pwc-archive/papers-with-abstracts huggingface dataset:
Spatial Fusion GAN for Image Synthesis
Authors: Fangneng Zhan, Hongyuan Zhu, Shijian Lu
Date: 2018-12-14 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Generation

Recent advances in generative adversarial networks (GANs) have shown great
potentials in realistic image synthesis whereas most existing works address
synthesis realism in either appearance space or geometry space but few in both.
This paper presents an innovative Spatial Fusion GAN (SF-GAN) that combines a
geometry synthesizer and an appearance synthesizer to achieve synthesis realism
in both geometry and appearance spaces. The geometry synthesizer learns
contextual geometries of background images and transforms and places foreground
objects into the background images unanimously. The appearance synthesizer
adjusts the color, brightness and styles of the foreground objects and embeds
them into background images harmoniously, where a guided filter is introduced
for detail preserving. The two synthesizers are inter-connected as mutual
references which can be trained end-to-end without supervision. The SF-GAN has
been evaluated in two tasks: (1) realistic scene text image synthesis for
training better recognition models; (2) glass and hat wearing for realistic
matching glasses and hats with real portraits. Qualitative and quantitative
comparisons with the state-of-the-art demonstrate the superiority of the
proposed SF-GAN.
Rethinking Layer-wise Feature Amounts in Convolutional Neural Network Architectures
Authors: Martin Mundt, Sagnik Majumder, Tobias Weis, Visvanathan Ramesh
Date: 2018-12-14 00:00:00
Tasks: General Classification, image-classification, Image Classification

We characterize convolutional neural networks with respect to the relative
amount of features per layer. Using a skew normal distribution as a
parametrized framework, we investigate the common assumption of monotonously
increasing feature-counts with higher layers of architecture designs. Our
evaluation on models with VGG-type layers on the MNIST, Fashion-MNIST and
CIFAR-10 image classification benchmarks provides evidence that motivates
rethinking of our common assumption: architectures that favor larger early
layers seem to yield better accuracy.
Combining Deep and Depth: Deep Learning and Face Depth Maps for Driver Attention Monitoring
Authors: Guido Borghi
Date: 2018-12-14 00:00:00
Tasks: Deep Learning, Driver Attention Monitoring, Facial Landmark Detection, Head Pose Estimation, Pose Estimation

Recently, deep learning approaches have achieved promising results in various
fields of computer vision. In this paper, we investigate the combination of
deep learning based methods and depth maps as input images to tackle the
problem of driver attention monitoring. Moreover, we assume the concept of
attention as Head Pose Estimation and Facial Landmark Detection tasks.
Differently from other proposals in the literature, the proposed systems are
able to work directly and based only on raw depth data. All presented methods
are trained and tested on two new public datasets, namely Pandora and
MotorMark, achieving state-of-art results and running with real time
performance.
ESIR: End-to-end Scene Text Recognition via Iterative Image Rectification
Authors: Fangneng Zhan, Shijian Lu
Date: 2018-12-14 00:00:00
Proceeding: CVPR 2019 6
Tasks: Scene Text Recognition

Automated recognition of texts in scenes has been a research challenge for
years, largely due to the arbitrary variation of text appearances in
perspective distortion, text line curvature, text styles and different types of
imaging artifacts. The recent deep networks are capable of learning robust
representations with respect to imaging artifacts and text style changes, but
still face various problems while dealing with scene texts with perspective and
curvature distortions. This paper presents an end-to-end trainable scene text
recognition system (ESIR) that iteratively removes perspective distortion and
text line curvature as driven by better scene text recognition performance. An
innovative rectification network is developed which employs a novel
line-fitting transformation to estimate the pose of text lines in scenes. In
addition, an iterative rectification pipeline is developed where scene text
distortions are corrected iteratively towards a fronto-parallel view. The ESIR
is also robust to parameter initialization and the training needs only scene
text images and word-level annotations as required by most scene text
recognition systems. Extensive experiments over a number of public datasets
show that the proposed ESIR is capable of rectifying scene text distortions
accurately, achieving superior recognition performance for both normal scene
text images and those suffering from perspective and curvature distortions.
Unsupervised Change Detection in Satellite Images Using Convolutional Neural Networks
Authors: Kevin Louis de Jong, Anna Sergeevna Bosman
Date: 2018-12-14 00:00:00
Tasks: Change Detection, Segmentation, Semantic Segmentation

