pwc-archive/papers-with-abstracts huggingface dataset:
Polarity Loss for Zero-shot Object Detection
Authors: Shafin Rahman, Salman Khan, Nick Barnes
Date: 2018-11-22 00:00:00
Tasks: Generalized Zero-Shot Object Detection, Metric Learning, Object, object-detection, Object Detection, Zero-Shot Learning, Zero-Shot Object Detection

Conventional object detection models require large amounts of training data. In comparison, humans can recognize previously unseen objects by merely knowing their semantic description. To mimic similar behaviour, zero-shot object detection aims to recognize and localize 'unseen' object instances by using only their semantic information. The model is first trained to learn the relationships between visual and semantic domains for seen objects, later transferring the acquired knowledge to totally unseen objects. This setting gives rise to the need for correct alignment between visual and semantic concepts, so that the unseen objects can be identified using only their semantic attributes. In this paper, we propose a novel loss function called 'Polarity loss', that promotes correct visual-semantic alignment for an improved zero-shot object detection. On one hand, it refines the noisy semantic embeddings via metric learning on a 'Semantic vocabulary' of related concepts to establish a better synergy between visual and semantic domains. On the other hand, it explicitly maximizes the gap between positive and negative predictions to achieve better discrimination between seen, unseen and background objects. Our approach is inspired by embodiment theories in cognitive science, that claim human semantic understanding to be grounded in past experiences (seen objects), related linguistic concepts (word vocabulary) and visual perception (seen/unseen object images). We conduct extensive evaluations on MS-COCO and Pascal VOC datasets, showing significant improvements over state of the art.
An Efficient Approach to Informative Feature Extraction from Multimodal Data
Authors: Lichen Wang, Jiaxiang Wu, Shao-Lun Huang, Lizhong Zheng, Xiangxiang Xu, Lin Zhang, Junzhou Huang
Date: 2018-11-22 00:00:00

One primary focus in multimodal feature extraction is to find the representations of individual modalities that are maximally correlated. As a well-known measure of dependence, the Hirschfeld-Gebelein-R\'{e}nyi (HGR) maximal correlation becomes an appealing objective because of its operational meaning and desirable properties. However, the strict whitening constraints formalized in the HGR maximal correlation limit its application. To address this problem, this paper proposes Soft-HGR, a novel framework to extract informative features from multiple data modalities. Specifically, our framework prevents the "hard" whitening constraints, while simultaneously preserving the same feature geometry as in the HGR maximal correlation. The objective of Soft-HGR is straightforward, only involving two inner products, which guarantees the efficiency and stability in optimization. We further generalize the framework to handle more than two modalities and missing modalities. When labels are partially available, we enhance the discriminative power of the feature representations by making a semi-supervised adaptation. Empirical evaluation implies that our approach learns more informative feature mappings and is more efficient to optimize.
Improving Grey-Box Fuzzing by Modeling Program Behavior
Authors: Siddharth Karamcheti, Gideon Mann, David Rosenberg
Date: 2018-11-21 00:00:00

Grey-box fuzzers such as American Fuzzy Lop (AFL) are popular tools for
finding bugs and potential vulnerabilities in programs. While these fuzzers
have been able to find vulnerabilities in many widely used programs, they are
not efficient; of the millions of inputs executed by AFL in a typical fuzzing
run, only a handful discover unseen behavior or trigger a crash. The remaining
inputs are redundant, exhibiting behavior that has already been observed. Here,
we present an approach to increase the efficiency of fuzzers like AFL by
applying machine learning to directly model how programs behave. We learn a
forward prediction model that maps program inputs to execution traces, training
on the thousands of inputs collected during standard fuzzing. This learned
model guides exploration by focusing on fuzzing inputs on which our model is
the most uncertain (measured via the entropy of the predicted execution trace
distribution). By focusing on executing inputs our learned model is unsure
about, and ignoring any input whose behavior our model is certain about, we
show that we can significantly limit wasteful execution. Through testing our
approach on a set of binaries released as part of the DARPA Cyber Grand
Challenge, we show that our approach is able to find a set of inputs that
result in more code coverage and discovered crashes than baseline fuzzers with
significantly fewer executions.
Spread Divergence
Authors: Mingtian Zhang, Peter Hayes, Tom Bird, Raza Habib, David Barber
Date: 2018-11-21 00:00:00

