pwc-archive/papers-with-abstracts huggingface dataset:
Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification
Authors: Andrew Patterson, Arun Lakshmanan, Naira Hovakimyan
Date: 2019-04-04 00:00:00
Tasks: Prediction, Uncertainty Quantification

Collision prediction in a dynamic and unknown environment relies on knowledge
of how the environment is changing. Many collision prediction methods rely on
deterministic knowledge of how obstacles are moving in the environment.
However, complete deterministic knowledge of the obstacles' motion is often
unavailable. This work proposes a Gaussian process based prediction method that
replaces the assumption of deterministic knowledge of each obstacle's future
behavior with probabilistic knowledge, to allow a larger class of obstacles to
be considered. The method solely relies on position and velocity measurements
to predict collisions with dynamic obstacles. We show that the uncertainty
region for obstacle positions can be expressed in terms of a combination of
polynomials generated with Gaussian process regression. To control the growth
of uncertainty over arbitrary time horizons, a probabilistic obstacle intention
is assumed as a distribution over obstacle positions and velocities, which can
be naturally included in the Gaussian process framework. Our approach is
demonstrated in two case studies in which (i), an obstacle overtakes the agent
and (ii), an obstacle crosses the agent's path perpendicularly. In these
simulations we show that the collision can be predicted despite having limited
knowledge of the obstacle's behavior.
Learning Implicit Generative Models by Matching Perceptual Features
Authors: Cicero Nogueira dos Santos, Youssef Mroueh, Inkit Padhi, Pierre Dognin
Date: 2019-04-04 00:00:00
Proceeding: ICCV 2019 10
Tasks: Style Transfer, Super-Resolution, Transfer Learning

Perceptual features (PFs) have been used with great success in tasks such as
transfer learning, style transfer, and super-resolution. However, the efficacy
of PFs as key source of information for learning generative models is not well
studied. We investigate here the use of PFs in the context of learning implicit
generative models through moment matching (MM). More specifically, we propose a
new effective MM approach that learns implicit generative models by performing
mean and covariance matching of features extracted from pretrained ConvNets.
Our proposed approach improves upon existing MM methods by: (1) breaking away
from the problematic min/max game of adversarial learning; (2) avoiding online
learning of kernel functions; and (3) being efficient with respect to both
number of used moments and required minibatch size. Our experimental results
demonstrate that, due to the expressiveness of PFs from pretrained deep
ConvNets, our method achieves state-of-the-art results for challenging
benchmarks.
Blind Visual Motif Removal from a Single Image
Authors: Amir Hertz, Sharon Fogel, Rana Hanocka, Raja Giryes, Daniel Cohen-Or
Date: 2019-04-04 00:00:00
Proceeding: CVPR 2019 6

Many images shared over the web include overlaid objects, or visual motifs,
such as text, symbols or drawings, which add a description or decoration to the
image. For example, decorative text that specifies where the image was taken,
repeatedly appears across a variety of different images. Often, the reoccurring
visual motif, is semantically similar, yet, differs in location, style and
content (e.g. text placement, font and letters). This work proposes a deep
learning based technique for blind removal of such objects. In the blind
setting, the location and exact geometry of the motif are unknown. Our approach
simultaneously estimates which pixels contain the visual motif, and synthesizes
the underlying latent image. It is applied to a single input image, without any
user assistance in specifying the location of the motif, achieving
state-of-the-art results for blind removal of both opaque and semi-transparent
visual motifs.
ExCL: Extractive Clip Localization Using Natural Language Descriptions
Authors: Soham Ghosh, Anuva Agarwal, Zarana Parekh, Alexander Hauptmann
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6

