pwc-archive/papers-with-abstracts huggingface dataset:
Dance Dance Generation: Motion Transfer for Internet Videos
Authors: Yipin Zhou, Zhaowen Wang, Chen Fang, Trung Bui, Tamara L. Berg
Date: 2019-03-30 00:00:00

This work presents computational methods for transferring body movements from
one person to another with videos collected in the wild. Specifically, we train
a personalized model on a single video from the Internet which can generate
videos of this target person driven by the motions of other people. Our model
is built on two generative networks: a human (foreground) synthesis net which
generates photo-realistic imagery of the target person in a novel pose, and a
fusion net which combines the generated foreground with the scene (background),
adding shadows or reflections as needed to enhance realism. We validate the the
efficacy of our proposed models over baselines with qualitative and
quantitative evaluations as well as a subjective test.
UVA: A Universal Variational Framework for Continuous Age Analysis
Authors: Pei-Pei Li, Huaibo Huang, Yibo Hu, Xiang Wu, Ran He, Zhenan Sun
Date: 2019-03-30 00:00:00
Tasks: Age Estimation, MORPH, Translation

Conventional methods for facial age analysis tend to utilize accurate age
labels in a supervised way. However, existing age datasets lies in a limited
range of ages, leading to a long-tailed distribution. To alleviate the problem,
this paper proposes a Universal Variational Aging (UVA) framework to formulate
facial age priors in a disentangling manner. Benefiting from the variational
evidence lower bound, the facial images are encoded and disentangled into an
age-irrelevant distribution and an age-related distribution in the latent
space. A conditional introspective adversarial learning mechanism is introduced
to boost the image quality. In this way, when manipulating the age-related
distribution, UVA can achieve age translation with arbitrary ages. Further, by
sampling noise from the age-irrelevant distribution, we can generate
photorealistic facial images with a specific age. Moreover, given an input face
image, the mean value of age-related distribution can be treated as an age
estimator. These indicate that UVA can efficiently and accurately estimate the
age-related distribution by a disentangling manner, even if the training
dataset performs a long-tailed age distribution. UVA is the first attempt to
achieve facial age analysis tasks, including age translation, age generation
and age estimation, in a universal framework. The qualitative and quantitative
experiments demonstrate the superiority of UVA on five popular datasets,
including CACD2000, Morph, UTKFace, MegaAge-Asian and FG-NET.
Exposing GAN-synthesized Faces Using Landmark Locations
Authors: Xin Yang, Yuezun Li, Honggang Qi, Siwei Lyu
Date: 2019-03-30 00:00:00
Tasks: General Classification, Image Generation

Generative adversary networks (GANs) have recently led to highly realistic
image synthesis results. In this work, we describe a new method to expose
GAN-synthesized images using the locations of the facial landmark points. Our
method is based on the observations that the facial parts configuration
generated by GAN models are different from those of the real faces, due to the
lack of global constraints. We perform experiments demonstrating this
phenomenon, and show that an SVM classifier trained using the locations of
facial landmark points is sufficient to achieve good classification performance
for GAN-synthesized faces.
M2FPA: A Multi-Yaw Multi-Pitch High-Quality Database and Benchmark for Facial Pose Analysis
Authors: Pei-Pei Li, Xiang Wu, Yibo Hu, Ran He, Zhenan Sun
Date: 2019-03-30 00:00:00
Tasks: Attribute, Face Generation, Face Recognition, Pose Estimation, Robust Face Recognition

Facial images in surveillance or mobile scenarios often have large view-point variations in terms of pitch and yaw angles. These jointly occurred angle variations make face recognition challenging. Current public face databases mainly consider the case of yaw variations. In this paper, a new large-scale Multi-yaw Multi-pitch high-quality database is proposed for Facial Pose Analysis (M2FPA), including face frontalization, face rotation, facial pose estimation and pose-invariant face recognition. It contains 397,544 images of 229 subjects with yaw, pitch, attribute, illumination and accessory. M2FPA is the most comprehensive multi-view face database for facial pose analysis. Further, we provide an effective benchmark for face frontalization and pose-invariant face recognition on M2FPA with several state-of-the-art methods, including DR-GAN, TP-GAN and CAPG-GAN. We believe that the new database and benchmark can significantly push forward the advance of facial pose analysis in real-world applications. Moreover, a simple yet effective parsing guided discriminator is introduced to capture the local consistency during GAN optimization. Extensive quantitative and qualitative results on M2FPA and Multi-PIE demonstrate the superiority of our face frontalization method. Baseline results for both face synthesis and face recognition from state-of-theart methods demonstrate the challenge offered by this new database.
Adaptive Adjustment with Semantic Feature Space for Zero-Shot Recognition
Authors: Jingcai Guo, Song Guo
Date: 2019-03-30 00:00:00
Tasks: Zero-Shot Learning