For distributions $\mathbb{P}$ and $\mathbb{Q}$ with different supports or undefined densities, the divergence $\textrm{D}(\mathbb{P}||\mathbb{Q})$ may not exist. We define a Spread Divergence $\tilde{\textrm{D}}(\mathbb{P}||\mathbb{Q})$ on modified $\mathbb{P}$ and $\mathbb{Q}$ and describe sufficient conditions for the existence of such a divergence. We demonstrate how to maximize the discriminatory power of a given divergence by parameterizing and learning the spread. We also give examples of using a Spread Divergence to train implicit generative models, including linear models (Independent Components Analysis) and non-linear models (Deep Generative Networks).
Low-Resolution Face Recognition
Authors: Zhiyi Cheng, Xiatian Zhu, Shaogang Gong
Date: 2018-11-21 00:00:00
Tasks: Face Recognition, Super-Resolution

Whilst recent face-recognition (FR) techniques have made significant progress
on recognising constrained high-resolution web images, the same cannot be said
on natively unconstrained low-resolution images at large scales. In this work,
we examine systematically this under-studied FR problem, and introduce a novel
Complement Super-Resolution and Identity (CSRI) joint deep learning method with
a unified end-to-end network architecture. We further construct a new
large-scale dataset TinyFace of native unconstrained low-resolution face images
from selected public datasets, because none benchmark of this nature exists in
the literature. With extensive experiments we show there is a significant gap
between the reported FR performances on popular benchmarks and the results on
TinyFace, and the advantages of the proposed CSRI over a variety of
state-of-the-art FR and super-resolution deep models on solving this largely
ignored FR scenario. The TinyFace dataset is released publicly at:
https://qmul-tinyface.github.io/.
Multivariate Forecasting of Crude Oil Spot Prices using Neural Networks
Authors: Ganapathy S. Natarajan, Aishwarya Ashok
Date: 2018-11-21 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

Crude oil is a major component in most advanced economies of the world.
Accurately predicting and understanding the behavior of crude oil prices is
important for economists, analysts, forecasters, and traders, to name a few.
The price of crude oil has declined in the past decade and is seeing a phase of
stability; but will this stability last? This work is an empirical study on how
multivariate analysis may be employed to predict crude oil spot prices using
neural networks. The concept of using neural networks showed promising
potential. A very simple neural network model was able to perform on par with
ARIMA models - the state-of-the-art model in time-series forecasting. Advanced
neural network models using larger datasets may be used in the future to extend
this proof-of-concept to a full scale framework.
Integrating Task-Motion Planning with Reinforcement Learning for Robust Decision Making in Mobile Robots
Authors: Yuqian Jiang, Fangkai Yang, Shiqi Zhang, Peter Stone
Date: 2018-11-21 00:00:00
Tasks: Decision Making, Motion Planning, Reinforcement Learning, Reinforcement Learning (RL), Task and Motion Planning, Task Planning

