pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual Effects
Authors: Ding-Jie Chen, Jui-Ting Chien, Hwann-Tzong Chen, Tyng-Luh Liu
Date: 2018-12-20 00:00:00
Tasks: Image Generation, Image Segmentation, Meta-Learning, Segmentation, Semantic Segmentation, Style Transfer

This paper presents a "learning to learn" approach to figure-ground image
segmentation. By exploring webly-abundant images of specific visual effects,
our method can effectively learn the visual-effect internal representations in
an unsupervised manner and uses this knowledge to differentiate the figure from
the ground in an image. Specifically, we formulate the meta-learning process as
a compositional image editing task that learns to imitate a certain visual
effect and derive the corresponding internal representation. Such a generative
process can help instantiate the underlying figure-ground notion and enables
the system to accomplish the intended image segmentation. Whereas existing
generative methods are mostly tailored to image synthesis or style transfer,
our approach offers a flexible learning mechanism to model a general concept of
figure-ground segmentation from unorganized images that have no explicit
pixel-level annotations. We validate our approach via extensive experiments on
six datasets to demonstrate that the proposed model can be end-to-end trained
without ground-truth pixel labeling yet outperforms the existing methods of
unsupervised segmentation tasks.
Graph Neural Networks: A Review of Methods and Applications
Authors: Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, LiFeng Wang, Changcheng Li, Maosong Sun
Date: 2018-12-20 00:00:00
Tasks: Graph Attention, Graph Neural Network

Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks. In this survey, we propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research.
A Survey of Hierarchy Identification in Social Networks
Authors: Denys Katerenchuk
Date: 2018-12-20 00:00:00
Tasks: Survey

Humans are social by nature. Throughout history, people have formed
communities and built relationships. Most relationships with coworkers,
friends, and family are developed during face-to-face interactions. These
relationships are established through explicit means of communications such as
words and implicit such as intonation, body language, etc. By analyzing human
interactions we can derive information about the relationships and influence
among conversation participants. However, with the development of the Internet,
people started to communicate through text in online social networks.
Interestingly, they brought their communicational habits to the Internet. Many
social network users form relationships with each other and establish
communities with leaders and followers. Recognizing these hierarchical
relationships is an important task because it will help to understand social
networks and predict future trends, improve recommendations, better target
advertisement, and improve national security by identifying leaders of
anonymous terror groups. In this work, I provide an overview of current
research in this area and present the state-of-the-art approaches to deal with
the problem of identifying hierarchical relationships in social networks.
Context, Attention and Audio Feature Explorations for Audio Visual Scene-Aware Dialog
Authors: Shachi H. Kumar, Eda Okur, Saurav Sahay, Juan Jose Alvarado Leanos, Jonathan Huang, Lama Nachman
Date: 2018-12-20 00:00:00
Tasks: Audio Classification, General Classification

With the recent advancements in AI, Intelligent Virtual Assistants (IVA) have
become a ubiquitous part of every home. Going forward, we are witnessing a
confluence of vision, speech and dialog system technologies that are enabling
the IVAs to learn audio-visual groundings of utterances and have conversations
with users about the objects, activities and events surrounding them. As a part
of the 7th Dialog System Technology Challenges (DSTC7), for Audio Visual
Scene-Aware Dialog (AVSD) track, We explore `topics' of the dialog as an
important contextual feature into the architecture along with explorations
around multimodal Attention. We also incorporate an end-to-end audio
classification ConvNet, AclNet, into our models. We present detailed analysis
of the experiments and show that some of our model variations outperform the
baseline system presented for this task.
SFA: Small Faces Attention Face Detector
Authors: Shi Luo, Xiongfei Li, Rui Zhu, Xiaoli Zhang
Date: 2018-12-20 00:00:00
Tasks: Face Detection, GPU

