pwc-archive/papers-with-abstracts huggingface dataset:
A Deep Look into Neural Ranking Models for Information Retrieval
Authors: Jiafeng Guo, Yixing Fan, Liang Pang, Liu Yang, Qingyao Ai, Hamed Zamani, Chen Wu, W. Bruce Croft, Xue-Qi Cheng
Date: 2019-03-16 00:00:00
Tasks: Information Retrieval, Retrieval

Ranking models lie at the heart of research on information retrieval (IR). During the past decades, different techniques have been proposed for constructing ranking models, from traditional heuristic methods, probabilistic methods, to modern machine learning methods. Recently, with the advance of deep learning technology, we have witnessed a growing body of work in applying shallow or deep neural networks to the ranking problem in IR, referred to as neural ranking models in this paper. The power of neural ranking models lies in the ability to learn from the raw text inputs for the ranking problem to avoid many limitations of hand-crafted features. Neural networks have sufficient capacity to model complicated tasks, which is needed to handle the complexity of relevance estimation in ranking. Since there have been a large variety of neural ranking models proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we will take a deep look into the neural ranking models from different dimensions to analyze their underlying assumptions, major design principles, and learning strategies. We compare these models through benchmark tasks to obtain a comprehensive empirical understanding of the existing techniques. We will also discuss what is missing in the current literature and what are the promising and desired future directions.
Emotion Action Detection and Emotion Inference: the Task and Dataset
Authors: Pengyuan Liu, Chengyu Du, Shuofeng Zhao, Chenghao Zhu
Date: 2019-03-16 00:00:00
Tasks: Action Detection, Emotion Classification, Emotion Recognition, General Classification

Many Natural Language Processing works on emotion analysis only focus on
simple emotion classification without exploring the potentials of putting
emotion into "event context", and ignore the analysis of emotion-related
events. One main reason is the lack of this kind of corpus. Here we present
Cause-Emotion-Action Corpus, which manually annotates not only emotion, but
also cause events and action events. We propose two new tasks based on the
data-set: emotion causality and emotion inference. The first task is to extract
a triple (cause, emotion, action). The second task is to infer the probable
emotion. We are currently releasing the data-set with 10,603 samples and 15,892
events, basic statistic analysis and baseline on both emotion causality and
emotion inference tasks. Baseline performance demonstrates that there is much
room for both tasks to be improved.
Ontology Based Global and Collective Motion Patterns for Event Classification in Basketball Videos
Authors: Lifang Wu, Zhou Yang, Jiaoyu He, Meng Jian, Yaowen Xu, Dezhong Xu, Chang Wen Chen
Date: 2019-03-16 00:00:00
Tasks: Classification, General Classification, Optical Flow Estimation

In multi-person videos, especially team sport videos, a semantic event is
usually represented as a confrontation between two teams of players, which can
be represented as collective motion. In broadcast basketball videos, specific
camera motions are used to present specific events. Therefore, a semantic event
in broadcast basketball videos is closely related to both the global motion
(camera motion) and the collective motion. A semantic event in basketball
videos can be generally divided into three stages: pre-event, event occurrence
(event-occ), and post-event. In this paper, we propose an ontology-based global
and collective motion pattern (On_GCMP) algorithm for basketball event
classification. First, a two-stage GCMP based event classification scheme is
proposed. The GCMP is extracted using optical flow. The two-stage scheme
progressively combines a five-class event classification algorithm on
event-occs and a two-class event classification algorithm on pre-events. Both
algorithms utilize sequential convolutional neural networks (CNNs) and long
short-term memory (LSTM) networks to extract the spatial and temporal features
of GCMP for event classification. Second, we utilize post-event segments to
predict success/failure using deep features of images in the video frames
(RGB_DF_VF) based algorithms. Finally the event classification results and
success/failure classification results are integrated to obtain the final
results. To evaluate the proposed scheme, we collected a new dataset called
NCAA+, which is automatically obtained from the NCAA dataset by extending the
fixed length of video clips forward and backward of the corresponding semantic
events. The experimental results demonstrate that the proposed scheme achieves
the mean average precision of 58.10% on NCAA+. It is higher by 6.50% than
state-of-the-art on NCAA.
Spherical Principal Component Analysis
Authors: Kai Liu, Qiuwei Li, Hua Wang, Gongguo Tang
Date: 2019-03-16 00:00:00
Tasks: Clustering