This paper proposes an efficient unsupervised method for detecting relevant
changes between two temporally different images of the same scene. A
convolutional neural network (CNN) for semantic segmentation is implemented to
extract compressed image features, as well as to classify the detected changes
into the correct semantic classes. A difference image is created using the
feature map information generated by the CNN, without explicitly training on
target difference images. Thus, the proposed change detection method is
unsupervised, and can be performed using any CNN model pre-trained for semantic
segmentation.
A corpus of precise natural textual entailment problems
Authors: Jean-Philippe Bernardy, Stergios Chatzikyriakidis
Date: 2018-12-14 00:00:00
Tasks: Natural Language Inference

In this paper, we present a new corpus of entailment problems. This corpus
combines the following characteristics: 1. it is precise (does not leave out
implicit hypotheses) 2. it is based on "real-world" texts (i.e. most of the
premises were written for purposes other than testing textual entailment). 3.
its size is 150. The corpus was constructed by taking problems from the Real
Text Entailment and discovering missing hypotheses using a crowd of experts. We
believe that this corpus constitutes a first step towards wide-coverage testing
of precise natural-language inference systems.
Combating Uncertainty with Novel Losses for Automatic Left Atrium Segmentation
Authors: Xin Yang, Na Wang, Yi Wang, Xu Wang, Reza Nezafat, Dong Ni, Pheng-Ann Heng
Date: 2018-12-14 00:00:00
Tasks: Left Atrium Segmentation, Transfer Learning

Segmenting left atrium in MR volume holds great potentials in promoting the
treatment of atrial fibrillation. However, the varying anatomies, artifacts and
low contrasts among tissues hinder the advance of both manual and automated
solutions. In this paper, we propose a fully-automated framework to segment
left atrium in gadolinium-enhanced MR volumes. The region of left atrium is
firstly automatically localized by a detection module. Our framework then
originates with a customized 3D deep neural network to fully explore the
spatial dependency in the region for segmentation. To alleviate the risk of low
training efficiency and potential overfitting, we enhance our deep network with
the transfer learning and deep supervision strategy. Main contribution of our
network design lies in the composite loss function to combat the boundary
ambiguity and hard examples. We firstly adopt the Overlap loss to encourage
network reduce the overlap between the foreground and background and thus
sharpen the predictions on boundary. We then propose a novel Focal Positive
loss to guide the learning of voxel-specific threshold and emphasize the
foreground to improve classification sensitivity. Further improvement is
obtained with an recursive training scheme. With ablation studies, all the
introduced modules prove to be effective. The proposed framework achieves an
average Dice of 92.24 in segmenting left atrium with pulmonary veins on 20
testing volumes.
A Self-Supervised Bootstrap Method for Single-Image 3D Face Reconstruction
Authors: Yifan Xing, Rahul Tewari, Paulo R. S. Mendonca
Date: 2018-12-14 00:00:00
Tasks: 3D Face Reconstruction, 3D Reconstruction, Face Model, Face Reconstruction

