification. All experiments are performed on two publicly available
datasets such as JAFFE and CK+ database. The results show that representations
learned from pre-trained networks for a task such as object recognition can be
transferred, and used for facial expression recognition. Furthermore, for a
small dataset, using features from earlier layers of the VGG19 network provides
better classification accuracy. Accuracies of 92.26% and 92.86% were achieved
for the CK+ and JAFFE datasets respectively.
TET-GAN: Text Effects Transfer via Stylization and Destylization
Authors: Shuai Yang, Jiaying Liu, Wenjing Wang, Zongming Guo
Date: 2018-12-16 00:00:00
Tasks: One-Shot Learning, Style Transfer, Text Effects Transfer

Text effects transfer technology automatically makes the text dramatically
more impressive. However, previous style transfer methods either study the
model for general style, which cannot handle the highly-structured text effects
along the glyph, or require manual design of subtle matching criteria for text
effects. In this paper, we focus on the use of the powerful representation
abilities of deep neural features for text effects transfer. For this purpose,
we propose a novel Texture Effects Transfer GAN (TET-GAN), which consists of a
stylization subnetwork and a destylization subnetwork. The key idea is to train
our network to accomplish both the objective of style transfer and style
removal, so that it can learn to disentangle and recombine the content and
style features of text effects images. To support the training of our network,
we propose a new text effects dataset with as much as 64 professionally
designed styles on 837 characters. We show that the disentangled feature
representations enable us to transfer or remove all these styles on arbitrary
glyphs using one network. Furthermore, the flexible network design empowers
TET-GAN to efficiently extend to a new text style via one-shot learning where
only one example is required. We demonstrate the superiority of the proposed
method in generating high-quality stylized text over the state-of-the-art
methods.
Embedding Push and Pull Search in the Framework of Differential Evolution for Solving Constrained Single-objective Optimization Problems
Authors: Zhun Fan, Wenji Li, Zhaojun Wang, Yutong Yuan, Fuzan Sun, Zhi Yang, Jie Ruan, Zhaocheng Li, Erik Goodman
Date: 2018-12-16 00:00:00

This paper proposes a push and pull search method in the framework of
differential evolution (PPS-DE) to solve constrained single-objective
optimization problems (CSOPs). More specifically, two sub-populations,
including the top and bottom sub-populations, are collaborated with each other
to search global optimal solutions efficiently. The top sub-population adopts
the pull and pull search (PPS) mechanism to deal with constraints, while the
bottom sub-population use the superiority of feasible solutions (SF) technique
to deal with constraints. In the top sub-population, the search process is
divided into two different stages --- push and pull stages.An adaptive DE
variant with three trial vector generation strategies is employed in the
proposed PPS-DE. In the top sub-population, all the three trial vector
generation strategies are used to generate offsprings, just like in CoDE. In
the bottom sub-population, a strategy adaptation, in which the trial vector
generation strategies are periodically self-adapted by learning from their
experiences in generating promising solutions in the top sub-population, is
used to choose a suitable trial vector generation strategy to generate one
offspring. Furthermore, a parameter adaptation strategy from LSHADE44 is
employed in both sup-populations to generate scale factor $F$ and crossover
rate $CR$ for each trial vector generation strategy. Twenty-eight CSOPs with
10-, 30-, and 50-dimensional decision variables provided in the CEC2018
competition on real parameter single objective optimization are optimized by
the proposed PPS-DE. The experimental results demonstrate that the proposed
PPS-DE has the best performance compared with the other seven state-of-the-art
algorithms, including AGA-PPS, LSHADE44, LSHADE44+IDE, UDE, IUDE,
$\epsilon$MAg-ES and C$^2$oDE.
Efficient Super Resolution Using Binarized Neural Network
Authors: Yinglan Ma, Hongyu Xiong, Zhe Hu, Lizhuang Ma
Date: 2018-12-16 00:00:00
Tasks: Binarization, image-classification, Image Classification, Image Enhancement, Image Super-Resolution, Quantization, Super-Resolution