The task of retrieving clips within videos based on a given natural language
query requires cross-modal reasoning over multiple frames. Prior approaches
such as sliding window classifiers are inefficient, while text-clip similarity
driven ranking-based approaches such as segment proposal networks are far more
complicated. In order to select the most relevant video clip corresponding to
the given text description, we propose a novel extractive approach that
predicts the start and end frames by leveraging cross-modal interactions
between the text and video - this removes the need to retrieve and re-rank
multiple proposal segments. Using recurrent networks we encode the two
modalities into a joint representation which is then used in different variants
of start-end frame predictor networks. Through extensive experimentation and
ablative analysis, we demonstrate that our simple and elegant approach
significantly outperforms state of the art on two datasets and has comparable
performance on a third.
DeceptionNet: Network-Driven Domain Randomization
Authors: Sergey Zakharov, Wadim Kehl, Slobodan Ilic
Date: 2019-04-04 00:00:00
Proceeding: ICCV 2019 10
Tasks: Colorization, Domain Adaptation, Pose Estimation, Semantic Segmentation

We present a novel approach to tackle domain adaptation between synthetic and real data. Instead, of employing "blind" domain randomization, i.e., augmenting synthetic renderings with random backgrounds or changing illumination and colorization, we leverage the task network as its own adversarial guide toward useful augmentations that maximize the uncertainty of the output. To this end, we design a min-max optimization scheme where a given task competes against a special deception network to minimize the task error subject to the specific constraints enforced by the deceiver. The deception network samples from a family of differentiable pixel-level perturbations and exploits the task architecture to find the most destructive augmentations. Unlike GAN-based approaches that require unlabeled data from the target domain, our method achieves robust mappings that scale well to multiple target distributions from source data alone. We apply our framework to the tasks of digit recognition on enhanced MNIST variants, classification and object pose estimation on the Cropped LineMOD dataset as well as semantic segmentation on the Cityscapes dataset and compare it to a number of domain adaptation approaches, thereby demonstrating similar results with superior generalization capabilities.
Learning to Cluster Faces on an Affinity Graph
Authors: Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin
Date: 2019-04-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Clustering, Face Recognition, Open-Ended Question Answering

Face recognition sees remarkable progress in recent years, and its performance has reached a very high level. Taking it to a next level requires substantially larger data, which would involve prohibitive annotation cost. Hence, exploiting unlabeled data becomes an appealing alternative. Recent works have shown that clustering unlabeled faces is a promising approach, often leading to notable performance gains. Yet, how to effectively cluster, especially on a large-scale (i.e. million-level or above) dataset, remains an open question. A key challenge lies in the complex variations of cluster patterns, which make it difficult for conventional clustering methods to meet the needed accuracy. This work explores a novel approach, namely, learning to cluster instead of relying on hand-crafted criteria. Specifically, we propose a framework based on graph convolutional network, which combines a detection and a segmentation module to pinpoint face clusters. Experiments show that our method yields significantly more accurate face clusters, which, as a result, also lead to further performance gain in face recognition.
Neural Models of the Psychosemantics of `Most'
Authors: Lewis O'Sullivan, Shane Steinert-Threlkeld
Date: 2019-04-04 00:00:00
Proceeding: WS 2019 6

How are the meanings of linguistic expressions related to their use in
concrete cognitive tasks? Visual identification tasks show human speakers can
exhibit considerable variation in their understanding, representation and
verification of certain quantifiers. This paper initiates an investigation into
neural models of these psycho-semantic tasks. We trained two types of network
-- a convolutional neural network (CNN) model and a recurrent model of visual
attention (RAM) -- on the "most" verification task from \citet{Pietroski2009},
manipulating the visual scene and novel notions of task duration. Our results
qualitatively mirror certain features of human performance (such as sensitivity
to the ratio of set sizes, indicating a reliance on approximate number) while
differing in interesting ways (such as exhibiting a subtly different pattern
for the effect of image type). We conclude by discussing the prospects for
using neural models as cognitive models of this and other psychosemantic tasks.
Inferring the dynamics of oscillatory systems using recurrent neural networks
Authors: Rok Cestnik, Markus Abel
Date: 2019-04-04 00:00:00