In recent year, tremendous strides have been made in face detection thanks to
deep learning. However, most published face detectors deteriorate dramatically
as the faces become smaller. In this paper, we present the Small Faces
Attention (SFA) face detector to better detect faces with small scale. First,
we propose a new scale-invariant face detection architecture which pays more
attention to small faces, including 4-branch detection architecture and small
faces sensitive anchor design. Second, feature maps fusion strategy is applied
in SFA by partially combining high-level features into low-level features to
further improve the ability of finding hard faces. Third, we use multi-scale
training and testing strategy to enhance face detection performance in
practice. Comprehensive experiments show that SFA significantly improves face
detection performance, especially on small faces. Our real-time SFA face
detector can run at 5 FPS on a single GPU as well as maintain high performance.
Besides, our final SFA face detector achieves state-of-the-art detection
performance on challenging face detection benchmarks, including WIDER FACE and
FDDB datasets, with competitive runtime speed. Both our code and models will be
available to the research community.
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames
Authors: Geneviève Robin, Hoi-To Wai, Julie Josse, Olga Klopp, Éric Moulines
Date: 2018-12-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Clustering, Imputation, Missing Values

Many applications of machine learning involve the analysis of large data
frames-matrices collecting heterogeneous measurements (binary, numerical,
counts, etc.) across samples-with missing values. Low-rank models, as studied
by Udell et al. [30], are popular in this framework for tasks such as
visualization, clustering and missing value imputation. Yet, available methods
with statistical guarantees and efficient optimization do not allow explicit
modeling of main additive effects such as row and column, or covariate effects.
In this paper, we introduce a low-rank interaction and sparse additive effects
(LORIS) model which combines matrix regression on a dictionary and low-rank
design, to estimate main effects and interactions simultaneously. We provide
statistical guarantees in the form of upper bounds on the estimation error of
both components. Then, we introduce a mixed coordinate gradient descent (MCGD)
method which provably converges sub-linearly to an optimal solution and is
computationally efficient for large scale data sets. We show on simulated and
survey data that the method has a clear advantage over current practices, which
consist in dealing separately with additive effects in a preprocessing step.
Kappa Learning: A New Method for Measuring Similarity Between Educational Items Using Performance Data
Authors: Tanya Nazaretsky, Sara Hershkovitz, Giora Alexandron
Date: 2018-12-20 00:00:00
Tasks: Clustering, Math

Sequencing items in adaptive learning systems typically relies on a large
pool of interactive assessment items (questions) that are analyzed into a
hierarchy of skills or Knowledge Components (KCs). Educational data mining
techniques can be used to analyze students performance data in order to
optimize the mapping of items to KCs. Standard methods that map items into KCs
using item-similarity measures make the implicit assumption that students
performance on items that depend on the same skill should be similar. This
assumption holds if the latent trait (mastery of the underlying skill) is
relatively fixed during students activity, as in the context of testing, which
is the primary context in which these measures were developed and applied.
However, in adaptive learning systems that aim for learning, and address
subject matters such as K6 Math that consist of multiple sub-skills, this
assumption does not hold. In this paper we propose a new item-similarity
measure, termed Kappa Learning (KL), which aims to address this gap. KL
identifies similarity between items under the assumption of learning, namely,
that learners mastery of the underlying skills changes as they progress through
the items. We evaluate Kappa Learning on data from a computerized tutor that
teaches Fractions for 4th grade, with experts tagging as ground truth, and on
simulated data. Our results show that clustering that is based on Kappa
Learning outperforms clustering that is based on commonly used similarity
measures (Cohen Kappa, Yule, and Pearson).
Feedforward Neural Network for Time Series Anomaly Detection
Authors: Zhang Rong, Dong Shandong, Nie Xin, Xiao Shiguang
Date: 2018-12-20 00:00:00
Tasks: Anomaly Detection, CPU, Feature Engineering, Time Series, Time Series Analysis, Time Series Anomaly Detection