Principal Component Analysis (PCA) is one of the most important methods to
handle high dimensional data. However, most of the studies on PCA aim to
minimize the loss after projection, which usually measures the Euclidean
distance, though in some fields, angle distance is known to be more important
and critical for analysis. In this paper, we propose a method by adding
constraints on factors to unify the Euclidean distance and angle distance.
However, due to the nonconvexity of the objective and constraints, the
optimized solution is not easy to obtain. We propose an alternating linearized
minimization method to solve it with provable convergence rate and guarantee.
Experiments on synthetic data and real-world datasets have validated the
effectiveness of our method and demonstrated its advantages over state-of-art
clustering methods.
A Provably Communication-Efficient Asynchronous Distributed Inference Method for Convex and Nonconvex Problems
Authors: Jineng Ren, Jarvis Haupt
Date: 2019-03-16 00:00:00
Tasks: Distributed Optimization

This paper proposes and analyzes a communication-efficient distributed
optimization framework for general nonconvex nonsmooth signal processing and
machine learning problems under an asynchronous protocol. At each iteration,
worker machines compute gradients of a known empirical loss function using
their own local data, and a master machine solves a related minimization
problem to update the current estimate. We prove that for nonconvex nonsmooth
problems, the proposed algorithm converges with a sublinear rate over the
number of communication rounds, coinciding with the best theoretical rate that
can be achieved for this class of problems. Linear convergence is established
without any statistical assumptions of the local data for problems
characterized by composite loss functions whose smooth parts are strongly
convex. Extensive numerical experiments verify that the performance of the
proposed approach indeed improves -- sometimes significantly -- over other
state-of-the-art algorithms in terms of total communication efficiency.
Learning Super-resolution 3D Segmentation of Plant Root MRI Images from Few Examples
Authors: Ali Oguz Uzman, Jannis Horn, Sven Behnke
Date: 2019-03-16 00:00:00
Tasks: Super-Resolution

Analyzing plant roots is crucial to understand plant performance in different
soil environments. While magnetic resonance imaging (MRI) can be used to obtain
3D images of plant roots, extracting the root structural model is challenging
due to highly noisy soil environments and low-resolution of MRI images. To
improve both contrast and resolution, we adapt the state-of-the-art method
RefineNet for 3D segmentation of the plant root MRI images in super-resolution.
The networks are trained from few manual segmentations that are augmented by
geometric transformations, realistic noise, and other variabilities. The
resulting segmentations contain most root structures, including branches not
extracted by the human annotator.
Safe Coordination of Human-Robot Firefighting Teams
Authors: Esmaeil Seraj, Andrew Silva, Matthew Gombolay
Date: 2019-03-16 00:00:00

Wildfires are destructive and inflict massive, irreversible harm to victims'
lives and natural resources. Researchers have proposed commissioning unmanned
aerial vehicles (UAVs) to provide firefighters with real-time tracking
information; yet, these UAVs are not able to reason about a fire's track,
including current location, measurement, and uncertainty, as well as
propagation. We propose a model-predictive, probabilistically safe distributed
control algorithm for human-robot collaboration in wildfire fighting. The
proposed algorithm overcomes the limitations of prior work by explicitly
estimating the latent fire propagation dynamics to enable intelligent,
time-extended coordination of the UAVs in support of on-the-ground human
firefighters. We derive a novel, analytical bound that enables UAVs to
distribute their resources and provides a probabilistic guarantee of the
humans' safety while preserving the UAVs' ability to cover an entire fire.
Directional PointNet: 3D Environmental Classification for Wearable Robotics
Authors: Kuangen Zhang, Jing Wang, Chenglong Fu
Date: 2019-03-16 00:00:00
Tasks: Classification, General Classification

