pwc-archive/papers-with-abstracts huggingface dataset:
Large-Scale Pedestrian Retrieval Competition
Authors: Da Li, Zhang Zhang
Date: 2019-03-06 00:00:00
Tasks: Attribute, Pedestrian Detection, Person Retrieval, Retrieval

The Large-Scale Pedestrian Retrieval Competition (LSPRC) mainly focuses on
person retrieval which is an important end application in intelligent vision
system of surveillance. Person retrieval aims at searching the interested
target with specific visual attributes or images. The low image quality,
various camera viewpoints, large pose variations and occlusions in real scenes
make it a challenge problem. By providing large-scale surveillance data in real
scene and standard evaluation methods that are closer to real application, the
competition aims to improve the robust of related algorithms and further meet
the complicated situations in real application. LSPRC includes two kinds of
tasks, i.e., Attribute based Pedestrian Retrieval (PR-A) and Re-IDentification
(ReID) based Pedestrian Retrieval (PR-ID). The normal evaluation index, i.e.,
mean Average Precision (mAP), is used to measure the performances of the two
tasks under various scale, pose and occlusion. While the method of system
evaluation is introduced to evaluate the person retrieval system in which the
related algorithms of the two tasks are integrated into a large-scale video
parsing platform (named ISEE) combing with algorithm of pedestrian detection.
Negative Training for Neural Dialogue Response Generation
Authors: Tianxing He, James Glass
Date: 2019-03-06 00:00:00
Proceeding: ACL 2020 6
Tasks: Diversity, Response Generation

Although deep learning models have brought tremendous advancements to the field of open-domain dialogue response generation, recent research results have revealed that the trained models have undesirable generation behaviors, such as malicious responses and generic (boring) responses. In this work, we propose a framework named "Negative Training" to minimize such behaviors. Given a trained model, the framework will first find generated samples that exhibit the undesirable behavior, and then use them to feed negative training signals for fine-tuning the model. Our experiments show that negative training can significantly reduce the hit rate of malicious responses, or discourage frequent responses and improve response diversity.
Age Progression and Regression with Spatial Attention Modules
Authors: Qi Li, Yunfan Liu, Zhenan Sun
Date: 2019-03-06 00:00:00
Tasks: regression, Translation

Age progression and regression refers to aesthetically render-ing a given face image to present effects of face aging and rejuvenation, respectively. Although numerous studies have been conducted in this topic, there are two major problems: 1) multiple models are usually trained to simulate different age mappings, and 2) the photo-realism of generated face images is heavily influenced by the variation of training images in terms of pose, illumination, and background. To address these issues, in this paper, we propose a framework based on conditional Generative Adversarial Networks (cGANs) to achieve age progression and regression simultaneously. Particularly, since face aging and rejuvenation are largely different in terms of image translation patterns, we model these two processes using two separate generators, each dedicated to one age changing process. In addition, we exploit spatial attention mechanisms to limit image modifications to regions closely related to age changes, so that images with high visual fidelity could be synthesized for in-the-wild cases. Experiments on multiple datasets demonstrate the ability of our model in synthesizing lifelike face images at desired ages with personalized features well preserved, and keeping age-irrelevant regions unchanged.
Evolutionary Cell Aided Design for Neural Network Architectures
Authors: Philip Colangelo, Oren Segal, Alexander Speicher, Martin Margala
Date: 2019-03-06 00:00:00
Tasks: Efficient Neural Network, Evolutionary Algorithms, image-classification, Image Classification