Task-motion planning (TMP) addresses the problem of efficiently generating
executable and low-cost task plans in a discrete space such that the (initially
unknown) action costs are determined by motion plans in a corresponding
continuous space. However, a task-motion plan can be sensitive to unexpected
domain uncertainty and changes, leading to suboptimal behaviors or execution
failures. In this paper, we propose a novel framework, TMP-RL, which is an
integration of TMP and reinforcement learning (RL) from the execution
experience, to solve the problem of robust task-motion planning in dynamic and
uncertain domains. TMP-RL features two nested planning-learning loops. In the
inner TMP loop, the robot generates a low-cost, feasible task-motion plan by
iteratively planning in the discrete space and updating relevant action costs
evaluated by the motion planner in continuous space. In the outer loop, the
plan is executed, and the robot learns from the execution experience via
model-free RL, to further improve its task-motion plans. RL in the outer loop
is more accurate to the current domain but also more expensive, and using less
costly task and motion planning leads to a jump-start for learning in the real
world. Our approach is evaluated on a mobile service robot conducting
navigation tasks in an office area. Results show that TMP-RL approach
significantly improves adaptability and robustness (in comparison to TMP
methods) and leads to rapid convergence (in comparison to task planning (TP)-RL
methods). We also show that TMP-RL can reuse learned values to smoothly adapt
to new scenarios during long-term deployments.
MS-UNIQUE: Multi-model and Sharpness-weighted Unsupervised Image Quality Estimation
Authors: Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib
Date: 2018-11-21 00:00:00
Tasks: Decoder, Image Quality Assessment

In this paper, we train independent linear decoder models to estimate the
perceived quality of images. More specifically, we calculate the responses of
individual non-overlapping image patches to each of the decoders and scale
these responses based on the sharpness characteristics of filter set. We use
multiple linear decoders to capture different abstraction levels of the image
patches. Training each model is carried out on 100,000 image patches from the
ImageNet database in an unsupervised fashion. Color space selection and ZCA
Whitening are performed over these patches to enhance the descriptiveness of
the data. The proposed quality estimator is tested on the LIVE and the TID 2013
image quality assessment databases. Performance of the proposed method is
compared against eleven other state of the art methods in terms of accuracy,
consistency, linearity, and monotonic behavior. Based on experimental results,
the proposed method is generally among the top performing quality estimators in
all categories.
Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning
Authors: Changhee Lee, Nicholas Mastronarde, Mihaela van der Schaar
Date: 2018-11-21 00:00:00

Estimating the individual treatment effect (ITE) from observational data is
essential in medicine. A central challenge in estimating the ITE is handling
confounders, which are factors that affect both an intervention and its
outcome. Most previous work relies on the unconfoundedness assumption, which
posits that all the confounders are measured in the observational data.
However, if there are unmeasurable (latent) confounders, then confounding bias
is introduced. Fortunately, noisy proxies for the latent confounders are often
available and can be used to make an unbiased estimate of the ITE. In this
paper, we develop a novel adversarial learning framework to make unbiased
estimates of the ITE using noisy proxies.
Pneumonia Detection in Chest Radiographs
Authors: The DeepRadiology Team
Date: 2018-11-21 00:00:00
Tasks: General Classification, Object, object-detection, Object Detection, Pneumonia Detection

In this work, we describe our approach to pneumonia classification and
localization in chest radiographs. This method uses only \emph{open-source}
deep learning object detection and is based on CoupleNet, a fully convolutional
network which incorporates global and local features for object detection. Our
approach achieves robustness through critical modifications of the training
process and a novel ensembling algorithm which merges bounding boxes from
several models. We tested our detection algorithm tested on a dataset of 3000
chest radiographs as part of the 2018 RSNA Pneumonia Challenge; our solution
was recognized as a winning entry in a contest which attracted more than 1400
participants worldwide.
Self-Adversarially Learned Bayesian Sampling
Authors: Yang Zhao, Jianyi Zhang, Changyou Chen
Date: 2018-11-21 00:00:00
Tasks: Self-Learning