In most recent years, zero-shot recognition (ZSR) has gained increasing
attention in machine learning and image processing fields. It aims at
recognizing unseen class instances with knowledge transferred from seen
classes. This is typically achieved by exploiting a pre-defined semantic
feature space (FS), i.e., semantic attributes or word vectors, as a bridge to
transfer knowledge between seen and unseen classes. However, due to the absence
of unseen classes during training, the conventional ZSR easily suffers from
domain shift and hubness problems. In this paper, we propose a novel ZSR
learning framework that can handle these two issues well by adaptively
adjusting semantic FS. To the best of our knowledge, our work is the first to
consider the adaptive adjustment of semantic FS in ZSR. Moreover, our solution
can be formulated to a more efficient framework that significantly boosts the
training. Extensive experiments show the remarkable performance improvement of
our model compared with other existing methods.
An LBP-HOG Descriptor Based on Matrix Projection For Mammogram Classification
Authors: Zainab Alhakeem, Se-In Jang
Date: 2019-03-30 00:00:00
Tasks: Computational Efficiency, General Classification

In image based feature descriptor design, local information from image patches are extracted using iterative scanning operations which cause high computational costs. In order to avoid such scanning operations, we present matrix multiplication based local feature descriptors, namely a Matrix projection based Local Binary Pattern (M-LBP) descriptor and a Matrix projection based Histogram of Oriented Gradients (M-HOG) descriptor. Additionally, an integrated formulation of M-LBP and M-HOG (M-LBP-HOG) is also proposed to perform the two descriptors together in a single step. The proposed descriptors are evaluated using a publicly available mammogram database. The results show promising performances in terms of classification accuracy and computational efficiency.
Exploiting SIFT Descriptor for Rotation Invariant Convolutional Neural Network
Authors: Abhay Kumar, Nishant Jain, Chirag Singh, Suraj Tripathi
Date: 2019-03-30 00:00:00

This paper presents a novel approach to exploit the distinctive invariant
features in convolutional neural network. The proposed CNN model uses Scale
Invariant Feature Transform (SIFT) descriptor instead of the max-pooling layer.
Max-pooling layer discards the pose, i.e., translational and rotational
relationship between the low-level features, and hence unable to capture the
spatial hierarchies between low and high level features. The SIFT descriptor
layer captures the orientation and the spatial relationship of the features
extracted by convolutional layer. The proposed SIFT Descriptor CNN therefore
combines the feature extraction capabilities of CNN model and rotation
invariance of SIFT descriptor. Experimental results on the MNIST and
fashionMNIST datasets indicates reasonable improvements over conventional
methods available in literature.
Boundary Aware Multi-Focus Image Fusion Using Deep Neural Network
Authors: Haoyu Ma, Juncheng Zhang, Shaojun Liu, Qingmin Liao
Date: 2019-03-30 00:00:00
Tasks: Dataset Generation, Multi Focus Image Fusion

Since it is usually difficult to capture an all-in-focus image of a 3D scene
directly, various multi-focus image fusion methods are employed to generate it
from several images focusing at different depths. However, the performance of
existing methods is barely satisfactory and often degrades for areas near the
focused/defocused boundary (FDB). In this paper, a boundary aware method using
deep neural network is proposed to overcome this problem. (1) Aiming to acquire
improved fusion images, a 2-channel deep network is proposed to better extract
the relative defocus information of the two source images. (2) After analyzing
the different situations for patches far away from and near the FDB, we use two
networks to handle them respectively. (3) To simulate the reality more
precisely, a new approach of dataset generation is designed. Experiments
demonstrate that the proposed method outperforms the state-of-the-art methods,
both qualitatively and quantitatively.
A HVS-inspired Attention to Improve Loss Metrics for CNN-based Perception-Oriented Super-Resolution
Authors: Taimoor Tariq, Juan Luis Gonzalez, Munchurl Kim
Date: 2019-03-30 00:00:00
Tasks: Image Restoration, Sensitivity, Super-Resolution