Mathematical theory shows us that multilayer feedforward Artificial Neural Networks(ANNs) are universal function approximators, capable of approximating any measurable function to any desired degree of accuracy. In practice designing practical and efficient neural network architectures require significant effort and expertise. We present a novel software framework called Evolutionary Cell Aided Design(ECAD) meant to aid in the exploration and design of efficient Neural Network Architectures(NNAs) for reconfigurable hardware. Given a general neural network structure and a set of constraints and fitness functions, the framework will explore both the space of possible NNA and the space of possible hardware designs, using evolutionary algorithms, and attempt to find the fittest co-design solutions according to a predefined set of goals. We test the framework on an image classification task and use the MNIST data set of hand written digits with an Intel Arria 10 GX 1150 device as our target platform. We design and implement a modular and scalable 2D systolic array with enhancements for machine learning that can be used by the framework for the hardware search space. Our results demonstrate the ability to pair neural network design and hardware development together using an evolutionary algorithm and removing traditional human-in-the-loop development tasks. By running various experiments of the fittest solutions for neural network and hardware searches, we demonstrate the full end-to-end capabilities of the ECAD framework.
Graph-aware Modeling of Brain Connectivity Networks
Authors: Yura Kim, Daniel Kessler, Elizaveta Levina
Date: 2019-03-06 00:00:00

Functional connections in the brain are frequently represented by weighted networks, with nodes representing locations in the brain, and edges representing the strength of connectivity between these locations. One challenge in analyzing such data is that inference at the individual edge level is not particularly biologically meaningful; interpretation is more useful at the level of so-called functional regions, or groups of nodes and connections between them; this is often called "graph-aware" inference in the neuroimaging literature. However, pooling over functional regions leads to significant loss of information and lower accuracy. Another challenge is correlation among edge weights within a subject, which makes inference based on independence assumptions unreliable. We address both these challenges with a linear mixed effects model, which accounts for functional regions and for edge dependence, while still modeling individual edge weights to avoid loss of information. The model allows for comparing two populations, such as patients and healthy controls, both at the functional regions level and at individual edge level, leading to biologically meaningful interpretations. We fit this model to a resting state fMRI data on schizophrenics and healthy controls, obtaining interpretable results consistent with the schizophrenia literature.
Signed Link Prediction with Sparse Data: The Role of Personality Information
Authors: Ghazaleh Beigi, Suhas Ranganath, Huan Liu
Date: 2019-03-06 00:00:00
Tasks: Link Prediction, Prediction

Predicting signed links in social networks often faces the problem of signed
link data sparsity, i.e., only a small percentage of signed links are given.
The problem is exacerbated when the number of negative links is much smaller
than that of positive links. Boosting signed link prediction necessitates
additional information to compensate for data sparsity. According to psychology
theories, one rich source of such information is user's personality such as
optimism and pessimism that can help determine her propensity in establishing
positive and negative links. In this study, we investigate how personality
information can be obtained, and if personality information can help alleviate
the data sparsity problem for signed link prediction. We propose a novel signed
link prediction model that enables empirical exploration of user personality
via social media data. We evaluate our proposed model on two datasets of
real-world signed link networks. The results demonstrate the complementary role
of personality information in the signed link prediction problem. Experimental
results also indicate the effectiveness of different levels of personality
information for signed link data sparsity problem.
Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives
Authors: João Silvério, Yanlong Huang, Fares J. Abu-Dakka, Leonel Rozo, Darwin G. Caldwell
Date: 2019-03-05 00:00:00
Tasks: Imitation Learning