State-of-the-art methods for 3D reconstruction of faces from a single image
require 2D-3D pairs of ground-truth data for supervision. Such data is costly
to acquire, and most datasets available in the literature are restricted to
pairs for which the input 2D images depict faces in a near fronto-parallel
pose. Therefore, many data-driven methods for single-image 3D facial
reconstruction perform poorly on profile and near-profile faces. We propose a
method to improve the performance of single-image 3D facial reconstruction
networks by utilizing the network to synthesize its own training data for
fine-tuning, comprising: (i) single-image 3D reconstruction of faces in
near-frontal images without ground-truth 3D shape; (ii) application of a
rigid-body transformation to the reconstructed face model; (iii) rendering of
the face model from new viewpoints; and (iv) use of the rendered image and
corresponding 3D reconstruction as additional data for supervised fine-tuning.
The new 2D-3D pairs thus produced have the same high-quality observed for near
fronto-parallel reconstructions, thereby nudging the network towards more
uniform performance as a function of the viewing angle of input faces.
Application of the proposed technique to the fine-tuning of a state-of-the-art
single-image 3D-reconstruction network for faces demonstrates the usefulness of
the method, with particularly significant gains for profile or near-profile
views.
Pyramid Network with Online Hard Example Mining for Accurate Left Atrium Segmentation
Authors: Cheng Bian, Xin Yang, Jianqiang Ma, Shen Zheng, Yu-An Liu, Reza Nezafat, Pheng-Ann Heng, Yefeng Zheng
Date: 2018-12-14 00:00:00
Tasks: Left Atrium Segmentation, Segmentation

Accurately segmenting left atrium in MR volume can benefit the ablation
procedure of atrial fibrillation. Traditional automated solutions often fail in
relieving experts from the labor-intensive manual labeling. In this paper, we
propose a deep neural network based solution for automated left atrium
segmentation in gadolinium-enhanced MR volumes with promising performance. We
firstly argue that, for this volumetric segmentation task, networks in 2D
fashion can present great superiorities in time efficiency and segmentation
accuracy than networks with 3D fashion. Considering the highly varying shape of
atrium and the branchy structure of associated pulmonary veins, we propose to
adopt a pyramid module to collect semantic cues in feature maps from multiple
scales for fine-grained segmentation. Also, to promote our network in
classifying the hard examples, we propose an Online Hard Negative Example
Mining strategy to identify voxels in slices with low classification
certainties and penalize the wrong predictions on them. Finally, we devise a
competitive training scheme to further boost the generalization ability of
networks. Extensively verified on 20 testing volumes, our proposed framework
achieves an average Dice of 92.83% in segmenting the left atria and pulmonary
veins.
AdaFlow: Domain-Adaptive Density Estimator with Application to Anomaly Detection and Unpaired Cross-Domain Translation
Authors: Masataka Yamaguchi, Yuma Koizumi, Noboru Harada
Date: 2018-12-14 00:00:00
Tasks: Anomaly Detection, Density Estimation, Translation, Unsupervised Anomaly Detection

We tackle unsupervised anomaly detection (UAD), a problem of detecting data
that significantly differ from normal data. UAD is typically solved by using
density estimation. Recently, deep neural network (DNN)-based density
estimators, such as Normalizing Flows, have been attracting attention. However,
one of their drawbacks is the difficulty in adapting them to the change in the
normal data's distribution. To address this difficulty, we propose AdaFlow, a
new DNN-based density estimator that can be easily adapted to the change of the
distribution. AdaFlow is a unified model of a Normalizing Flow and Adaptive
Batch-Normalizations, a module that enables DNNs to adapt to new distributions.
AdaFlow can be adapted to a new distribution by just conducting forward
propagation once per sample; hence, it can be used on devices that have limited
computational resources. We have confirmed the effectiveness of the proposed
model through an anomaly detection in a sound task. We also propose a method of
applying AdaFlow to the unpaired cross-domain translation problem, in which one
has to train a cross-domain translation model with only unpaired samples. We
have confirmed that our model can be used for the cross-domain translation
problem through experiments on image datasets.
Guaranteed satisficing and finite regret: Analysis of a cognitive satisficing value function
Authors: Akihiro Tamatsukuri, Tatsuji Takahashi
Date: 2018-12-14 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