Deep Convolutional Neural Network (CNN) features have been demonstrated to be effective perceptual quality features. The perceptual loss, based on feature maps of pre-trained CNN's has proven to be remarkably effective for CNN based perceptual image restoration problems. In this work, taking inspiration from the the Human Visual System (HVS) and visual perception, we propose a spatial attention mechanism based on the dependency human contrast sensitivity on spatial frequency. We identify regions in input images, based on the underlying spatial frequency, which are not generally well reconstructed during Super-Resolution but are most important in terms of visual sensitivity. Based on this prior, we design a spatial attention map that is applied to feature maps in the perceptual loss and its variants, helping them to identify regions that are of more perceptual importance. The results demonstrate the our technique improves the ability of the perceptual loss and contextual loss to deliver more natural images in CNN based super-resolution.
RefineLoc: Iterative Refinement for Weakly-Supervised Action Localization
Authors: Alejandro Pardo, Humam Alwassel, Fabian Caba Heilbron, Ali Thabet, Bernard Ghanem
Date: 2019-03-30 00:00:00
Tasks: Action Localization, Temporal Action Localization, Temporal Localization, Weakly Supervised Action Localization, Weakly-supervised Temporal Action Localization

Video action detectors are usually trained using datasets with fully-supervised temporal annotations. Building such datasets is an expensive task. To alleviate this problem, recent methods have tried to leverage weak labeling, where videos are untrimmed and only a video-level label is available. In this paper, we propose RefineLoc, a novel weakly-supervised temporal action localization method. RefineLoc uses an iterative refinement approach by estimating and training on snippet-level pseudo ground truth at every iteration. We show the benefit of this iterative approach and present an extensive analysis of five different pseudo ground truth generators. We show the effectiveness of our model on two standard action datasets, ActivityNet v1.2 and THUMOS14. RefineLoc shows competitive results with the state-of-the-art in weakly-supervised temporal localization. Additionally, our iterative refinement process is able to significantly improve the performance of two state-of-the-art methods, setting a new state-of-the-art on THUMOS14.
USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds
Authors: Jiaxin Li, Gim Hee Lee
Date: 2019-03-30 00:00:00
Proceeding: ICCV 2019 10
Tasks: Interest Point Detection

In this paper, we propose the USIP detector: an Unsupervised Stable Interest
Point detector that can detect highly repeatable and accurately localized
keypoints from 3D point clouds under arbitrary transformations without the need
for any ground truth training data. Our USIP detector consists of a feature
proposal network that learns stable keypoints from input 3D point clouds and
their respective transformed pairs from randomly generated transformations. We
provide degeneracy analysis of our USIP detector and suggest solutions to
prevent it. We encourage high repeatability and accurate localization of the
keypoints with a probabilistic chamfer loss that minimizes the distances
between the detected keypoints from the training point cloud pairs. Extensive
experimental results of repeatability tests on several simulated and real-world
3D point cloud datasets from Lidar, RGB-D and CAD models show that our USIP
detector significantly outperforms existing hand-crafted and deep
learning-based 3D keypoint detectors. Our code is available at the project
website. https://github.com/lijx10/USIP
MortonNet: Self-Supervised Learning of Local Features in 3D Point Clouds
Authors: Ali Thabet, Humam Alwassel, Bernard Ghanem
Date: 2019-03-30 00:00:00
Tasks: Segmentation, Self-Supervised Learning, Semantic Segmentation

We present a self-supervised task on point clouds, in order to learn
meaningful point-wise features that encode local structure around each point.
Our self-supervised network, named MortonNet, operates directly on
unstructured/unordered point clouds. Using a multi-layer RNN, MortonNet
predicts the next point in a point sequence created by a popular and fast Space
Filling Curve, the Morton-order curve. The final RNN state (coined Morton
feature) is versatile and can be used in generic 3D tasks on point clouds. In
fact, we show how Morton features can be used to significantly improve
performance (+3% for 2 popular semantic segmentation algorithms) in the task of
semantic segmentation of point clouds on the challenging and large-scale S3DIS
dataset. We also show how MortonNet trained on S3DIS transfers well to another
large-scale dataset, vKITTI, leading to an improvement over state-of-the-art of
3.8%. Finally, we use Morton features to train a much simpler and more stable
model for part segmentation in ShapeNet. Our results show how our
self-supervised task results in features that are useful for 3D segmentation
tasks, and generalize well to other datasets.
OSVNet: Convolutional Siamese Network for Writer Independent Online Signature Verification
Authors: Chandra Sekhar, Prerana Mukherjee, Devanur S Guru, Viswanath Pulabaigari
Date: 2019-03-30 00:00:00
Tasks: One-Shot Learning, Representation Learning