During the past few years, probabilistic approaches to imitation learning have earned a relevant place in the literature. One of their most prominent features, in addition to extracting a mean trajectory from task demonstrations, is that they provide a variance estimation. The intuitive meaning of this variance, however, changes across different techniques, indicating either variability or uncertainty. In this paper we leverage kernelized movement primitives (KMP) to provide a new perspective on imitation learning by predicting variability, correlations and uncertainty about robot actions. This rich set of information is used in combination with optimal controller fusion to learn actions from data, with two main advantages: i) robots become safe when uncertain about their actions and ii) they are able to leverage partial demonstrations, given as elementary sub-tasks, to optimally perform a higher level, more complex task. We showcase our approach in a painting task, where a human user and a KUKA robot collaborate to paint a wooden board. The task is divided into two sub-tasks and we show that using our approach the robot becomes compliant (hence safe) outside the training regions and executes the two sub-tasks with optimal gains.
Bounded Residual Gradient Networks (BReG-Net) for Facial Affect Computing
Authors: Behzad Hasani, Pooran Singh Negi, Mohammad H. Mahoor
Date: 2019-03-05 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Residual-based neural networks have shown remarkable results in various
visual recognition tasks including Facial Expression Recognition (FER). Despite
the tremendous efforts have been made to improve the performance of FER systems
using DNNs, existing methods are not generalizable enough for practical
applications. This paper introduces Bounded Residual Gradient Networks
(BReG-Net) for facial expression recognition, in which the shortcut connection
between the input and the output of the ResNet module is replaced with a
differentiable function with a bounded gradient. This configuration prevents
the network from facing the vanishing or exploding gradient problem. We show
that utilizing such non-linear units will result in shallower networks with
better performance. Further, by using a weighted loss function which gives a
higher priority to less represented categories, we can achieve an overall
better recognition rate. The results of our experiments show that BReG-Nets
outperform state-of-the-art methods on three publicly available facial
databases in the wild, on both the categorical and dimensional models of
affect.
Exploring the 3D architectures of deep material network in data-driven multiscale mechanics
Authors: Zeliang Liu, C. T. Wu
Date: 2019-01-02 00:00:00
Tasks: Model Compression

This paper extends the deep material network (DMN) proposed by Liu et al. (2019) to tackle general 3-dimensional (3D) problems with arbitrary material and geometric nonlinearities. It discovers a new way of describing multiscale heterogeneous materials by a multi-layer network structure and mechanistic building blocks. The data-driven framework of DMN is discussed in detail about the offline training and online extrapolation stages. Analytical solutions of the 3D building block with a two-layer structure in both small- and finite-strain formulations are derived based on interfacial equilibrium conditions and kinematic constraints. With linear elastic data generated by direct numerical simulations on a representative volume element (RVE), the network can be effectively trained in the offline stage using stochastic gradient descent and advanced model compression algorithms. Efficiency and accuracy of DMN on addressing the long-standing 3D RVE challenges with complex morphologies and material laws are validated through numerical experiments, including 1) hyperelastic particle-reinforced rubber composite with Mullins effect; 2) polycrystalline materials with rate-dependent crystal plasticity; 3) carbon fiber reinforced polymer (CFRP) composites with fiber anisotropic elasticity and matrix plasticity. In particular, we demonstrate a three-scale homogenization procedure of CFRP system by concatenating the microscale and mesoscale material networks. The complete learning and extrapolation procedures of DMN establish a reliable data-driven framework for multiscale material modeling and design.
Limitations of Pinned AUC for Measuring Unintended Bias
Authors: Daniel Borkan, Lucas Dixon, John Li, Jeffrey Sorensen, Nithum Thain, Lucy Vasserman
Date: 2019-03-05 00:00:00
Tasks: General Classification

This report examines the Pinned AUC metric introduced and highlights some of
its limitations. Pinned AUC provides a threshold-agnostic measure of unintended
bias in a classification model, inspired by the ROC-AUC metric. However, as we
highlight in this report, there are ways that the metric can obscure different
kinds of unintended biases when the underlying class distributions on which
bias is being measured are not carefully controlled.
Viewpoint Optimization for Autonomous Strawberry Harvesting with Deep Reinforcement Learning
Authors: Jonathon Sather, Xiaozheng Jane Zhang
Date: 2019-03-05 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Autonomous harvesting may provide a viable solution to mounting labor
pressures in the United States's strawberry industry. However, due to
bottlenecks in machine perception and economic viability, a profitable and
commercially adopted strawberry harvesting system remains elusive. In this
research, we explore the feasibility of using deep reinforcement learning to
overcome these bottlenecks and develop a practical algorithm to address the
sub-objective of viewpoint optimization, or the development of a control policy
to direct a camera to favorable vantage points for autonomous harvesting. We
evaluate the algorithm's performance in a custom, open-source simulated
environment and observe encouraging results. Our trained agent yields 8.7 times
higher returns than random actions and 8.8 percent faster exploration than our
best baseline policy, which uses visual servoing. Visual investigation shows
the agent is able to fixate on favorable viewpoints, despite having no explicit
means to propagate information through time. Overall, we conclude that deep
reinforcement learning is a promising area of research to advance the state of
the art in autonomous strawberry harvesting.
Defining Image Memorability using the Visual Memory Schema
Authors: Erdem Akagunduz, Adrian G. Bors, Karla K. Evans
Date: 2019-03-05 00:00:00
Tasks: Transfer Learning