Time series anomaly detection is usually formulated as finding outlier data
points relative to some usual data, which is also an important problem in
industry and academia. To ensure systems working stably, internet companies,
banks and other companies need to monitor time series, which is called KPI (Key
Performance Indicators), such as CPU used, number of orders, number of online
users and so on. However, millions of time series have several shapes (e.g.
seasonal KPIs, KPIs of timed tasks and KPIs of CPU used), so that it is very
difficult to use a simple statistical model to detect anomaly for all kinds of
time series. Although some anomaly detectors have developed many years and some
supervised models are also available in this field, we find many methods have
their own disadvantages. In this paper, we present our system, which is based
on deep feedforward neural network and detect anomaly points of time series.
The main difference between our system and other systems based on supervised
models is that we do not need feature engineering of time series to train deep
feedforward neural network in our system, which is essentially an end-to-end
system.
DAC: Data-free Automatic Acceleration of Convolutional Networks
Authors: Xin Li, Shuai Zhang, Bolan Jiang, Yingyong Qi, Mooi Choo Chuah, Ning Bi
Date: 2018-12-20 00:00:00
Tasks: image-classification, Image Classification, Multi-Person Pose Estimation, object-detection, Object Detection, Pose Estimation

Deploying a deep learning model on mobile/IoT devices is a challenging task.
The difficulty lies in the trade-off between computation speed and accuracy. A
complex deep learning model with high accuracy runs slowly on resource-limited
devices, while a light-weight model that runs much faster loses accuracy. In
this paper, we propose a novel decomposition method, namely DAC, that is
capable of factorizing an ordinary convolutional layer into two layers with
much fewer parameters. DAC computes the corresponding weights for the newly
generated layers directly from the weights of the original convolutional layer.
Thus, no training (or fine-tuning) or any data is needed. The experimental
results show that DAC reduces a large number of floating-point operations
(FLOPs) while maintaining high accuracy of a pre-trained model. If 2% accuracy
drop is acceptable, DAC saves 53% FLOPs of VGG16 image classification model on
ImageNet dataset, 29% FLOPS of SSD300 object detection model on PASCAL VOC2007
dataset, and 46% FLOPS of a multi-person pose estimation model on Microsoft
COCO dataset. Compared to other existing decomposition methods, DAC achieves
better performance.
SfMLearner++: Learning Monocular Depth & Ego-Motion using Meaningful Geometric Constraints
Authors: Vignesh Prasad, Brojeshwar Bhowmick
Date: 2018-12-20 00:00:00
Tasks: Monocular Visual Odometry, Visual Odometry

Most geometric approaches to monocular Visual Odometry (VO) provide robust
pose estimates, but sparse or semi-dense depth estimates. Off late, deep
methods have shown good performance in generating dense depths and VO from
monocular images by optimizing the photometric consistency between images.
Despite being intuitive, a naive photometric loss does not ensure proper pixel
correspondences between two views, which is the key factor for accurate depth
and relative pose estimations. It is a well known fact that simply minimizing
such an error is prone to failures.
  We propose a method using Epipolar constraints to make the learning more
geometrically sound. We use the Essential matrix, obtained using Nister's Five
Point Algorithm, for enforcing meaningful geometric constraints on the loss,
rather than using it as labels for training. Our method, although simplistic
but more geometrically meaningful, using lesser number of parameters, gives a
comparable performance to state-of-the-art methods which use complex losses and
large networks showing the effectiveness of using epipolar constraints. Such a
geometrically constrained learning method performs successfully even in cases
where simply minimizing the photometric error would fail.
Joint Mapping and Calibration via Differentiable Sensor Fusion
Authors: Jonathan P. Chen, Fritz Obermeyer, Vladimir Lyapunov, Lionel Gueguen, Noah D. Goodman
Date: 2018-11-21 00:00:00
Tasks: Clustering, Probabilistic Programming, Sensor Fusion, Variational Inference