Online signature verification (OSV) is one of the most challenging tasks in writer identification and digital forensics. Owing to the large intra-individual variability, there is a critical requirement to accurately learn the intra-personal variations of the signature to achieve higher classification accuracy. To achieve this, in this paper, we propose an OSV framework based on deep convolutional Siamese network (DCSN). DCSN automatically extracts robust feature descriptions based on metric-based loss function which decreases intra-writer variability (Genuine-Genuine) and increases inter-individual variability (Genuine-Forgery) and directs the DCSN for effective discriminative representation learning for online signatures and extend it for one shot learning framework. Comprehensive experimentation conducted on three widely accepted benchmark datasets MCYT-100 (DB1), MCYT-330 (DB2) and SVC-2004-Task2 demonstrate the capability of our framework to distinguish the genuine and forgery samples. Experimental results confirm the efficiency of deep convolutional Siamese network based OSV by achieving a lower error rate as compared to many recent and state-of-the art OSV techniques.
Person Re-identification with Bias-controlled Adversarial Training
Authors: Sara Iodice, Krystian Mikolajczyk
Date: 2019-03-30 00:00:00
Tasks: Person Re-Identification

Inspired by the effectiveness of adversarial training in the area of
Generative Adversarial Networks we present a new approach for learning feature
representations in person re-identification. We investigate different types of
bias that typically occur in re-ID scenarios, i.e., pose, body part and camera
view, and propose a general approach to address them. We introduce an
adversarial strategy for controlling bias, named Bias-controlled Adversarial
framework (BCA), with two complementary branches to reduce or to enhance
bias-related features. The results and comparison to the state of the art on
different benchmarks show that our framework is an effective strategy for
person re-identification. The performance improvements are in both full and
partial views of persons.
Classification of Motorcycles using Extracted Images of Traffic Monitoring Videos
Authors: Adriano Belletti Felicio, André Luiz Cunha
Date: 2019-03-30 00:00:00
Tasks: Classification, General Classification

Due to the great growth of motorcycles in the urban fleet and the growth of the study on its behavior and of how this vehicle affects the flow of traffic becomes necessary the development of tools and techniques different from the conventional ones to identify its presence in the traffic flow and be able to extract your information. The article in question attempts to contribute to the study on this type of vehicle by generating a motorcycle image bank and developing and calibrating a motorcycle classifier by combining the LBP techniques to create the characteristic vectors and the classification technique LinearSVC to perform the predictions. In this way the classifier of vehicles of the type motorcycle developed in this research can classify the images of vehicles extracted of videos of monitoring between two classes motorcycles and non-motorcycles with a precision and an accuracy superior to 0,9.
Person-in-WiFi: Fine-grained Person Perception using WiFi
Authors: Fei Wang, Sanping Zhou, Stanislav Panev, Jinsong Han, Dong Huang
Date: 2019-03-30 00:00:00
Proceeding: ICCV 2019 10
Tasks: Pose Estimation, RF-based Pose Estimation

Fine-grained person perception such as body segmentation and pose estimation
has been achieved with many 2D and 3D sensors such as RGB/depth cameras, radars
(e.g., RF-Pose) and LiDARs. These sensors capture 2D pixels or 3D point clouds
of person bodies with high spatial resolution, such that the existing
Convolutional Neural Networks can be directly applied for perception. In this
paper, we take one step forward to show that fine-grained person perception is
possible even with 1D sensors: WiFi antennas. To our knowledge, this is the
first work to perceive persons with pervasive WiFi devices, which is cheaper
and power efficient than radars and LiDARs, invariant to illumination, and has
little privacy concern comparing to cameras. We used two sets of off-the-shelf
WiFi antennas to acquire signals, i.e., one transmitter set and one receiver
set. Each set contains three antennas lined-up as a regular household WiFi
router. The WiFi signal generated by a transmitter antenna, penetrates through
and reflects on human bodies, furniture and walls, and then superposes at a
receiver antenna as a 1D signal sample (instead of 2D pixels or 3D point
clouds). We developed a deep learning approach that uses annotations on 2D
images, takes the received 1D WiFi signals as inputs, and performs body
segmentation and pose estimation in an end-to-end manner. Experimental results
on over 100000 frames under 16 indoor scenes demonstrate that Person-in-WiFi
achieved person perception comparable to approaches using 2D images.
Can WiFi Estimate Person Pose?
Authors: Fei Wang, Stanislav Panev, Ziyi Dai, Jinsong Han, Dong Huang
Date: 2019-03-30 00:00:00
Tasks: 3D Human Pose Estimation, General Classification, Indoor Localization, RF-based Pose Estimation

