pwc-archive/papers-with-abstracts huggingface dataset:
Walk-Steered Convolution for Graph Classification
Authors: Jiatao Jiang, Chunyan Xu, Zhen Cui, Tong Zhang, Wenming Zheng, Jian Yang
Date: 2018-04-16 00:00:00
Tasks: Classification, Clustering, General Classification, Graph Classification, Video Classification

Graph classification is a fundamental but challenging issue for numerous real-world applications. Despite recent great progress in image/video classification, convolutional neural networks (CNNs) cannot yet cater to graphs well because of graphical non-Euclidean topology. In this work, we propose a walk-steered convolutional (WSC) network to assemble the essential success of standard convolutional neural networks as well as the powerful representation ability of random walk. Instead of deterministic neighbor searching used in previous graphical CNNs, we construct multi-scale walk fields (a.k.a. local receptive fields) with random walk paths to depict subgraph structures and advocate graph scalability. To express the internal variations of a walk field, Gaussian mixture models are introduced to encode principal components of walk paths therein. As an analogy to a standard convolution kernel on image, Gaussian models implicitly coordinate those unordered vertices/nodes and edges in a local receptive field after projecting to the gradient space of Gaussian parameters. We further stack graph coarsening upon Gaussian encoding by using dynamic clustering, such that high-level semantics of graph can be well learned like the conventional pooling on image. The experimental results on several public datasets demonstrate the superiority of our proposed WSC method over many state-of-the-arts for graph classification.
Semantic Visual Localization
Authors: Johannes L. Schönberger, Marc Pollefeys, Andreas Geiger, Torsten Sattler
Date: 2017-12-15 00:00:00
Proceeding: CVPR 2018 6
Tasks: Visual Localization

Robust visual localization under a wide range of viewing conditions is a
fundamental problem in computer vision. Handling the difficult cases of this
problem is not only very challenging but also of high practical relevance,
e.g., in the context of life-long localization for augmented reality or
autonomous robots. In this paper, we propose a novel approach based on a joint
3D geometric and semantic understanding of the world, enabling it to succeed
under conditions where previous approaches failed. Our method leverages a novel
generative model for descriptor learning, trained on semantic scene completion
as an auxiliary task. The resulting 3D descriptors are robust to missing
observations by encoding high-level 3D geometric and semantic information.
Experiments on several challenging large-scale localization datasets
demonstrate reliable localization under extreme viewpoint, illumination, and
geometry changes.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Authors: Antti Tarvainen, Harri Valpola
Date: 2017-03-06 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Semi-Supervised Image Classification, Semi-Supervised RGBD Semantic Segmentation, Semi-Supervised Semantic Segmentation, Source Free Object Detection

The recently proposed Temporal Ensembling has achieved state-of-the-art
results in several semi-supervised learning benchmarks. It maintains an
exponential moving average of label predictions on each training example, and
penalizes predictions that are inconsistent with this target. However, because
the targets change only once per epoch, Temporal Ensembling becomes unwieldy
when learning large datasets. To overcome this problem, we propose Mean
Teacher, a method that averages model weights instead of label predictions. As
an additional benefit, Mean Teacher improves test accuracy and enables training
with fewer labels than Temporal Ensembling. Without changing the network
architecture, Mean Teacher achieves an error rate of 4.35% on SVHN with 250
labels, outperforming Temporal Ensembling trained with 1000 labels. We also
show that a good network architecture is crucial to performance. Combining Mean
Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with
4000 labels from 10.55% to 6.28%, and on ImageNet 2012 with 10% of the labels
from 35.24% to 9.11%.
SPSA-FSR: Simultaneous Perturbation Stochastic Approximation for Feature Selection and Ranking
Authors: Zeren D. Yenice, Niranjan Adhikari, Yong Kai Wong, Vural Aksakalli, Alev Taskin Gumus, Babak Abbasi
Date: 2018-04-16 00:00:00
Tasks: feature selection, General Classification, regression