Scalable Bayesian sampling is playing an important role in modern machine
learning, especially in the fast-developed unsupervised-(deep)-learning models.
While tremendous progresses have been achieved via scalable Bayesian sampling
such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient
descent (SVGD), the generated samples are typically highly correlated.
Moreover, their sample-generation processes are often criticized to be
inefficient. In this paper, we propose a novel self-adversarial learning
framework that automatically learns a conditional generator to mimic the
behavior of a Markov kernel (transition kernel). High-quality samples can be
efficiently generated by direct forward passes though a learned generator. Most
importantly, the learning process adopts a self-learning paradigm, requiring no
information on existing Markov kernels, e.g., knowledge of how to draw samples
from them. Specifically, our framework learns to use current samples, either
from the generator or pre-provided training data, to update the generator such
that the generated samples progressively approach a target distribution, thus
it is called self-learning. Experiments on both synthetic and real datasets
verify advantages of our framework, outperforming related methods in terms of
both sampling efficiency and sample quality.
Generating Adaptive and Robust Filter Sets Using an Unsupervised Learning Framework
Authors: Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib
Date: 2018-11-21 00:00:00
Tasks: Image Quality Assessment, Retrieval

In this paper, we introduce an adaptive unsupervised learning framework,
which utilizes natural images to train filter sets. The applicability of these
filter sets is demonstrated by evaluating their performance in two contrasting
applications - image quality assessment and texture retrieval. While assessing
image quality, the filters need to capture perceptual differences based on
dissimilarities between a reference image and its distorted version. In texture
retrieval, the filters need to assess similarity between texture images to
retrieve closest matching textures. Based on experiments, we show that the
filter responses span a set in which a monotonicity-based metric can measure
both the perceptual dissimilarity of natural images and the similarity of
texture images. In addition, we corrupt the images in the test set and
demonstrate that the proposed method leads to robust and reliable retrieval
performance compared to existing methods.
Learning from Multiview Correlations in Open-Domain Videos
Authors: Nils Holzenberger, Shruti Palaskar, Pranava Madhyastha, Florian Metze, Raman Arora
Date: 2018-11-21 00:00:00
Tasks: Representation Learning, Retrieval

An increasing number of datasets contain multiple views, such as video, sound
and automatic captions. A basic challenge in representation learning is how to
leverage multiple views to learn better representations. This is further
complicated by the existence of a latent alignment between views, such as
between speech and its transcription, and by the multitude of choices for the
learning objective. We explore an advanced, correlation-based representation
learning method on a 4-way parallel, multimodal dataset, and assess the quality
of the learned representations on retrieval-based tasks. We show that the
proposed approach produces rich representations that capture most of the
information shared across views. Our best models for speech and textual
modalities achieve retrieval rates from 70.7% to 96.9% on open-domain,
user-generated instructional videos. This shows it is possible to learn
reliable representations across disparate, unaligned and noisy modalities, and
encourages using the proposed approach on larger datasets.
MAC: Mining Activity Concepts for Language-based Temporal Localization
Authors: Runzhou Ge, JIyang Gao, Kan Chen, Ram Nevatia
Date: 2018-11-21 00:00:00
Tasks: Language-Based Temporal Localization, Temporal Localization

We address the problem of language-based temporal localization in untrimmed
videos. Compared to temporal localization with fixed categories, this problem
is more challenging as the language-based queries not only have no pre-defined
activity list but also may contain complex descriptions. Previous methods
address the problem by considering features from video sliding windows and
language queries and learning a subspace to encode their correlation, which
ignore rich semantic cues about activities in videos and queries. We propose to
mine activity concepts from both video and language modalities by applying the
actionness score enhanced Activity Concepts based Localizer (ACL).
Specifically, the novel ACL encodes the semantic concepts from verb-obj pairs
in language queries and leverages activity classifiers' prediction scores to
encode visual concepts. Besides, ACL also has the capability to regress sliding
windows as localization results. Experiments show that ACL significantly
outperforms state-of-the-arts under the widely used metric, with more than 5%
increase on both Charades-STA and TACoS datasets.
A Comparative Study of Quality and Content-Based Spatial Pooling Strategies in Image Quality Assessment
Authors: Dogancan Temel, Ghassan AlRegib
Date: 2018-11-21 00:00:00
Tasks: Image Quality Assessment, SSIM