WiFi human sensing has achieved great progress in indoor localization,
activity classification, etc. Retracing the development of these work, we have
a natural question: can WiFi devices work like cameras for vision applications?
In this paper We try to answer this question by exploring the ability of WiFi
on estimating single person pose. We use a 3-antenna WiFi sender and a
3-antenna receiver to generate WiFi data. Meanwhile, we use a synchronized
camera to capture person videos for corresponding keypoint annotations. We
further propose a fully convolutional network (FCN), termed WiSPPN, to estimate
single person pose from the collected data and annotations. Evaluation on over
80k images (16 sites and 8 persons) replies aforesaid question with a positive
answer. Codes have been made publicly available at
https://github.com/geekfeiw/WiSPPN.
Evaluating CNNs on the Gestalt Principle of Closure
Authors: Gregor Ehrensperger, Sebastian Stabinger, Antonio Rodríguez Sánchez
Date: 2019-03-30 00:00:00
Tasks: valid

Deep convolutional neural networks (CNNs) are widely known for their
outstanding performance in classification and regression tasks over
high-dimensional data. This made them a popular and powerful tool for a large
variety of applications in industry and academia. Recent publications show that
seemingly easy classifaction tasks (for humans) can be very challenging for
state of the art CNNs. An attempt to describe how humans perceive visual
elements is given by the Gestalt principles. In this paper we evaluate AlexNet
and GoogLeNet regarding their performance on classifying the correctness of the
well known Kanizsa triangles, which heavily rely on the Gestalt principle of
closure. Therefore we created various datasets containing valid as well as
invalid variants of the Kanizsa triangle. Our findings suggest that perceiving
objects by utilizing the principle of closure is very challenging for the
applied network architectures but they appear to adapt to the effect of
closure.
Two-phase flow regime prediction using LSTM based deep recurrent neural network
Authors: Zhuoran Dang, Mamoru Ishii
Date: 2019-03-30 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis, Time Series Prediction

Long short-term memory (LSTM) and recurrent neural network (RNN) has achieved
great successes on time-series prediction. In this paper, a methodology of
using LSTM-based deep-RNN for two-phase flow regime prediction is proposed,
motivated by previous research on constructing deep RNN. The method is featured
with fast response and accuracy. The built RNN networks are trained and tested
with time-series void fraction data collected using impedance void meter. The
result shows that the prediction accuracy depends on the depth of network and
the number of layer cells. However, deeper and larger network consumes more
time in predicting.
Discrete Rotation Equivariance for Point Cloud Recognition
Authors: Jiaxin Li, Yingcai Bi, Gim Hee Lee
Date: 2019-03-31 00:00:00

Despite the recent active research on processing point clouds with deep
networks, few attention has been on the sensitivity of the networks to
rotations. In this paper, we propose a deep learning architecture that achieves
discrete $\mathbf{SO}(2)$/$\mathbf{SO}(3)$ rotation equivariance for point
cloud recognition. Specifically, the rotation of an input point cloud with
elements of a rotation group is similar to shuffling the feature vectors
generated by our approach. The equivariance is easily reduced to invariance by
eliminating the permutation with operations such as maximum or average. Our
method can be directly applied to any existing point cloud based networks,
resulting in significant improvements in their performance for rotated inputs.
We show state-of-the-art results in the classification tasks with various
datasets under both $\mathbf{SO}(2)$ and $\mathbf{SO}(3)$ rotations. In
addition, we further analyze the necessary conditions of applying our approach
to PointNet based networks. Source codes at
https://github.com/lijx10/rot-equ-net