This manuscript presents the following: (1) an improved version of the Binary
Simultaneous Perturbation Stochastic Approximation (SPSA) Method for feature
selection in machine learning (Aksakalli and Malekipirbazari, Pattern
Recognition Letters, Vol. 75, 2016) based on non-monotone iteration gains
computed via the Barzilai and Borwein (BB) method, (2) its adaptation for
feature ranking, and (3) comparison against popular methods on public benchmark
datasets. The improved method, which we call SPSA-FSR, dramatically reduces the
number of iterations required for convergence without impacting solution
quality. SPSA-FSR can be used for feature ranking and feature selection both
for classification and regression problems. After a review of the current
state-of-the-art, we discuss our improvements in detail and present three sets
of computational experiments: (1) comparison of SPSA-FS as a (wrapper) feature
selection method against sequential methods as well as genetic algorithms, (2)
comparison of SPSA-FS as a feature ranking method in a classification setting
against random forest importance, chi-squared, and information main methods,
and (3) comparison of SPSA-FS as a feature ranking method in a regression
setting against minimum redundancy maximum relevance (MRMR), RELIEF, and linear
correlation methods. The number of features in the datasets we use range from a
few dozens to a few thousands. Our results indicate that SPSA-FS converges to a
good feature set in no more than 100 iterations and therefore it is quite fast
for a wrapper method. SPSA-FS also outperforms popular feature selection as
well as feature ranking methods in majority of test cases, sometimes by a large
margin, and it stands as a promising new feature selection and ranking method.
Video Based Reconstruction of 3D People Models
Authors: Thiemo Alldieck, Marcus Magnor, Weipeng Xu, Christian Theobalt, Gerard Pons-Moll
Date: 2018-03-13 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Reconstruction, Surface Reconstruction, Virtual Try-on

This paper describes how to obtain accurate 3D body models and texture of
arbitrary people from a single, monocular video in which a person is moving.
Based on a parametric body model, we present a robust processing pipeline
achieving 3D model fits with 5mm accuracy also for clothed people. Our main
contribution is a method to nonrigidly deform the silhouette cones
corresponding to the dynamic human silhouettes, resulting in a visual hull in a
common reference frame that enables surface reconstruction. This enables
efficient estimation of a consensus 3D shape, texture and implanted animation
skeleton based on a large number of frames. We present evaluation results for a
number of test subjects and analyze overall performance. Requiring only a
smartphone or webcam, our method enables everyone to create their own fully
animatable digital double, e.g., for social VR applications or virtual try-on
for online fashion shopping.
Long-term Correlation Tracking using Multi-layer Hybrid Features in Sparse and Dense Environments
Authors: Nathanael L. Baisa, Deepayan Bhowmik, Andrew Wallace
Date: 2017-05-31 00:00:00
Tasks: Visual Tracking

Tracking a target of interest in both sparse and crowded environments is a
challenging problem, not yet successfully addressed in the literature. In this
paper, we propose a new long-term visual tracking algorithm, learning
discriminative correlation filters and using an online classifier, to track a
target of interest in both sparse and crowded video sequences. First, we learn
a translation correlation filter using a multi-layer hybrid of convolutional
neural networks (CNN) and traditional hand-crafted features. We combine
advantages of both the lower convolutional layer which retains more spatial
details for precise localization and the higher convolutional layer which
encodes semantic information for handling appearance variations, and then
integrate these with histogram of oriented gradients (HOG) and color-naming
traditional features. Second, we include a re-detection module for overcoming
tracking failures due to long-term occlusions by training an incremental
(online) SVM on the most confident frames using hand-engineered features. This
re-detection module is activated only when the correlation response of the
object is below some pre-defined threshold. This generates high score detection
proposals which are temporally filtered using a Gaussian mixture probability
hypothesis density (GM-PHD) filter to find the detection proposal with the
maximum weight as the target state estimate by removing the other detection
proposals as clutter. Finally, we learn a scale correlation filter for
estimating the scale of a target by constructing a target pyramid around the
estimated or re-detected position using the HOG features. We carry out
extensive experiments on both sparse and dense data sets which show that our
method significantly outperforms state-of-the-art methods.
Constant Step Size Stochastic Gradient Descent for Probabilistic Modeling
Authors: Dmitry Babichev, Francis Bach
Date: 2018-04-16 00:00:00