We leverage automatic differentiation (AD) and probabilistic programming to
develop an end-to-end optimization algorithm for batch triangulation of a large
number of unknown objects. Given noisy detections extracted from noisily
geo-located street level imagery without depth information, we jointly estimate
the number and location of objects of different types, together with parameters
for sensor noise characteristics and prior distribution of objects conditioned
on side information. The entire algorithm is framed as nested stochastic
variational inference. An inner loop solves a soft data association problem via
loopy belief propagation; a middle loop performs soft EM clustering using a
regularized Newton solver (leveraging an AD framework); an outer loop
backpropagates through the inner loops to train global parameters. We place
priors over sensor parameters for different traffic object types, and
demonstrate improvements with richer priors incorporating knowledge of the
environment.
  We test our algorithm on detections of road signs observed by cars with
mounted cameras, though in practice this technique can be used for any
geo-tagged images. The detections were extracted by neural image detectors and
classifiers, and we independently triangulate each type of sign (e.g. stop,
traffic light). We find that our model is more robust to DNN misclassifications
than current methods, generalizes across sign types, and can use geometric
information to increase precision. Our algorithm outperforms our current
production baseline based on k-means clustering. We show that variational
inference training allows generalization by learning sign-specific parameters.
Sequential Attention GAN for Interactive Image Editing
Authors: Yu Cheng, Zhe Gan, Yitong Li, Jingjing Liu, Jianfeng Gao
Date: 2018-12-20 00:00:00
Tasks: Image Description, Image Generation, Text-to-Image Generation

Most existing text-to-image synthesis tasks are static single-turn generation, based on pre-defined textual descriptions of images. To explore more practical and interactive real-life applications, we introduce a new task - Interactive Image Editing, where users can guide an agent to edit images via multi-turn textual commands on-the-fly. In each session, the agent takes a natural language description from the user as the input and modifies the image generated in the previous turn to a new design, following the user description. The main challenges in this sequential and interactive image generation task are two-fold: 1) contextual consistency between a generated image and the provided textual description; 2) step-by-step region-level modification to maintain visual consistency across the generated image sequence in each session. To address these challenges, we propose a novel Sequential Attention Generative Adversarial Net-work (SeqAttnGAN), which applies a neural state tracker to encode the previous image and the textual description in each turn of the sequence, and uses a GAN framework to generate a modified version of the image that is consistent with the preceding images and coherent with the description. To achieve better region-specific refinement, we also introduce a sequential attention mechanism into the model. To benchmark on the new task, we introduce two new datasets, Zap-Seq and DeepFashion-Seq, which contain multi-turn sessions with image-description sequences in the fashion domain. Experiments on both datasets show that the proposed SeqAttnGANmodel outperforms state-of-the-art approaches on the interactive image editing task across all evaluation metrics including visual quality, image sequence coherence, and text-image consistency.
Robustness Meets Deep Learning: An End-to-End Hybrid Pipeline for Unsupervised Learning of Egomotion
Authors: Alex Zihao Zhu, Wenxin Liu, ZiYun Wang, Vijay Kumar, Kostas Daniilidis
Date: 2018-12-20 00:00:00
Tasks: Optical Flow Estimation

In this work, we propose a method that combines unsupervised deep learning
predictions for optical flow and monocular disparity with a model based
optimization procedure for instantaneous camera pose. Given the flow and
disparity predictions from the network, we apply a RANSAC outlier rejection
scheme to find an inlier set of flows and disparities, which we use to solve
for the relative camera pose in a least squares fashion. We show that this
pipeline is fully differentiable, allowing us to combine the pose with the
network outputs as an additional unsupervised training loss to further refine
the predicted flows and disparities. This method not only allows us to directly
regress relative pose from the network outputs, but also automatically segments
away pixels that do not fit the rigid scene assumptions that many unsupervised
structure from motion methods apply, such as on independently moving objects.
We evaluate our method on the KITTI dataset, and demonstrate state of the art
results, even in the presence of challenging independently moving objects.
Rain Removal By Image Quasi-Sparsity Priors
Authors: Yinglong Wang, Shuaicheng Liu, Chen Chen, Dehua Xie, Bing Zeng
Date: 2018-12-20 00:00:00
Tasks: Rain Removal