Memorability of an image is a characteristic determined by the human
observers' ability to remember images they have seen. Yet recent work on image
memorability defines it as an intrinsic property that can be obtained
independent of the observer. {The current study aims to enhance our
understanding and prediction of image memorability, improving upon existing
approaches by incorporating the properties of cumulative human annotations.} We
propose a new concept called the Visual Memory Schema (VMS) referring to an
organisation of image components human observers share when encoding and
recognising images. The concept of VMS is operationalised by asking human
observers to define memorable regions of images they were asked to remember
during an episodic memory test. We then statistically assess the consistency of
VMSs across observers for either correctly or incorrectly recognised images.
The associations of the VMSs with eye fixations and saliency are analysed
separately as well. Lastly, we adapt various deep learning architectures for
the reconstruction and prediction of memorable regions in images and analyse
the results when using transfer learning at the outputs of different
convolutional network layers.
Size of Interventional Markov Equivalence Classes in Random DAG Models
Authors: Dmitriy Katz, Karthikeyan Shanmugam, Chandler Squires, Caroline Uhler
Date: 2019-03-05 00:00:00
Tasks: Causal Inference, Experimental Design

Directed acyclic graph (DAG) models are popular for capturing causal
relationships. From observational and interventional data, a DAG model can only
be determined up to its \emph{interventional Markov equivalence class} (I-MEC).
We investigate the size of MECs for random DAG models generated by uniformly
sampling and ordering an Erd\H{o}s-R\'{e}nyi graph. For constant density, we
show that the expected $\log$ observational MEC size asymptotically (in the
number of vertices) approaches a constant. We characterize I-MEC size in a
similar fashion in the above settings with high precision. We show that the
asymptotic expected number of interventions required to fully identify a DAG is
a constant. These results are obtained by exploiting Meek rules and coupling
arguments to provide sharp upper and lower bounds on the asymptotic quantities,
which are then calculated numerically up to high precision. Our results have
important consequences for experimental design of interventions and the
development of algorithms for causal inference.
Revisiting the Evaluation of Uncertainty Estimation and Its Application to Explore Model Complexity-Uncertainty Trade-Off
Authors: Yukun Ding, Jinglan Liu, JinJun Xiong, Yiyu Shi
Date: 2019-03-05 00:00:00
Tasks: Autonomous Driving

Accurately estimating uncertainties in neural network predictions is of great importance in building trusted DNNs-based models, and there is an increasing interest in providing accurate uncertainty estimation on many tasks, such as security cameras and autonomous driving vehicles. In this paper, we focus on the two main use cases of uncertainty estimation, i.e. selective prediction and confidence calibration. We first reveal potential issues of commonly used quality metrics for uncertainty estimation in both use cases, and propose our new metrics to mitigate them. We then apply these new metrics to explore the trade-off between model complexity and uncertainty estimation quality, a critically missing work in the literature. Our empirical experiment results validate the superiority of the proposed metrics, and some interesting trends about the complexity-uncertainty trade-off are observed.
On the Quantization of Cellular Neural Networks for Cyber-Physical Systems
Authors: Xiaowei Xu
Date: 2019-03-05 00:00:00
Tasks: Quantization