Stochastic gradient methods enable learning probabilistic models from large
amounts of data. While large step-sizes (learning rates) have shown to be best
for least-squares (e.g., Gaussian noise) once combined with parameter
averaging, these are not leading to convergent algorithms in general. In this
paper, we consider generalized linear models, that is, conditional models based
on exponential families. We propose averaging moment parameters instead of
natural parameters for constant-step-size stochastic gradient descent. For
finite-dimensional models, we show that this can sometimes (and surprisingly)
lead to better predictions than the best linear model. For infinite-dimensional
models, we show that it always converges to optimal predictions, while
averaging natural parameters never does. We illustrate our findings with
simulations on synthetic data and classical benchmarks with many observations.
Detecting Egregious Conversations between Customers and Virtual Agents
Authors: Tommy Sandbank, Michal Shmueli-Scheuer, Jonathan Herzig, David Konopnicki, John Richards, David Piorkowski
Date: 2017-11-15 00:00:00
Proceeding: NAACL 2018 6

Virtual agents are becoming a prominent channel of interaction in customer
service. Not all customer interactions are smooth, however, and some can become
almost comically bad. In such instances, a human agent might need to step in
and salvage the conversation. Detecting bad conversations is important since
disappointing customer service may threaten customer loyalty and impact
revenue. In this paper, we outline an approach to detecting such egregious
conversations, using behavioral cues from the user, patterns in agent
responses, and user-agent interaction. Using logs of two commercial systems, we
show that using these features improves the detection F1-score by around 20%
over using textual features alone. In addition, we show that those features are
common across two quite different domains and, arguably, universal.
Deep Bayesian Trust : A Dominant and Fair Incentive Mechanism for Crowd
Authors: Naman Goel, Boi Faltings
Date: 2018-04-16 00:00:00
Tasks: Fairness

An important class of game-theoretic incentive mechanisms for eliciting
effort from a crowd are the peer based mechanisms, in which workers are paid by
matching their answers with one another. The other classic mechanism is to have
the workers solve some gold standard tasks and pay them according to their
accuracy on gold tasks. This mechanism ensures stronger incentive compatibility
than the peer based mechanisms but assigning gold tasks to all workers becomes
inefficient at large scale. We propose a novel mechanism that assigns gold
tasks to only a few workers and exploits transitivity to derive accuracy of the
rest of the workers from their peers' accuracy. We show that the resulting
mechanism ensures a dominant notion of incentive compatibility and fairness.
BinarEye: An Always-On Energy-Accuracy-Scalable Binary CNN Processor With All Memory On Chip in 28nm CMOS
Authors: Bert Moons, Daniel Bankman, Lita Yang, Boris Murmann, Marian Verhelst
Date: 2018-04-16 00:00:00
Tasks: All, Face Detection

This paper introduces BinarEye: a digital processor for always-on Binary
Convolutional Neural Networks. The chip maximizes data reuse through a Neuron
Array exploiting local weight Flip-Flops. It stores full network models and
feature maps and hence requires no off-chip bandwidth, which leads to a 230
1b-TOPS/W peak efficiency. Its 3 levels of flexibility - (a) weight
reconfiguration, (b) a programmable network depth and (c) a programmable
network width - allow trading energy for accuracy depending on the task's
requirements. BinarEye's full system input-to-label energy consumption ranges
from 14.4uJ/f for 86% CIFAR-10 and 98% owner recognition down to 0.92uJ/f for
94% face detection at up to 1700 frames per second. This is 3-12-70x more
efficient than the state-of-the-art at on-par accuracy.
Unsupervised Keyphrase Extraction with Multipartite Graphs
Authors: Florian Boudin
Date: 2018-03-23 00:00:00
Proceeding: NAACL 2018 6
Tasks: Keyphrase Extraction