Environmental information can provide reliable prior information about human
motion intent, which can aid the subject with wearable robotics to walk in
complex environments. Previous researchers have utilized 1D signal and 2D
images to classify environments, but they may face the problems of
self-occlusion. Comparatively, 3D point cloud can be more appropriate to depict
environments, thus we propose a directional PointNet to classify 3D point cloud
directly. By utilizing the orientation information of the point cloud, the
directional PointNet can classify daily terrains, including level ground, up
stairs, and down stairs, and the classification accuracy achieves 99% for
testing set. Moreover, the directional PointNet is more efficient than the
previous PointNet because the T-net, which is utilized to estimate the
transformation of the point cloud, is removed in this research and the length
of the global feature is optimized. The experimental results demonstrate that
the directional PointNet can classify the environments robustly and
efficiently.
Visual recognition in the wild by sampling deep similarity functions
Authors: Mikhail Usvyatsov, Konrad Schindler
Date: 2019-03-15 00:00:00
Tasks: Object Recognition

Recognising relevant objects or object states in its environment is a basic
capability for an autonomous robot. The dominant approach to object recognition
in images and range images is classification by supervised machine learning,
nowadays mostly with deep convolutional neural networks (CNNs). This works well
for target classes whose variability can be completely covered with training
examples. However, a robot moving in the wild, i.e., in an environment that is
not known at the time the recognition system is trained, will often face
\emph{domain shift}: the training data cannot be assumed to exhaustively cover
all the within-class variability that will be encountered in the test data. In
that situation, learning is in principle possible, since the training set does
capture the defining properties, respectively dissimilarities, of the target
classes. But directly training a CNN to predict class probabilities is prone to
overfitting to irrelevant correlations between the class labels and the
specific subset of the target class that is represented in the training set. We
explore the idea to instead learn a Siamese CNN that acts as similarity
function between pairs of training examples. Class predictions are then
obtained by measuring the similarities between a new test instance and the
training samples. We show that the CNN embedding correctly recovers the
relative similarities to arbitrary class exemplars in the training set. And
that therefore few, randomly picked training exemplars are sufficient to
achieve good predictions, making the procedure efficient.
Detecting GAN generated Fake Images using Co-occurrence Matrices
Authors: Lakshmanan Nataraj, Tajuddin Manhar Mohammed, Shivkumar Chandrasekaran, Arjuna Flenner, Jawadul H. Bappy, Amit K. Roy-Chowdhury, B. S. Manjunath
Date: 2019-03-15 00:00:00

The advent of Generative Adversarial Networks (GANs) has brought about completely novel ways of transforming and manipulating pixels in digital images. GAN based techniques such as Image-to-Image translations, DeepFakes, and other automated methods have become increasingly popular in creating fake images. In this paper, we propose a novel approach to detect GAN generated fake images using a combination of co-occurrence matrices and deep learning. We extract co-occurrence matrices on three color channels in the pixel domain and train a model using a deep convolutional neural network (CNN) framework. Experimental results on two diverse and challenging GAN datasets comprising more than 56,000 images based on unpaired image-to-image translations (cycleGAN [1]) and facial attributes/expressions (StarGAN [2]) show that our approach is promising and achieves more than 99% classification accuracy in both datasets. Further, our approach also generalizes well and achieves good results when trained on one dataset and tested on the other.
Generate What You Can't See - a View-dependent Image Generation
Authors: Karol Piaskowski, Rafal Staszak, Dominik Belter
Date: 2019-03-15 00:00:00
Tasks: Image Generation, Object