We investigate the predictive power of recurrent neural networks for oscillatory systems not only on the attractor, but in its vicinity as well. For this we consider systems perturbed by an external force. This allows us to not merely predict the time evolution of the system, but also study its dynamical properties, such as bifurcations, dynamical response curves, characteristic exponents etc. It is shown that they can be effectively estimated even in some regions of the state space where no input data were given. We consider several different oscillatory examples, including self-sustained, excitatory, time-delay and chaotic systems. Furthermore, with a statistical analysis we assess the amount of training data required for effective inference for two common recurrent neural network cells, the long short-term memory and the gated recurrent unit.
Transfer Learning for Performance Modeling of Deep Neural Network Systems
Authors: Md Shahriar Iqbal, Lars Kotthoff, Pooyan Jamshidi
Date: 2019-04-04 00:00:00
Tasks: Transfer Learning

Modern deep neural network (DNN) systems are highly configurable with large a
number of options that significantly affect their non-functional behavior, for
example inference time and energy consumption. Performance models allow to
understand and predict the effects of such configuration options on system
behavior, but are costly to build because of large configuration spaces.
Performance models from one environment cannot be transferred directly to
another; usually models are rebuilt from scratch for different environments,
for example different hardware. Recently, transfer learning methods have been
applied to reuse knowledge from performance models trained in one environment
in another. In this paper, we perform an empirical study to understand the
effectiveness of different transfer learning strategies for building
performance models of DNN systems. Our results show that transferring
information on the most influential configuration options and their
interactions is an effective way of reducing the cost to build performance
models in new environments.
A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning
Authors: Duo Wang, Yu Cheng, Mo Yu, Xiaoxiao Guo, Tao Zhang
Date: 2019-04-04 00:00:00
Tasks: Few-Shot Learning, General Classification, image-classification, Image Classification, Meta-Learning, Metric Learning, Parameter Prediction

Few-shot learning aims to learn classifiers for new classes with only a few training examples per class. Most existing few-shot learning approaches belong to either metric-based meta-learning or optimization-based meta-learning category, both of which have achieved successes in the simplified "$k$-shot $N$-way" image classification settings. Specifically, the optimization-based approaches train a meta-learner to predict the parameters of the task-specific classifiers. The task-specific classifiers are required to be homogeneous-structured to ease the parameter prediction, so the meta-learning approaches could only handle few-shot learning problems where the tasks share a uniform number of classes. The metric-based approaches learn one task-invariant metric for all the tasks. Even though the metric-learning approaches allow different numbers of classes, they require the tasks all coming from a similar domain such that there exists a uniform metric that could work across tasks. In this work, we propose a hybrid meta-learning model called Meta-Metric-Learner which combines the merits of both optimization- and metric-based approaches. Our meta-metric-learning approach consists of two components, a task-specific metric-based learner as a base model, and a meta-learner that learns and specifies the base model. Thus our model is able to handle flexible numbers of classes as well as generate more generalized metrics for classification across tasks. We test our approach in the standard "$k$-shot $N$-way" few-shot learning setting following previous works and a new realistic few-shot setting with flexible class numbers in both single-source form and multi-source forms. Experiments show that our approach can obtain superior performance in all settings.
Open Issues in Combating Fake News: Interpretability as an Opportunity
Authors: Sina Mohseni, Eric Ragan, Xia Hu
Date: 2019-04-04 00:00:00
Tasks: BIG-bench Machine Learning, Fake News Detection, Interpretable Machine Learning

Combating fake news needs a variety of defense methods. Although rumor
detection and various linguistic analysis techniques are common methods to
detect false content in social media, there are other feasible mitigation
approaches that could be explored in the machine learning community. In this
paper, we present open issues and opportunities in fake news research that need
further attention. We first review different stages of the news life cycle in
social media and discuss core vulnerability issues for news feed algorithms in
propagating fake news content with three examples. We then discuss how
complexity and unclarity of the fake news problem limit the advancements in
this field. Lastly, we present research opportunities from interpretable
machine learning to mitigate fake news problems with 1) interpretable fake news
detection and 2) transparent news feed algorithms. We propose three dimensions
of interpretability consisting of algorithmic interpretability, human
interpretability, and the inclusion of supporting evidence that can benefit
fake news mitigation methods in different ways.
Speech Model Pre-training for End-to-End Spoken Language Understanding
Authors: Loren Lugosch, Mirco Ravanelli, Patrick Ignoto, Vikrant Singh Tomar, Yoshua Bengio
Date: 2019-04-07 00:00:00
Tasks: Speech-to-Text, Spoken Language Understanding