We propose an unsupervised keyphrase extraction model that encodes topical
information within a multipartite graph structure. Our model represents
keyphrase candidates and topics in a single graph and exploits their mutually
reinforcing relationship to improve candidate ranking. We further introduce a
novel mechanism to incorporate keyphrase selection preferences into the model.
Experiments conducted on three widely used datasets show significant
improvements over state-of-the-art graph-based models.
Building robust prediction models for defective sensor data using Artificial Neural Networks
Authors: Arvind Kumar Shekar, Cláudio Rebelo de Sá, Hugo Ferreira, Carlos Soares
Date: 2018-04-16 00:00:00
Tasks: Data Augmentation, feature selection

Predicting the health of components in complex dynamic systems such as an
automobile poses numerous challenges. The primary aim of such predictive
systems is to use the high-dimensional data acquired from different sensors and
predict the state-of-health of a particular component, e.g., brake pad. The
classical approach involves selecting a smaller set of relevant sensor signals
using feature selection and using them to train a machine learning algorithm.
However, this fails to address two prominent problems: (1) sensors are
susceptible to failure when exposed to extreme conditions over a long periods
of time; (2) sensors are electrical devices that can be affected by noise or
electrical interference. Using the failed and noisy sensor signals as inputs
largely reduce the prediction accuracy. To tackle this problem, it is
advantageous to use the information from all sensor signals, so that the
failure of one sensor can be compensated by another. In this work, we propose
an Artificial Neural Network (ANN) based framework to exploit the information
from a large number of signals. Secondly, our framework introduces a data
augmentation approach to perform accurate predictions in spite of noisy
signals. The plausibility of our framework is validated on real life industrial
application from Robert Bosch GmbH.
Skepxels: Spatio-temporal Image Representation of Human Skeleton Joints for Action Recognition
Authors: Jian Liu, Naveed Akhtar, Ajmal Mian
Date: 2017-11-16 00:00:00
Tasks: Action Analysis, Action Recognition, Temporal Action Localization

Human skeleton joints are popular for action analysis since they can be
easily extracted from videos to discard background noises. However, current
skeleton representations do not fully benefit from machine learning with CNNs.
We propose "Skepxels" a spatio-temporal representation for skeleton sequences
to fully exploit the "local" correlations between joints using the 2D
convolution kernels of CNN. We transform skeleton videos into images of
flexible dimensions using Skepxels and develop a CNN-based framework for
effective human action recognition using the resulting images. Skepxels encode
rich spatio-temporal information about the skeleton joints in the frames by
maximizing a unique distance metric, defined collaboratively over the distinct
joint arrangements used in the skeletal image. Moreover, they are flexible in
encoding compound semantic notions such as location and speed of the joints.
The proposed action recognition exploits the representation in a hierarchical
manner by first capturing the micro-temporal relations between the skeleton
joints with the Skepxels and then exploiting their macro-temporal relations by
computing the Fourier Temporal Pyramids over the CNN features of the skeletal
images. We extend the Inception-ResNet CNN architecture with the proposed
method and improve the state-of-the-art accuracy by 4.4% on the large scale NTU
human activity dataset. On the medium-sized N-UCLA and UTH-MHAD datasets, our
method outperforms the existing results by 5.7% and 9.3% respectively.
Learning Simple Thresholded Features with Sparse Support Recovery
Authors: Hongyu Xu, Zhangyang Wang, Haichuan Yang, Ding Liu, Ji Liu
Date: 2018-04-16 00:00:00
Tasks: Dictionary Learning