The process of quantifying image quality consists of engineering the quality
features and pooling these features to obtain a value or a map. There has been
a significant research interest in designing the quality features but pooling
is usually overlooked compared to feature design. In this work, we compare the
state of the art quality and content-based spatial pooling strategies and show
that although features are the key in any image quality assessment, pooling
also matters. We also propose a quality-based spatial pooling strategy that is
based on linearly weighted percentile pooling (WPP). Pooling strategies are
analyzed for squared error, SSIM and PerSIM in LIVE, multiply distorted LIVE
and TID2013 image databases.
Robust Active Learning for Electrocardiographic Signal Classification
Authors: Xu Chen, Saratendu Sethi
Date: 2018-11-21 00:00:00
Tasks: Active Learning, Classification, Clustering, General Classification

The classification of electrocardiographic (ECG) signals is a challenging
problem for healthcare industry. Traditional supervised learning methods
require a large number of labeled data which is usually expensive and difficult
to obtain for ECG signals. Active learning is well-suited for ECG signal
classification as it aims at selecting the best set of labeled data in order to
maximize the classification performance. Motivated by the fact that ECG data
are usually heavily unbalanced among different classes and the class labels are
noisy as they are manually labeled, this paper proposes a novel solution based
on robust active learning for addressing these challenges. The key idea is to
first apply the clustering of the data in a low dimensional embedded space and
then select the most information instances within local clusters. By selecting
the most informative instances relying on local average minimal distances, the
algorithm tends to select the data for labelling in a more diversified way.
Finally, the robustness of the model is further enhanced by adding a novel
noisy label reduction scheme after the selection of the labeled data.
Experiments on the ECG signal classification from the MIT-BIH arrhythmia
database demonstrate the effectiveness of the proposed algorithm.
An Interpretable Model for Scene Graph Generation
Authors: Ji Zhang, Kevin Shih, Andrew Tao, Bryan Catanzaro, Ahmed Elgammal
Date: 2018-11-21 00:00:00
Tasks: Graph Generation, Image Captioning, model, Relationship Detection, Scene Graph Generation, Visual Relationship Detection

We propose an efficient and interpretable scene graph generator. We consider
three types of features: visual, spatial and semantic, and we use a late fusion
strategy such that each feature's contribution can be explicitly investigated.
We study the key factors about these features that have the most impact on the
performance, and also visualize the learned visual features for relationships
and investigate the efficacy of our model. We won the champion of the
OpenImages Visual Relationship Detection Challenge on Kaggle, where we
outperform the 2nd place by 5\% (20\% relatively). We believe an accurate scene
graph generator is a fundamental stepping stone for higher-level
vision-language tasks such as image captioning and visual QA, since it provides
a semantic, structured comprehension of an image that is beyond pixels and
objects.
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
Authors: David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, Antonio Torralba
Date: 2018-11-26 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image Generation, Object

Generative Adversarial Networks (GANs) have recently achieved impressive
results for many real-world applications, and many GAN variants have emerged
with improvements in sample quality and training stability. However, they have
not been well visualized or understood. How does a GAN represent our visual
world internally? What causes the artifacts in GAN results? How do
architectural choices affect GAN learning? Answering such questions could
enable us to develop new insights and better models.
  In this work, we present an analytic framework to visualize and understand
GANs at the unit-, object-, and scene-level. We first identify a group of
interpretable units that are closely related to object concepts using a
segmentation-based network dissection method. Then, we quantify the causal
effect of interpretable units by measuring the ability of interventions to
control objects in the output. We examine the contextual relationship between
these units and their surroundings by inserting the discovered object concepts
into new images. We show several practical applications enabled by our
framework, from comparing internal representations across different layers,
models, and datasets, to improving GANs by locating and removing
artifact-causing units, to interactively manipulating objects in a scene. We
provide open source interpretation tools to help researchers and practitioners
better understand their GAN models.