As reinforcement learning algorithms are being applied to increasingly
complicated and realistic tasks, it is becoming increasingly difficult to solve
such problems within a practical time frame. Hence, we focus on a
\textit{satisficing} strategy that looks for an action whose value is above the
aspiration level (analogous to the break-even point), rather than the optimal
action. In this paper, we introduce a simple mathematical model called
risk-sensitive satisficing ($RS$) that implements a satisficing strategy by
integrating risk-averse and risk-prone attitudes under the greedy policy. We
apply the proposed model to the $K$-armed bandit problems, which constitute the
most basic class of reinforcement learning tasks, and prove two propositions.
The first is that $RS$ is guaranteed to find an action whose value is above the
aspiration level. The second is that the regret (expected loss) of $RS$ is
upper bounded by a finite value, given that the aspiration level is set to an
"optimal level" so that satisficing implies optimizing. We confirm the results
through numerical simulations and compare the performance of $RS$ with that of
other representative algorithms for the $K$-armed bandit problems.
The Entropy of Artificial Intelligence and a Case Study of AlphaZero from Shannon's Perspective
Authors: Bo Zhang, Bin Chen, Jin-lin Peng
Date: 2018-12-14 00:00:00
Tasks: Reinforcement Learning

The recently released AlphaZero algorithm achieves superhuman performance in
the games of chess, shogi and Go, which raises two open questions. Firstly, as
there is a finite number of possibilities in the game, is there a quantifiable
intelligence measurement for evaluating intelligent systems, e.g. AlphaZero?
Secondly, AlphaZero introduces sophisticated reinforcement learning and
self-play to efficiently encode the possible states, is there a simple
information-theoretic model to represent the learning process and offer more
insights in fostering strong AI systems?
  This paper explores the above two questions by proposing a simple variance of
Shannon's communication model, the concept of intelligence entropy and the
Unified Intelligence-Communication Model is proposed, which provide an
information-theoretic metric for investigating the intelligence level and also
provide an bound for intelligent agents in the form of Shannon's capacity,
namely, the intelligence capacity. This paper then applies the concept and
model to AlphaZero as a case study and explains the learning process of
intelligent agent as turbo-like iterative decoding, so that the learning
performance of AlphaZero may be quantitatively evaluated. Finally, conclusions
are provided along with theoretical and practical remarks.
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
Authors: Jingyi Wang, Guoliang Dong, Jun Sun, Xinyu Wang, Peixin Zhang
Date: 2018-12-14 00:00:00
Tasks: Sensitivity, Two-sample testing

Deep neural networks (DNN) have been shown to be useful in a wide range of
applications. However, they are also known to be vulnerable to adversarial
samples. By transforming a normal sample with some carefully crafted human
imperceptible perturbations, even highly accurate DNN make wrong decisions.
Multiple defense mechanisms have been proposed which aim to hinder the
generation of such adversarial samples. However, a recent work show that most
of them are ineffective. In this work, we propose an alternative approach to
detect adversarial samples at runtime. Our main observation is that adversarial
samples are much more sensitive than normal samples if we impose random
mutations on the DNN. We thus first propose a measure of `sensitivity' and show
empirically that normal samples and adversarial samples have distinguishable
sensitivity. We then integrate statistical hypothesis testing and model
mutation testing to check whether an input sample is likely to be normal or
adversarial at runtime by measuring its sensitivity. We evaluated our approach
on the MNIST and CIFAR10 datasets. The results show that our approach detects
adversarial samples generated by state-of-the-art attacking methods efficiently
and accurately.
Making Sense of Random Forest Probabilities: a Kernel Perspective
Authors: Matthew A. Olson, Abraham J. Wyner
Date: 2018-12-14 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, regression

A random forest is a popular tool for estimating probabilities in machine
learning classification tasks. However, the means by which this is accomplished
is unprincipled: one simply counts the fraction of trees in a forest that vote
for a certain class. In this paper, we forge a connection between random
forests and kernel regression. This places random forest probability estimation
on more sound statistical footing. As part of our investigation, we develop a
model for the proximity kernel and relate it to the geometry and sparsity of
the estimation problem. We also provide intuition and recommendations for
tuning a random forest to improve its probability estimates.
AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection
Authors: Chen Ma, Li Chen, Junhai Yong
Date: 2018-12-14 00:00:00
Tasks: Action Unit Detection, Temporal Action Localization