In order to operate autonomously, a robot should explore the environment and
build a model of each of the surrounding objects. A common approach is to
carefully scan the whole workspace. This is time-consuming. It is also often
impossible to reach all the viewpoints required to acquire full knowledge about
the environment. Humans can perform shape completion of occluded objects by
relying on past experience. Therefore, we propose a method that generates
images of an object from various viewpoints using a single input RGB image. A
deep neural network is trained to imagine the object appearance from many
viewpoints. We present the whole pipeline, which takes a single RGB image as
input and returns a sequence of RGB and depth images of the object. The method
utilizes a CNN-based object detector to extract the object from the natural
scene. Then, the proposed network generates a set of RGB and depth images. We
show the results both on a synthetic dataset and on real images.
Multi-camera calibration with pattern rigs, including for non-overlapping cameras: CALICO
Authors: Amy Tabb, Henry Medeiros, Mitchell J. Feldmann, Thiago T. Santos
Date: 2019-03-15 00:00:00
Tasks: Camera Calibration, Object Reconstruction

This paper describes CALICO, a method for multi-camera calibration suitable for challenging contexts: stationary and mobile multi-camera systems, cameras without overlapping fields of view, and non-synchronized cameras. Recent approaches are roughly divided into infrastructure- and pattern-based. Infrastructure-based approaches use the scene's features to calibrate, while pattern-based approaches use calibration patterns. Infrastructure-based approaches are not suitable for stationary camera systems, and pattern-based approaches may constrain camera placement because shared fields of view or extremely large patterns are required. CALICO is a pattern-based approach, where the multi-calibration problem is formulated using rigidity constraints between patterns and cameras. We use a {\it pattern rig}: several patterns rigidly attached to each other or some structure. We express the calibration problem as that of algebraic and reprojection error minimization problems. Simulated and real experiments demonstrate the method in a variety of settings. CALICO compared favorably to Kalibr. Mean reconstruction accuracy error was $\le 0.71$ mm for real camera rigs, and $\le 1.11$ for simulated camera rigs. Code and data releases are available at \cite{tabb_amy_2019_3520866} and \url{https://github.com/amy-tabb/calico}.
Online Antenna Tuning in Heterogeneous Cellular Networks with Deep Reinforcement Learning
Authors: Eren Balevi, Jeffrey G. Andrews
Date: 2019-03-15 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We aim to jointly optimize antenna tilt angle, and vertical and horizontal half-power beamwidths of the macrocells in a heterogeneous cellular network (HetNet). The interactions between the cells, most notably due to their coupled interference render this optimization prohibitively complex. Utilizing a single agent reinforcement learning (RL) algorithm for this optimization becomes quite suboptimum despite its scalability, whereas multi-agent RL algorithms yield better solutions at the expense of scalability. Hence, we propose a compromise algorithm between these two. Specifically, a multi-agent mean field RL algorithm is first utilized in the offline phase so as to transfer information as features for the second (online) phase single agent RL algorithm, which employs a deep neural network to learn users locations. This two-step approach is a practical solution for real deployments, which should automatically adapt to environmental changes in the network. Our results illustrate that the proposed algorithm approaches the performance of the multi-agent RL, which requires millions of trials, with hundreds of online trials, assuming relatively low environmental dynamics, and performs much better than a single agent RL. Furthermore, the proposed algorithm is compact and implementable, and empirically appears to provide a performance guarantee regardless of the amount of environmental dynamics.
Zero Shot Learning with the Isoperimetric Loss
Authors: Shay Deutsch, Andrea Bertozzi, Stefano Soatto
Date: 2019-03-15 00:00:00
Tasks: Zero-Shot Learning

We introduce the isoperimetric loss as a regularization criterion for learning the map from a visual representation to a semantic embedding, to be used to transfer knowledge to unknown classes in a zero-shot learning setting. We use a pre-trained deep neural network model as a visual representation of image data, a Word2Vec embedding of class labels, and linear maps between the visual and semantic embedding spaces. However, the spaces themselves are not linear, and we postulate the sample embedding to be populated by noisy samples near otherwise smooth manifolds. We exploit the graph structure defined by the sample points to regularize the estimates of the manifolds by inferring the graph connectivity using a generalization of the isoperimetric inequalities from Riemannian geometry to graphs. Surprisingly, this regularization alone, paired with the simplest baseline model, outperforms the state-of-the-art among fully automated methods in zero-shot learning benchmarks such as AwA and CUB. This improvement is achieved solely by learning the structure of the underlying spaces by imposing regularity.
Smart, Deep Copy-Paste
Authors: Tiziano Portenier, Qiyang Hu, Paolo Favaro, Matthias Zwicker
Date: 2019-03-15 00:00:00

In this work, we propose a novel system for smart copy-paste, enabling the
synthesis of high-quality results given a masked source image content and a
target image context as input. Our system naturally resolves both shading and
geometric inconsistencies between source and target image, resulting in a
merged result image that features the content from the pasted source image,
seamlessly pasted into the target context. Our framework is based on a novel
training image transformation procedure that allows to train a deep
convolutional neural network end-to-end to automatically learn a representation
that is suitable for copy-pasting. Our training procedure works with any image
dataset without additional information such as labels, and we demonstrate the
effectiveness of our system on two popular datasets, high-resolution face
images and the more complex Cityscapes dataset. Our technique outperforms the
current state of the art on face images, and we show promising results on the
Cityscapes dataset, demonstrating that our system generalizes to much higher
resolution than the training data.
Algorithms for Verifying Deep Neural Networks
Authors: Changliu Liu, Tomer Arnon, Christopher Lazarus, Clark Barrett, Mykel J. Kochenderfer
Date: 2019-03-15 00:00:00

Deep neural networks are widely used for nonlinear function approximation with applications ranging from computer vision to control. Although these networks involve the composition of simple arithmetic operations, it can be very challenging to verify whether a particular network satisfies certain input-output properties. This article surveys methods that have emerged recently for soundly verifying such properties. These methods borrow insights from reachability analysis, optimization, and search. We discuss fundamental differences and connections between existing algorithms. In addition, we provide pedagogical implementations of existing methods and compare them on a set of benchmark problems.
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset
Authors: Ruohan Zhang, Calen Walshe, Zhuode Liu, Lin Guan, Karl S. Muller, Jake A. Whritner, Luxin Zhang, Mary M. Hayhoe, Dana H. Ballard
Date: 2019-03-15 00:00:00
Tasks: Decision Making, Imitation Learning, Reinforcement Learning

Large-scale public datasets have been shown to benefit research in multiple areas of modern artificial intelligence. For decision-making research that requires human data, high-quality datasets serve as important benchmarks to facilitate the development of new methods by providing a common reproducible standard. Many human decision-making tasks require visual attention to obtain high levels of performance. Therefore, measuring eye movements can provide a rich source of information about the strategies that humans use to solve decision-making tasks. Here, we provide a large-scale, high-quality dataset of human actions with simultaneously recorded eye movements while humans play Atari video games. The dataset consists of 117 hours of gameplay data from a diverse set of 20 games, with 8 million action demonstrations and 328 million gaze samples. We introduce a novel form of gameplay, in which the human plays in a semi-frame-by-frame manner. This leads to near-optimal game decisions and game scores that are comparable or better than known human records. We demonstrate the usefulness of the dataset through two simple applications: predicting human gaze and imitating human demonstrated actions. The quality of the data leads to promising results in both tasks. Moreover, using a learned human gaze model to inform imitation learning leads to an 115\% increase in game performance. We interpret these results as highlighting the importance of incorporating human visual attention in models of decision making and demonstrating the value of the current dataset to the research community. We hope that the scale and quality of this dataset can provide more opportunities to researchers in the areas of visual attention, imitation learning, and reinforcement learning.