Rain streaks will inevitably be captured by some outdoor vision systems,
which lowers the image visual quality and also interferes various computer
vision applications. We present a novel rain removal method in this paper,
which consists of two steps, i.e., detection of rain streaks and reconstruction
of the rain-removed image. An accurate detection of rain streaks determines the
quality of the overall performance. To this end, we first detect rain streaks
according to pixel intensities, motivated by the observation that rain streaks
often possess higher intensities compared to other neighboring image
structures. Some mis-detected locations are then refined through a
morphological processing and the principal component analysis (PCA) such that
only locations corresponding to real rain streaks are retained. In the second
step, we separate image gradients into a background layer and a rain streak
layer, thanks to the image quasi-sparsity prior, so that a rain image can be
decomposed into a background layer and a rain layer. We validate the
effectiveness of our method through quantitative and qualitative evaluations.
We show that our method can remove rain (even for some relatively bright rain)
from images robustly and outperforms some state-of-the-art rain removal
algorithms.
Generating lyrics with variational autoencoder and multi-modal artist embeddings
Authors: Olga Vechtomova, Hareesh Bahuleyan, Amirpasha Ghabussi, Vineet John
Date: 2018-12-20 00:00:00

We present a system for generating song lyrics lines conditioned on the style
of a specified artist. The system uses a variational autoencoder with artist
embeddings. We propose the pre-training of artist embeddings with the
representations learned by a CNN classifier, which is trained to predict
artists based on MEL spectrograms of their song clips. This work is the first
step towards combining audio and text modalities of songs for generating lyrics
conditioned on the artist's style. Our preliminary results suggest that there
is a benefit in initializing artists' embeddings with the representations
learned by a spectrogram classifier.
Iterated Belief Revision Under Resource Constraints: Logic as Geometry
Authors: Dan P. Guralnik, Daniel E. Koditschek
Date: 2018-12-20 00:00:00

We propose a variant of iterated belief revision designed for settings with
limited computational resources, such as mobile autonomous robots. The proposed
memory architecture---called the {\em universal memory architecture}
(UMA)---maintains an epistemic state in the form of a system of default rules
similar to those studied by Pearl and by Goldszmidt and Pearl (systems $Z$ and
$Z^+$). A duality between the category of UMA representations and the category
of the corresponding model spaces, extending the Sageev-Roller duality between
discrete poc sets and discrete median algebras provides a two-way dictionary
from inference to geometry, leading to immense savings in computation, at a
cost in the quality of representation that can be quantified in terms of
topological invariants. Moreover, the same framework naturally enables
comparisons between different model spaces, making it possible to analyze the
deficiencies of one model space in comparison to others. This paper develops
the formalism underlying UMA, analyzes the complexity of maintenance and
inference operations in UMA, and presents some learning guarantees for
different UMA-based learners. Finally, we present simulation results to
illustrate the viability of the approach, and close with a discussion of the
strengths, weaknesses, and potential development of UMA-based learners.
NeuralWarp: Time-Series Similarity with Warping Networks
Authors: Josif Grabocka, Lars Schmidt-Thieme
Date: 2018-12-20 00:00:00
Tasks: Sentence, Sentence Similarity, Time Series, Time Series Analysis

Research on time-series similarity measures has emphasized the need for
elastic methods which align the indices of pairs of time series and a plethora
of non-parametric have been proposed for the task. On the other hand, deep
learning approaches are dominant in closely related domains, such as learning
image and text sentence similarity. In this paper, we propose
\textit{NeuralWarp}, a novel measure that models the alignment of time-series
indices in a deep representation space, by modeling a warping function as an
upper level neural network between deeply-encoded time series values.
Experimental results demonstrate that \textit{NeuralWarp} outperforms both
non-parametric and un-warped deep models on a range of diverse real-life
datasets.