Whereas conventional spoken language understanding (SLU) systems map speech to text, and then text to intent, end-to-end SLU systems map speech directly to intent through a single trainable model. Achieving high accuracy with these end-to-end models without a large amount of training data is difficult. We propose a method to reduce the data requirements of end-to-end SLU in which the model is first pre-trained to predict words and phonemes, thus learning good features for SLU. We introduce a new SLU dataset, Fluent Speech Commands, and show that our method improves performance both when the full dataset is used for training and when only a small subset is used. We also describe preliminary experiments to gauge the model's ability to generalize to new phrases not heard during training.
A Closer Look at Few-shot Classification
Authors: Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, Jia-Bin Huang
Date: 2019-04-08 00:00:00
Proceeding: ICLR 2019 5
Tasks: Domain Generalization, Few-Shot Learning, General Classification

Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples. While significant progress has been made, the growing complexity of network designs, meta-learning algorithms, and differences in implementation details make a fair comparison difficult. In this paper, we present 1) a consistent comparative analysis of several representative few-shot classification algorithms, with results showing that deeper backbones significantly reduce the performance differences among methods on datasets with limited domain differences, 2) a modified baseline method that surprisingly achieves competitive performance when compared with the state-of-the-art on both the \miniI and the CUB datasets, and 3) a new experimental setting for evaluating the cross-domain generalization ability for few-shot classification algorithms. Our results reveal that reducing intra-class variation is an important factor when the feature backbone is shallow, but not as critical when using deeper backbones. In a realistic cross-domain evaluation setting, we show that a baseline method with a standard fine-tuning practice compares favorably against other state-of-the-art few-shot learning algorithms.
Relational Action Forecasting
Authors: Chen Sun, Abhinav Shrivastava, Carl Vondrick, Rahul Sukthankar, Kevin Murphy, Cordelia Schmid
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Classification, Action Recognition, General Classification

This paper focuses on multi-person action forecasting in videos. More
precisely, given a history of H previous frames, the goal is to detect actors
and to predict their future actions for the next T frames. Our approach jointly
models temporal and spatial interactions among different actors by constructing
a recurrent graph, using actor proposals obtained with Faster R-CNN as nodes.
Our method learns to select a subset of discriminative relations without
requiring explicit supervision, thus enabling us to tackle challenging visual
data. We refer to our model as Discriminative Relational Recurrent Network
(DRRN). Evaluation of action prediction on AVA demonstrates the effectiveness
of our proposed method compared to simpler baselines. Furthermore, we
significantly improve performance on the task of early action classification on
J-HMDB, from the previous SOTA of 48% to 60%.
Unsupervised Feature Learning for Environmental Sound Classification Using Weighted Cycle-Consistent Generative Adversarial Network
Authors: Mohammad Esmaeilpour, Patrick Cardinal, Alessandro Lameiras Koerich
Date: 2019-04-08 00:00:00
Tasks: Benchmarking, Classification, Data Augmentation, Environmental Sound Classification, General Classification, Generative Adversarial Network, Sound Classification