Detecting action units (AUs) on human faces is challenging because various AUs make subtle facial appearance change over various regions at different scales. Current works have attempted to recognize AUs by emphasizing important regions. However, the incorporation of expert prior knowledge into region definition remains under-exploited, and current AU detection approaches do not use regional convolutional neural networks (R-CNN) with expert prior knowledge to directly focus on AU-related regions adaptively. By incorporating expert prior knowledge, we propose a novel R-CNN based model named AU R-CNN. The proposed solution offers two main contributions: (1) AU R-CNN directly observes different facial regions, where various AUs are located. Specifically, we define an AU partition rule which encodes the expert prior knowledge into the region definition and RoI-level label definition. This design produces considerably better detection performance than existing approaches. (2) We integrate various dynamic models (including convolutional long short-term memory, two stream network, conditional random field, and temporal action localization network) into AU R-CNN and then investigate and analyze the reason behind the performance of dynamic models. Experiment results demonstrate that \textit{only} static RGB image information and no optical flow-based AU R-CNN surpasses the one fused with dynamic models. AU R-CNN is also superior to traditional CNNs that use the same backbone on varying image resolutions. State-of-the-art recognition performance of AU detection is achieved. The complete network is end-to-end trainable. Experiments on BP4D and DISFA datasets show the effectiveness of our approach. The implementation code is available online.
Deep Active Learning for Video-based Person Re-identification
Authors: Menglin Wang, Baisheng Lai, Zhongming Jin, Xiaojin Gong, Jianqiang Huang, Xian-Sheng Hua
Date: 2018-12-14 00:00:00
Tasks: Active Learning, Person Re-Identification, Video-Based Person Re-Identification

It is prohibitively expensive to annotate a large-scale video-based person
re-identification (re-ID) dataset, which makes fully supervised methods
inapplicable to real-world deployment. How to maximally reduce the annotation
cost while retaining the re-ID performance becomes an interesting problem. In
this paper, we address this problem by integrating an active learning scheme
into a deep learning framework. Noticing that the truly matched tracklet-pairs,
also denoted as true positives (TP), are the most informative samples for our
re-ID model, we propose a sampling criterion to choose the most TP-likely
tracklet-pairs for annotation. A view-aware sampling strategy considering
view-specific biases is designed to facilitate candidate selection, followed by
an adaptive resampling step to leave out the selected candidates that are
unnecessary to annotate. Our method learns the re-ID model and updates the
annotation set iteratively. The re-ID model is supervised by the tracklets'
pesudo labels that are initialized by treating each tracklet as a distinct
class. With the gained annotations of the actively selected candidates, the
tracklets' pesudo labels are updated by label merging and further used to
re-train our re-ID model. While being simple, the proposed method demonstrates
its effectiveness on three video-based person re-ID datasets. Experimental
results show that less than 3\% pairwise annotations are needed for our method
to reach comparable performance with the fully-supervised setting.
PointPillars: Fast Encoders for Object Detection from Point Clouds
Authors: Alex H. Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, Oscar Beijbom
Date: 2018-12-14 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Object Detection, Autonomous Driving, Birds Eye View Object Detection, Object, object-detection, Object Detection, Robust 3D Object Detection

Object detection in point clouds is an important aspect of many robotics applications such as autonomous driving. In this paper we consider the problem of encoding a point cloud into a format appropriate for a downstream detection pipeline. Recent literature suggests two types of encoders; fixed encoders tend to be fast but sacrifice accuracy, while encoders that are learned from data are more accurate, but slower. In this work we propose PointPillars, a novel encoder which utilizes PointNets to learn a representation of point clouds organized in vertical columns (pillars). While the encoded features can be used with any standard 2D convolutional detection architecture, we further propose a lean downstream network. Extensive experimentation shows that PointPillars outperforms previous encoders with respect to both speed and accuracy by a large margin. Despite only using lidar, our full detection pipeline significantly outperforms the state of the art, even among fusion methods, with respect to both the 3D and bird's eye view KITTI benchmarks. This detection performance is achieved while running at 62 Hz: a 2 - 4 fold runtime improvement. A faster version of our method matches the state of the art at 105 Hz. These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds.