pwc-archive/papers-with-abstracts huggingface dataset:
AuxNet: Auxiliary tasks enhanced Semantic Segmentation for Automated Driving
Authors: Sumanth Chennupati, Ganesh Sistu, Senthil Yogamani, Samir Rawashdeh
Date: 2019-01-17 00:00:00
Tasks: Decision Making, Depth Estimation, Domain Adaptation, Multi-Task Learning, Segmentation, Semantic Segmentation

Decision making in automated driving is highly specific to the environment
and thus semantic segmentation plays a key role in recognizing the objects in
the environment around the car. Pixel level classification once considered a
challenging task which is now becoming mature to be productized in a car.
However, semantic annotation is time consuming and quite expensive. Synthetic
datasets with domain adaptation techniques have been used to alleviate the lack
of large annotated datasets. In this work, we explore an alternate approach of
leveraging the annotations of other tasks to improve semantic segmentation.
Recently, multi-task learning became a popular paradigm in automated driving
which demonstrates joint learning of multiple tasks improves overall
performance of each tasks. Motivated by this, we use auxiliary tasks like depth
estimation to improve the performance of semantic segmentation task. We propose
adaptive task loss weighting techniques to address scale issues in multi-task
loss functions which become more crucial in auxiliary tasks. We experimented on
automotive datasets including SYNTHIA and KITTI and obtained 3% and 5%
improvement in accuracy respectively.
Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students
Authors: Nese Alyuz, Eda Okur, Utku Genc, Sinem Aslan, Cagri Tanriover, Asli Arslan Esme
Date: 2019-01-16 00:00:00

We propose a multimodal approach for detection of students' behavioral
engagement states (i.e., On-Task vs. Off-Task), based on three unobtrusive
modalities: Appearance, Context-Performance, and Mouse. Final behavioral
engagement states are achieved by fusing modality-specific classifiers at the
decision level. Various experiments were conducted on a student dataset
collected in an authentic classroom.
Residual Attention based Network for Hand Bone Age Assessment
Authors: Eric Wu, Bin Kong, Xin Wang, Junjie Bai, Yi Lu, Feng Gao, Shaoting Zhang, Kunlin Cao, Qi Song, Siwei Lyu, Youbing Yin
Date: 2018-12-21 00:00:00
Tasks: Hand Segmentation

Computerized automatic methods have been employed to boost the productivity
as well as objectiveness of hand bone age assessment. These approaches make
predictions according to the whole X-ray images, which include other objects
that may introduce distractions. Instead, our framework is inspired by the
clinical workflow (Tanner-Whitehouse) of hand bone age assessment, which
focuses on the key components of the hand. The proposed framework is composed
of two components: a Mask R-CNN subnet of pixelwise hand segmentation and a
residual attention network for hand bone age assessment. The Mask R-CNN subnet
segments the hands from X-ray images to avoid the distractions of other objects
(e.g., X-ray tags). The hierarchical attention components of the residual
attention subnet force our network to focus on the key components of the X-ray
images and generate the final predictions as well as the associated visual
supports, which is similar to the assessment procedure of clinicians. We
evaluate the performance of the proposed pipeline on the RSNA pediatric bone
age dataset and the results demonstrate its superiority over the previous
methods.
Stochastic Gradient Descent on a Tree: an Adaptive and Robust Approach to Stochastic Convex Optimization
Authors: Sattar Vakili, Sudeep Salgia, Qing Zhao
Date: 2019-01-17 00:00:00

Online minimization of an unknown convex function over the interval $[0,1]$ is considered under first-order stochastic bandit feedback, which returns a random realization of the gradient of the function at each query point. Without knowing the distribution of the random gradients, a learning algorithm sequentially chooses query points with the objective of minimizing regret defined as the expected cumulative loss of the function values at the query points in excess to the minimum value of the function. An approach based on devising a biased random walk on an infinite-depth binary tree constructed through successive partitioning of the domain of the function is developed. Each move of the random walk is guided by a sequential test based on confidence bounds on the empirical mean constructed using the law of the iterated logarithm. With no tuning parameters, this learning algorithm is robust to heavy-tailed noise with infinite variance and adaptive to unknown function characteristics (specifically, convex, strongly convex, and nonsmooth). It achieves the corresponding optimal regret orders (up to a $\sqrt{\log T}$ or a $\log\log T$ factor) in each class of functions and offers better or matching regret orders than the classical stochastic gradient descent approach which requires the knowledge of the function characteristics for tuning the sequence of step-sizes.
Machine Learning Techniques for Brand-Influencer Matchmaking on the Instagram Social Network
Authors: Taylor Sweet, Austin Rothwell, Xuan Luo
Date: 2019-01-17 00:00:00
Tasks: BIG-bench Machine Learning

The social media revolution has changed the way that brands interact with
consumers. Instead of spending their advertising budget on interstate
billboards, more and more companies are choosing to partner with so-called
Internet "influencers" --- individuals who have gained a loyal following on
online platforms for the high quality of the content they post. Unfortunately,
it's not always easy for small brands to find the right influencer: someone who
aligns with their corporate image and has not yet grown in popularity to the
point of unaffordability. In this paper we sought to develop a system for
brand-influencer matchmaking, harnessing the power and flexibility of modern
machine learning techniques. The result is an algorithm that can predict the
most fruitful brand-influencer partnerships based on the similarity of the
content they post.
An Underwater Image Enhancement Benchmark Dataset and Beyond
Authors: Chongyi Li, Chunle Guo, Wenqi Ren, Runmin Cong, Junhui Hou, Sam Kwong, DaCheng Tao
Date: 2019-01-11 00:00:00
Tasks: Image Enhancement, Underwater Image Restoration

Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large-scale real-world images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images. We treat the rest 60 underwater images which cannot obtain satisfactory reference images as challenging data. Using this dataset, we conduct a comprehensive study of the state-of-the-art underwater image enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater image enhancement. The dataset and code are available at https://li-chongyi.github.io/proj_benchmark.html.
Truly Generalizable Radiograph Segmentation with Conditional Domain Adaptation
Authors: Hugo Oliveira, Edemir Ferreira, Jefersson A. dos Santos
Date: 2019-01-16 00:00:00
Tasks: Domain Adaptation, General Classification, Segmentation, Semantic Segmentation, Semi-supervised Domain Adaptation, Transfer Learning, Unsupervised Domain Adaptation

Digitization techniques for biomedical images yield different visual patterns in radiological exams. These differences may hamper the use of data-driven approaches for inference over these images, such as Deep Neural Networks. Another noticeable difficulty in this field is the lack of labeled data, even though in many cases there is an abundance of unlabeled data available. Therefore an important step in improving the generalization capabilities of these methods is to perform Unsupervised and Semi-Supervised Domain Adaptation between different datasets of biomedical images. In order to tackle this problem, in this work we propose an Unsupervised and Semi-Supervised Domain Adaptation method for segmentation of biomedical images using Generative Adversarial Networks for Unsupervised Image Translation. We merge these unsupervised networks with supervised deep semantic segmentation architectures in order to create a semi-supervised method capable of learning from both unlabeled and labeled data, whenever labeling is available. We compare our method using several domains, datasets, segmentation tasks and traditional baselines, such as unsupervised distance-based methods and reusing pretrained models both with and without Fine-tuning. We perform both quantitative and qualitative analysis of the proposed method and baselines in the distinct scenarios considered in our experimental evaluation. The proposed method shows consistently better results than the baselines in scarce labeled data scenarios, achieving Jaccard values greater than 0.9 and good segmentation quality in most tasks. Unsupervised Domain Adaptation results were observed to be close to the Fully Supervised Domain Adaptation used in the traditional procedure of Fine-tuning pretrained networks.
Primitive-based 3D Building Modeling, Sensor Simulation, and Estimation
Authors: Xia Li, Yen-Liang Lin, James Miller, Alex Cheon, Walt Dixon
Date: 2019-01-16 00:00:00
Tasks: Instance Segmentation, Semantic Segmentation

As we begin to consider modeling large, realistic 3D building scenes, it
becomes necessary to consider a more compact representation over the polygonal
mesh model. Due to the large amounts of annotated training data, which is
costly to obtain, we leverage synthetic data to train our system for the
satellite image domain. By utilizing the synthetic data, we formulate the
building decomposition as an application of instance segmentation and primitive
fitting to decompose a building into a set of primitive shapes. Experimental
results on WorldView-3 satellite image dataset demonstrate the effectiveness of
our 3D building modeling approach.
Class-Balanced Loss Based on Effective Number of Samples
Authors: Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang song, Serge Belongie
Date: 2019-01-16 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Classification, Long-tail Learning

With the rapid increase of large-scale, real-world datasets, it becomes
critical to address the problem of long-tailed data distribution (i.e., a few
classes account for most of the data, while most classes are
under-represented). Existing solutions typically adopt class re-balancing
strategies such as re-sampling and re-weighting based on the number of
observations for each class. In this work, we argue that as the number of
samples increases, the additional benefit of a newly added data point will
diminish. We introduce a novel theoretical framework to measure data overlap by
associating with each sample a small neighboring region rather than a single
point. The effective number of samples is defined as the volume of samples and
can be calculated by a simple formula $(1-\beta^{n})/(1-\beta)$, where $n$ is
the number of samples and $\beta \in [0,1)$ is a hyperparameter. We design a
re-weighting scheme that uses the effective number of samples for each class to
re-balance the loss, thereby yielding a class-balanced loss. Comprehensive
experiments are conducted on artificially induced long-tailed CIFAR datasets
and large-scale datasets including ImageNet and iNaturalist. Our results show
that when trained with the proposed class-balanced loss, the network is able to
achieve significant performance gains on long-tailed datasets.
Visual Feature Fusion and its Application to Support Unsupervised Clustering Tasks
Authors: Gladys Hilasaca, Fernando Paulovich
Date: 2019-01-16 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Small Data Image Classification

On visual analytics applications, the concept of putting the user on the loop
refers to the ability to replace heuristics by user knowledge on machine
learning and data mining tasks. On supervised tasks, the user engagement occurs
via the manipulation of the training data. However, on unsupervised tasks, the
user involvement is limited to changes in the algorithm parametrization or the
input data representation, also known as features. Depending on the application
domain, different types of features can be extracted from the raw data.
Therefore, the result of unsupervised algorithms heavily depends on the type of
employed feature. Since there is no perfect feature extractor, combining
different features have been explored in a process called feature fusion. The
feature fusion is straightforward when the machine learning or data mining task
has a cost function. However, when such a function does not exist, user support
for combination needs to be provided otherwise the process is impractical. In
this paper, we present a novel feature fusion approach that uses small data
samples to allows users not only to effortless control the combination of
different feature sets but also to interpret the attained results. The
effectiveness of our approach is confirmed by a comprehensive set of
qualitative and quantitative tests, opening up different possibilities of
user-guided analytical scenarios not covered yet. The ability of our approach
to providing real-time feedback for the feature fusion is exploited on the
context of unsupervised clustering techniques, where the composed groups
reflect the semantics of the feature combination.
Soft Rasterizer: Differentiable Rendering for Unsupervised Single-View Mesh Reconstruction
Authors: Shichen Liu, Weikai Chen, Tianye Li, Hao Li
Date: 2019-01-17 00:00:00

Rendering is the process of generating 2D images from 3D assets, simulated in
a virtual environment, typically with a graphics pipeline. By inverting such
renderer, one can think of a learning approach to predict a 3D shape from an
input image. However, standard rendering pipelines involve a fundamental
discretization step called rasterization, which prevents the rendering process
to be differentiable, hence suitable for learning. We present the first
non-parametric and truly differentiable rasterizer based on silhouettes. Our
method enables unsupervised learning for high-quality 3D mesh reconstruction
from a single image. We call our framework `soft rasterizer' as it provides an
accurate soft approximation of the standard rasterizer. The key idea is to fuse
the probabilistic contributions of all mesh triangles with respect to the
rendered pixels. When combined with a mesh generator in a deep neural network,
our soft rasterizer is able to generate an approximated silhouette of the
generated polygon mesh in the forward pass. The rendering loss is
back-propagated to supervise the mesh generation without the need of 3D
training data. Experimental results demonstrate that our approach significantly
outperforms the state-of-the-art unsupervised techniques, both quantitatively
and qualitatively. We also show that our soft rasterizer can achieve comparable
results to the cutting-edge supervised learning method and in various cases
even better ones, especially for real-world data.
Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing
Authors: Xiaoguang Tu, Jian Zhao, Mei Xie, Guodong Du, Hengsheng Zhang, Jianshu Li, Zheng Ma, Jiashi Feng
Date: 2019-01-17 00:00:00
Tasks: Domain Adaptation, Face Anti-Spoofing, Face Recognition

Face anti-spoofing (a.k.a presentation attack detection) has drawn growing
attention due to the high-security demand in face authentication systems.
Existing CNN-based approaches usually well recognize the spoofing faces when
training and testing spoofing samples display similar patterns, but their
performance would drop drastically on testing spoofing faces of unseen scenes.
In this paper, we try to boost the generalizability and applicability of these
methods by designing a CNN model with two major novelties. First, we propose a
simple yet effective Total Pairwise Confusion (TPC) loss for CNN training,
which enhances the generalizability of the learned Presentation Attack (PA)
representations. Secondly, we incorporate a Fast Domain Adaptation (FDA)
component into the CNN model to alleviate negative effects brought by domain
changes. Besides, our proposed model, which is named Generalizable Face
Authentication CNN (GFA-CNN), works in a multi-task manner, performing face
anti-spoofing and face recognition simultaneously. Experimental results show
that GFA-CNN outperforms previous face anti-spoofing approaches and also well
preserves the identity information of input face images.
Hand Sign to Bangla Speech: A Deep Learning in Vision based system for Recognizing Hand Sign Digits and Generating Bangla Speech
Authors: Shahjalal Ahmed, Md. Rafiqul Islam, Jahid Hassan, Minhaz Uddin Ahmed, Bilkis Jamal Ferdosi, Sanjay Saha, Md. Shopon
Date: 2019-01-17 00:00:00
Tasks: Gesture Recognition, text-to-speech, Text to Speech

Recent advancements in the field of computer vision with the help of deep
neural networks have led us to explore and develop many existing challenges
that were once unattended due to the lack of necessary technologies. Hand
Sign/Gesture Recognition is one of the significant areas where the deep neural
network is making a substantial impact. In the last few years, a large number
of researches has been conducted to recognize hand signs and hand gestures,
which we aim to extend to our mother-tongue, Bangla (also known as Bengali).
The primary goal of our work is to make an automated tool to aid the people who
are unable to speak. We developed a system that automatically detects hand sign
based digits and speaks out the result in Bangla language. According to the
report of the World Health Organization (WHO), 15% of people in the world live
with some kind of disabilities. Among them, individuals with communication
impairment such as speech disabilities experience substantial barrier in social
interaction. The proposed system can be invaluable to mitigate such a barrier.
The core of the system is built with a deep learning model which is based on
convolutional neural networks (CNN). The model classifies hand sign based
digits with 92% accuracy over validation data which ensures it a highly
trustworthy system. Upon classification of the digits, the resulting output is
fed to the text to speech engine and the translator unit eventually which
generates audio output in Bangla language. A web application to demonstrate our
tool is available at http://bit.ly/signdigits2banglaspeech.
Deep Transfer Across Domains for Face Anti-spoofing
Authors: Xiaoguang Tu, Hengsheng Zhang, Mei Xie, Yao Luo, Yuefei Zhang, Zheng Ma
Date: 2019-01-17 00:00:00
Tasks: Face Anti-Spoofing, Face Recognition

A practical face recognition system demands not only high recognition performance, but also the capability of detecting spoofing attacks. While emerging approaches of face anti-spoofing have been proposed in recent years, most of them do not generalize well to new database. The generalization ability of face anti-spoofing needs to be significantly improved before they can be adopted by practical application systems. The main reason for the poor generalization of current approaches is the variety of materials among the spoofing devices. As the attacks are produced by putting a spoofing display (e.t., paper, electronic screen, forged mask) in front of a camera, the variety of spoofing materials can make the spoofing attacks quite different. Furthermore, the background/lighting condition of a new environment can make both the real accesses and spoofing attacks different. Another reason for the poor generalization is that limited labeled data is available for training in face anti-spoofing. In this paper, we focus on improving the generalization ability across different kinds of datasets. We propose a CNN framework using sparsely labeled data from the target domain to learn features that are invariant across domains for face anti-spoofing. Experiments on public-domain face spoofing databases show that the proposed method significantly improve the cross-dataset testing performance only with a small number of labeled samples from the target domain.
Cognitive Analysis of 360 degree Surround Photos
Authors: Madhawa Vidanapathirana, Lakmal Meegahapola, Indika Perera
Date: 2019-01-17 00:00:00

360 degrees surround photography or photospheres have taken the world by
storm as the new media for content creation providing viewers rich, immersive
experience compared to conventional photography. With the emergence of Virtual
Reality as a mainstream trend, the 360 degrees photography is increasingly
important to offer a practical approach to the general public to capture
virtual reality ready content from their mobile phones without explicit tool
support or knowledge. Even though the amount of 360-degree surround content
being uploaded to the Internet continues to grow, there is no proper way to
index them or to process them for further information. This is because of the
difficulty in image processing the photospheres due to the distorted nature of
objects embedded. This challenge lies in the way 360-degree panoramic
photospheres are saved. This paper presents a unique, and innovative technique
named Photosphere to Cognition Engine (P2CE), which allows cognitive analysis
on 360-degree surround photos using existing image cognitive analysis
algorithms and APIs designed for conventional photos. We have optimized the
system using a wide variety of indoor and outdoor samples and extensive
evaluation approaches. On average, P2CE provides up-to 100% growth in accuracy
on image cognitive analysis of Photospheres over direct use of conventional
non-photosphere based Image Cognition Systems.
Enhance the Motion Cues for Face Anti-Spoofing using CNN-LSTM Architecture
Authors: Xiaoguang Tu, Hengsheng Zhang, Mei Xie, Yao Luo, Yuefei Zhang, Zheng Ma
Date: 2019-01-17 00:00:00
Tasks: Face Anti-Spoofing, Motion Magnification

Spatio-temporal information is very important to capture the discriminative
cues between genuine and fake faces from video sequences. To explore such a
temporal feature, the fine-grained motions (e.g., eye blinking, mouth movements
and head swing) across video frames are very critical. In this paper, we
propose a joint CNN-LSTM network for face anti-spoofing, focusing on the motion
cues across video frames. We first extract the high discriminative features of
video frames using the conventional Convolutional Neural Network (CNN). Then we
leverage Long Short-Term Memory (LSTM) with the extracted features as inputs to
capture the temporal dynamics in videos. To ensure the fine-grained motions
more easily to be perceived in the training process, the eulerian motion
magnification is used as the preprocessing to enhance the facial expressions
exhibited by individuals, and the attention mechanism is embedded in LSTM to
ensure the model learn to focus selectively on the dynamic frames across the
video clips. Experiments on Replay Attack and MSU-MFSD databases show that the
proposed method yields state-of-the-art performance with better generalization
ability compared with several other popular algorithms.