In this paper we propose a novel environmental sound classification approach incorporating unsupervised feature learning from codebook via spherical $K$-Means++ algorithm and a new architecture for high-level data augmentation. The audio signal is transformed into a 2D representation using a discrete wavelet transform (DWT). The DWT spectrograms are then augmented by a novel architecture for cycle-consistent generative adversarial network. This high-level augmentation bootstraps generated spectrograms in both intra and inter class manners by translating structural features from sample to sample. A codebook is built by coding the DWT spectrograms with the speeded-up robust feature detector (SURF) and the K-Means++ algorithm. The Random Forest is our final learning algorithm which learns the environmental sound classification task from the clustered codewords in the codebook. Experimental results in four benchmarking environmental sound datasets (ESC-10, ESC-50, UrbanSound8k, and DCASE-2017) have shown that the proposed classification approach outperforms the state-of-the-art classifiers in the scope, including advanced and dense convolutional neural networks such as AlexNet and GoogLeNet, improving the classification rate between 3.51% and 14.34%, depending on the dataset.
Least-squares registration of point sets over SE (d) using closed-form projections
Authors: Sk. Miraj Ahmed, Niladri Ranjan Das, Kunal Narayan Chaudhury
Date: 2019-04-08 00:00:00
Tasks: Form

Consider the problem of registering multiple point sets in some
$d$-dimensional space using rotations and translations. Assume that there are
sets with common points, and moreover the pairwise correspondences are known
for such sets. We consider a least-squares formulation of this problem, where
the variables are the transforms associated with the point sets. The present
novelty is that we reduce this nonconvex problem to an optimization over the
positive semidefinite cone, where the objective is linear but the constraints
are nevertheless nonconvex. We propose to solve this using variable splitting
and the alternating directions method of multipliers (ADMM). Due to the
linearity of the objective and the structure of constraints, the ADMM
subproblems are given by projections with closed-form solutions. In particular,
for $m$ point sets, the dominant cost per iteration is the partial
eigendecomposition of an $md \times md$ matrix, and $m-1$ singular value
decompositions of $d \times d$ matrices. We empirically show that for
appropriate parameter settings, the proposed solver has a large convergence
basin and is stable under perturbations. As applications, we use our method for
$2$D shape matching and $3$D multiview registration. In either application, we
model the shapes/scans as point sets and determine the pairwise correspondences
using ICP. In particular, our algorithm compares favorably with existing
methods for multiview reconstruction in terms of timing and accuracy.
Desaturating EUV observations of solar flaring storms
Authors: Sabrina Guastavino, Michele Piana, Anna Maria Massone, Richard Schwartz, Federico Benvenuto
Date: 2019-04-08 00:00:00
Tasks: Astronomy

Image saturation has been an issue for several instruments in solar
astronomy, mainly at EUV wavelengths. However, with the launch of the
Atmospheric Imaging Assembly (AIA) as part of the payload of the Solar Dynamic
Observatory (SDO) image saturation has become a big data issue, involving
around 10^$ frames of the impressive dataset this beautiful telescope has been
providing every year since February 2010. This paper introduces a novel
desaturation method, which is able to recover the signal in the saturated
region of any AIA image by exploiting no other information but the one
contained in the image itself. This peculiar methodological property, jointly
with the unprecedented statistical reliability of the desaturated images, could
make this algorithm the perfect tool for the realization of a reconstruction
pipeline for AIA data, able to work properly even in the case of long-lasting,
very energetic flaring events.
Deep-Sentiment: Sentiment Analysis Using Ensemble of CNN and Bi-LSTM Models
Authors: Shervin Minaee, Elham Azimi, Amirali Abdolrashidi
Date: 2019-04-08 00:00:00
Tasks: Sentiment Analysis

With the popularity of social networks, and e-commerce websites, sentiment
analysis has become a more active area of research in the past few years. On a
high level, sentiment analysis tries to understand the public opinion about a
specific product or topic, or trends from reviews or tweets. Sentiment analysis
plays an important role in better understanding customer/user opinion, and also
extracting social/political trends. There has been a lot of previous works for
sentiment analysis, some based on hand-engineering relevant textual features,
and others based on different neural network architectures. In this work, we
present a model based on an ensemble of long-short-term-memory (LSTM), and
convolutional neural network (CNN), one to capture the temporal information of
the data, and the other one to extract the local structure thereof. Through
experimental results, we show that using this ensemble model we can outperform
both individual models. We are also able to achieve a very high accuracy rate
compared to the previous works.