The thresholded feature has recently emerged as an extremely efficient, yet
rough empirical approximation, of the time-consuming sparse coding inference
process. Such an approximation has not yet been rigorously examined, and
standard dictionaries often lead to non-optimal performance when used for
computing thresholded features. In this paper, we first present two theoretical
recovery guarantees for the thresholded feature to exactly recover the nonzero
support of the sparse code. Motivated by them, we then formulate the Dictionary
Learning for Thresholded Features (DLTF) model, which learns an optimized
dictionary for applying the thresholded feature. In particular, for the $(k,
2)$ norm involved, a novel proximal operator with log-linear time complexity
$O(m\log m)$ is derived. We evaluate the performance of DLTF on a vast range of
synthetic and real-data tasks, where DLTF demonstrates remarkable efficiency,
effectiveness and robustness in all experiments. In addition, we briefly
discuss the potential link between DLTF and deep learning building blocks.
Adversarial Ranking for Language Generation
Authors: Kevin Lin, Dianqi Li, Xiaodong He, Zhengyou Zhang, Ming-Ting Sun
Date: 2017-05-31 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Generative Adversarial Network, Text Generation

Generative adversarial networks (GANs) have great successes on synthesizing
data. However, the existing GANs restrict the discriminator to be a binary
classifier, and thus limit their learning capacity for tasks that need to
synthesize output with rich structures such as natural language descriptions.
In this paper, we propose a novel generative adversarial network, RankGAN, for
generating high-quality language descriptions. Rather than training the
discriminator to learn and assign absolute binary predicate for individual data
sample, the proposed RankGAN is able to analyze and rank a collection of
human-written and machine-written sentences by giving a reference group. By
viewing a set of data samples collectively and evaluating their quality through
relative ranking scores, the discriminator is able to make better assessment
which in turn helps to learn a better generator. The proposed RankGAN is
optimized through the policy gradient technique. Experimental results on
multiple public datasets clearly demonstrate the effectiveness of the proposed
approach.
STAIR Actions: A Video Dataset of Everyday Home Actions
Authors: Yuya Yoshikawa, Jiaqing Lin, Akikazu Takeuchi
Date: 2018-04-12 00:00:00
Tasks: Action Recognition, Temporal Action Localization

A new large-scale video dataset for human action recognition, called STAIR
Actions is introduced. STAIR Actions contains 100 categories of action labels
representing fine-grained everyday home actions so that it can be applied to
research in various home tasks such as nursing, caring, and security. In STAIR
Actions, each video has a single action label. Moreover, for each action
category, there are around 1,000 videos that were obtained from YouTube or
produced by crowdsource workers. The duration of each video is mostly five to
six seconds. The total number of videos is 102,462. We explain how we
constructed STAIR Actions and show the characteristics of STAIR Actions
compared to existing datasets for human action recognition. Experiments with
three major models for action recognition show that STAIR Actions can train
large models and achieve good performance. STAIR Actions can be downloaded from
http://actions.stair.center
Learning Warped Guidance for Blind Face Restoration
Authors: Xiaoming Li, Ming Liu, Yuting Ye, WangMeng Zuo, Liang Lin, Ruigang Yang
Date: 2018-04-13 00:00:00
Proceeding: ECCV 2018 9
Tasks: Blind Face Restoration

This paper studies the problem of blind face restoration from an
unconstrained blurry, noisy, low-resolution, or compressed image (i.e.,
degraded observation). For better recovery of fine facial details, we modify
the problem setting by taking both the degraded observation and a high-quality
guided image of the same identity as input to our guided face restoration
network (GFRNet). However, the degraded observation and guided image generally
are different in pose, illumination and expression, thereby making plain CNNs
(e.g., U-Net) fail to recover fine and identity-aware facial details. To tackle
this issue, our GFRNet model includes both a warping subnetwork (WarpNet) and a
reconstruction subnetwork (RecNet). The WarpNet is introduced to predict flow
field for warping the guided image to correct pose and expression (i.e., warped
guidance), while the RecNet takes the degraded observation and warped guidance
as input to produce the restoration result. Due to that the ground-truth flow
field is unavailable, landmark loss together with total variation
regularization are incorporated to guide the learning of WarpNet. Furthermore,
to make the model applicable to blind restoration, our GFRNet is trained on the
synthetic data with versatile settings on blur kernel, noise level,
downsampling scale factor, and JPEG quality factor. Experiments show that our
GFRNet not only performs favorably against the state-of-the-art image and face
restoration methods, but also generates visually photo-realistic results on
real degraded facial images.