Deep convolutional neural networks (DCNNs) have recently demonstrated
high-quality results in single-image super-resolution (SR). DCNNs often suffer
from over-parametrization and large amounts of redundancy, which results in
inefficient inference and high memory usage, preventing massive applications on
mobile devices. As a way to significantly reduce model size and computation
time, binarized neural network has only been shown to excel on semantic-level
tasks such as image classification and recognition. However, little effort of
network quantization has been spent on image enhancement tasks like SR, as
network quantization is usually assumed to sacrifice pixel-level accuracy. In
this work, we explore an network-binarization approach for SR tasks without
sacrificing much reconstruction accuracy. To achieve this, we binarize the
convolutional filters in only residual blocks, and adopt a learnable weight for
each binary filter. We evaluate this idea on several state-of-the-art
DCNN-based architectures, and show that binarized SR networks achieve
comparable qualitative and quantitative results as their real-weight
counterparts. Moreover, the proposed binarized strategy could help reduce model
size by 80% when applying on SRResNet, and could potentially speed up inference
by 5 times.
Trust Region Based Adversarial Attack on Neural Networks
Authors: Zhewei Yao, Amir Gholami, Peng Xu, Kurt Keutzer, Michael Mahoney
Date: 2018-12-16 00:00:00
Proceeding: CVPR 2019 6
Tasks: Adversarial Attack, GPU

Deep Neural Networks are quite vulnerable to adversarial perturbations.
Current state-of-the-art adversarial attack methods typically require very time
consuming hyper-parameter tuning, or require many iterations to solve an
optimization based adversarial attack. To address this problem, we present a
new family of trust region based adversarial attacks, with the goal of
computing adversarial perturbations efficiently. We propose several attacks
based on variants of the trust region optimization method. We test the proposed
methods on Cifar-10 and ImageNet datasets using several different models
including AlexNet, ResNet-50, VGG-16, and DenseNet-121 models. Our methods
achieve comparable results with the Carlini-Wagner (CW) attack, but with
significant speed up of up to $37\times$, for the VGG-16 model on a Titan Xp
GPU. For the case of ResNet-50 on ImageNet, we can bring down its
classification accuracy to less than 0.1\% with at most $1.5\%$ relative
$L_\infty$ (or $L_2$) perturbation requiring only $1.02$ seconds as compared to
$27.04$ seconds for the CW attack. We have open sourced our method which can be
accessed at [1].
Provable limitations of deep learning
Authors: Emmanuel Abbe, Colin Sandon
Date: 2018-12-16 00:00:00
Tasks: Community Detection, Deep Learning

As the success of deep learning reaches more grounds, one would like to also
envision the potential limits of deep learning. This paper gives a first set of
results proving that certain deep learning algorithms fail at learning certain
efficiently learnable functions. The results put forward a notion of
cross-predictability that characterizes when such failures take place. Parity
functions provide an extreme example with a cross-predictability that decays
exponentially, while a mere super-polynomial decay of the cross-predictability
is shown to be sufficient to obtain failures. Examples in community detection
and arithmetic learning are also discussed.
  Recall that it is known that the class of neural networks (NNs) with
polynomial network size can express any function that can be implemented in
polynomial time, and that their sample complexity scales polynomially with the
network size. The challenge is with the optimization error (the ERM is
NP-hard), and the success behind deep learning is to train deep NNs with
descent algorithms. The failures shown in this paper apply to training
poly-size NNs on function distributions of low cross-predictability with a
descent algorithm that is either run with limited memory per sample or that is
initialized and run with enough randomness. We further claim that such types of
constraints are necessary to obtain failures, in that exact SGD with careful
non-random initialization can be shown to learn parities. The
cross-predictability in our results plays a similar role the statistical
dimension in statistical query (SQ) algorithms, with distinctions explained in
the paper. The proof techniques are based on exhibiting algorithmic constraints
that imply a statistical indistinguishability between the algorithm's output on
the test model v.s.\ a null model, using information measures to bound the
total variation distance.
Action Quality Assessment Across Multiple Actions
Authors: Paritosh Parmar, Brendan Tran Morris
Date: 2018-12-15 00:00:00
Tasks: Action Quality Assessment, Transfer Learning

Can learning to measure the quality of an action help in measuring the
quality of other actions? If so, can consolidated samples from multiple actions
help improve the performance of current approaches? In this paper, we carry out
experiments to see if knowledge transfer is possible in the action quality
assessment (AQA) setting. Experiments are carried out on our newly released AQA
dataset (http://rtis.oit.unlv.edu/datasets.html) consisting of 1106 action
samples from seven actions with quality scores as measured by expert human
judges. Our experimental results show that there is utility in learning a
single model across multiple actions.
Low-rank semidefinite programming for the MAX2SAT problem
Authors: Po-Wei Wang, J. Zico Kolter
Date: 2018-12-15 00:00:00