Cyber-Physical Systems (CPSs) have been pervasive including smart grid,
autonomous automobile systems, medical monitoring, process control systems,
robotics systems, and automatic pilot avionics. As usually implemented on
embedded devices, CPS is typically constrained by computation capacity and
energy consumption. In some CPS applications such as telemedicine and advanced
driving assistance system (ADAS), data processing on the embedded devices is
preferred due to security/safety and real-time requirement. Therefore, high
efficiency is highly desirable for such CPS applications. In this paper we
present CeNN quantization for high-efficient processing for CPS applications,
particularly telemedicine and ADAS applications. We systematically put forward
powers-of-two based incremental quantization of CeNNs for efficient hardware
implementation. The incremental quantization contains iterative procedures
including parameter partition, parameter quantization, and re-training. We
propose five different strategies including random strategy, pruning inspired
strategy, weighted pruning inspired strategy, nearest neighbor strategy, and
weighted nearest neighbor strategy. Experimental results show that our approach
can achieve a speedup up to 7.8x with no performance loss compared with the
state-of-the-art FPGA solutions for CeNNs.
Learning a Lattice Planner Control Set for Autonomous Vehicles
Authors: Ryan De Iaco, Stephen L. Smith, Krzysztof Czarnecki
Date: 2019-03-05 00:00:00
Tasks: Autonomous Vehicles

This paper introduces a method to compute a sparse lattice planner control
set that is suited to a particular task by learning from a representative
dataset of vehicle paths. To do this, we use a scoring measure similar to the
Fr\'echet distance and propose an algorithm for evaluating a given control set
according to the scoring measure. Control actions are then selected from a
dense control set according to an objective function that rewards improvements
in matching the dataset while also encouraging sparsity. This method is
evaluated across several experiments involving real and synthetic datasets, and
it is shown to generate smaller control sets when compared to the previous
state-of-the-art lattice control set computation technique, with these smaller
control sets maintaining a high degree of manoeuvrability in the required task.
This results in a planning time speedup of up to 4.31x when using the learned
control set over the state-of-the-art computed control set. In addition, we
show the learned control sets are better able to capture the driving style of
the dataset in terms of path curvature.
Abnormal Chest X-ray Identification With Generative Adversarial One-Class Classifier
Authors: Yu-Xing Tang, You-Bao Tang, Mei Han, Jing Xiao, Ronald M. Summers
Date: 2019-03-05 00:00:00
Tasks: Diagnostic, One-class classifier

Being one of the most common diagnostic imaging tests, chest radiography
requires timely reporting of potential findings in the images. In this paper,
we propose an end-to-end architecture for abnormal chest X-ray identification
using generative adversarial one-class learning. Unlike previous approaches,
our method takes only normal chest X-ray images as input. The architecture is
composed of three deep neural networks, each of which learned by competing
while collaborating among them to model the underlying content structure of the
normal chest X-rays. Given a chest X-ray image in the testing phase, if it is
normal, the learned architecture can well model and reconstruct the content; if
it is abnormal, since the content is unseen in the training phase, the model
would perform poorly in its reconstruction. It thus enables distinguishing
abnormal chest X-rays from normal ones. Quantitative and qualitative
experiments demonstrate the effectiveness and efficiency of our approach, where
an AUC of 0.841 is achieved on the challenging NIH Chest X-ray dataset in a
one-class learning setting, with the potential in reducing the workload for
radiologists.
Crowd Counting Using Scale-Aware Attention Networks
Authors: Mohammad Asiful Hossain, Mehrdad Hosseinzadeh, Omit Chanda, Yang Wang
Date: 2019-03-05 00:00:00
Tasks: Crowd Counting

In this paper, we consider the problem of crowd counting in images. Given an
image of a crowded scene, our goal is to estimate the density map of this
image, where each pixel value in the density map corresponds to the crowd
density at the corresponding location in the image. Given the estimated density
map, the final crowd count can be obtained by summing over all values in the
density map. One challenge of crowd counting is the scale variation in images.
In this work, we propose a novel scale-aware attention network to address this
challenge. Using the attention mechanism popular in recent deep learning
architectures, our model can automatically focus on certain global and local
scales appropriate for the image. By combining these global and local scale
attention, our model outperforms other state-of-the-art methods for crowd
counting on several benchmark datasets.