This paper proposes a new algorithm for solving MAX2SAT problems based on
combining search methods with semidefinite programming approaches. Semidefinite
programming techniques are well-known as a theoretical tool for approximating
maximum satisfiability problems, but their application has traditionally been
very limited by their speed and randomized nature. Our approach overcomes this
difficult by using a recent approach to low-rank semidefinite programming,
specialized to work in an incremental fashion suitable for use in an exact
search algorithm. The method can be used both within complete or incomplete
solver, and we demonstrate on a variety of problems from recent competitions.
Our experiments show that the approach is faster (sometimes by orders of
magnitude) than existing state-of-the-art complete and incomplete solvers,
representing a substantial advance in search methods specialized for MAX2SAT
problems.
A Bandit Approach to Maximum Inner Product Search
Authors: Rui Liu, Tianyi Wu, Barzan Mozafari
Date: 2018-12-15 00:00:00

There has been substantial research on sub-linear time approximate algorithms
for Maximum Inner Product Search (MIPS). To achieve fast query time,
state-of-the-art techniques require significant preprocessing, which can be a
burden when the number of subsequent queries is not sufficiently large to
amortize the cost. Furthermore, existing methods do not have the ability to
directly control the suboptimality of their approximate results with
theoretical guarantees. In this paper, we propose the first approximate
algorithm for MIPS that does not require any preprocessing, and allows users to
control and bound the suboptimality of the results. We cast MIPS as a Best Arm
Identification problem, and introduce a new bandit setting that can fully
exploit the special structure of MIPS. Our approach outperforms
state-of-the-art methods on both synthetic and real-world datasets.
Searching with Consistent Prioritization for Multi-Agent Path Finding
Authors: Hang Ma, Daniel Harabor, Peter J. Stuckey, Jiaoyang Li, Sven Koenig
Date: 2018-12-15 00:00:00
Tasks: Multi-Agent Path Finding

We study prioritized planning for Multi-Agent Path Finding (MAPF). Existing
prioritized MAPF algorithms depend on rule-of-thumb heuristics and random
assignment to determine a fixed total priority ordering of all agents a priori.
We instead explore the space of all possible partial priority orderings as part
of a novel systematic and conflict-driven combinatorial search framework. In a
variety of empirical comparisons, we demonstrate state-of-the-art solution
qualities and success rates, often with similar runtimes to existing
algorithms. We also develop new theoretical results that explore the
limitations of prioritized planning, in terms of completeness and optimality,
for the first time.
Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery
Authors: Hang Ma, Wolfgang Hönig, T. K. Satish Kumar, Nora Ayanian, Sven Koenig
Date: 2018-12-15 00:00:00

The Multi-Agent Pickup and Delivery (MAPD) problem models applications where
a large number of agents attend to a stream of incoming pickup-and-delivery
tasks. Token Passing (TP) is a recent MAPD algorithm that is efficient and
effective. We make TP even more efficient and effective by using a novel
combinatorial search algorithm, called Safe Interval Path Planning with
Reservation Table (SIPPwRT), for single-agent path planning. SIPPwRT uses an
advanced data structure that allows for fast updates and lookups of the current
paths of all agents in an online setting. The resulting MAPD algorithm
TP-SIPPwRT takes kinematic constraints of real robots into account directly
during planning, computes continuous agent movements with given velocities that
work on non-holonomic robots rather than discrete agent movements with uniform
velocity, and is complete for well-formed MAPD instances. We demonstrate its
benefits for automated warehouses using both an agent simulator and a standard
robot simulator. For example, we demonstrate that it can compute paths for
hundreds of agents and thousands of tasks in seconds and is more efficient and
effective than existing MAPD algorithms that use a post-processing step to
adapt their paths to continuous agent movements with given velocities.
InverSynth: Deep Estimation of Synthesizer Parameter Configurations from Audio Signals
Authors: Oren Barkan, David Tsiris, Ori Katz, Noam Koenigstein
Date: 2018-12-15 00:00:00

Sound synthesis is a complex field that requires domain expertise. Manual tuning of synthesizer parameters to match a specific sound can be an exhaustive task, even for experienced sound engineers. In this paper, we introduce InverSynth - an automatic method for synthesizer parameters tuning to match a given input sound. InverSynth is based on strided convolutional neural networks and is capable of inferring the synthesizer parameters configuration from the input spectrogram and even from the raw audio. The effectiveness InverSynth is demonstrated on a subtractive synthesizer with four frequency modulated oscillators, envelope generator and a gater effect. We present extensive quantitative and qualitative results that showcase the superiority InverSynth over several baselines. Furthermore, we show that the network depth is an important factor that contributes to the prediction accuracy.