pwc-archive/papers-with-abstracts huggingface dataset:
Converting a Common Document Scanner to a Multispectral Scanner
Authors: Zohaib Khan, Faisal Shafait, Ajmal Mian
Date: 2019-04-17 00:00:00

We propose the construction of a prototype scanner designed to capture
multispectral images of documents. A standard sheet-feed scanner is modified by
disconnecting its internal light source and connecting an external
multispectral light source comprising of narrow band light emitting diodes
(LED). A document is scanned by illuminating the scanner light guide
successively with different LEDs and capturing a scan of the document. The
system is portable and can be used for potential applications in verification
of questioned documents, cheques, receipts and bank notes.
State Classification of Cooking Objects Using a VGG CNN
Authors: Kyle Mott
Date: 2019-04-21 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Data Augmentation, General Classification, image-classification, Image Classification

In machine learning, it is very important for a robot to know the state of an
object and recognize particular desired states. This is an image classification
problem that can be solved using a convolutional neural network. In this paper,
we will discuss the use of a VGG convolutional neural network to recognize
those states of cooking objects. We will discuss the uses of activation
functions, optimizers, data augmentation, layer additions, and other different
versions of architectures. The results of this paper will be used to identify
alternatives to the VGG convolutional neural network to improve accuracy.
Everyone is a Cartoonist: Selfie Cartoonization with Attentive Adversarial Networks
Authors: Xinyu Li, Wei zhang, Tong Shen, Tao Mei
Date: 2019-04-20 00:00:00
Tasks: Generative Adversarial Network, Translation

Selfie and cartoon are two popular artistic forms that are widely presented
in our daily life. Despite the great progress in image translation/stylization,
few techniques focus specifically on selfie cartoonization, since cartoon
images usually contain artistic abstraction (e.g., large smoothing areas) and
exaggeration (e.g., large/delicate eyebrows). In this paper, we address this
problem by proposing a selfie cartoonization Generative Adversarial Network
(scGAN), which mainly uses an attentive adversarial network (AAN) to emphasize
specific facial regions and ignore low-level details. More specifically, we
first design a cycle-like architecture to enable training with unpaired data.
Then we design three losses from different aspects. A total variation loss is
used to highlight important edges and contents in cartoon portraits. An
attentive cycle loss is added to lay more emphasis on delicate facial areas
such as eyes. In addition, a perceptual loss is included to eliminate artifacts
and improve robustness of our method. Experimental results show that our method
is capable of generating different cartoon styles and outperforms a number of
state-of-the-art methods.
Deep Learning Based Automatic Video Annotation Tool for Self-Driving Car
Authors: N. S. Manikandan, K. Ganesan
Date: 2019-04-19 00:00:00
Tasks: CPU, General Classification, GPU, Lane Detection, Object, object-detection, Object Detection, Object Tracking

In a self-driving car, objection detection, object classification, lane
detection and object tracking are considered to be the crucial modules. In
recent times, using the real time video one wants to narrate the scene captured
by the camera fitted in our vehicle. To effectively implement this task, deep
learning techniques and automatic video annotation tools are widely used. In
the present paper, we compare the various techniques that are available for
each module and choose the best algorithm among them by using appropriate
metrics. For object detection, YOLO and Retinanet-50 are considered and the
best one is chosen based on mean Average Precision (mAP). For object
classification, we consider VGG-19 and Resnet-50 and select the best algorithm
based on low error rate and good accuracy. For lane detection, Udacity's
'Finding Lane Line' and deep learning based LaneNet algorithms are compared and
the best one that can accurately identify the given lane is chosen for
implementation. As far as object tracking is concerned, we compare Udacity's
'Object Detection and Tracking' algorithm and deep learning based Deep Sort
algorithm. Based on the accuracy of tracking the same object in many frames and
predicting the movement of objects, the best algorithm is chosen. Our automatic
video annotation tool is found to be 83% accurate when compared with a human
annotator. We considered a video with 530 frames each of resolution 1035 x 1800
pixels. At an average each frame had about 15 objects. Our annotation tool
consumed 43 minutes in a CPU based system and 2.58 minutes in a mid-level GPU
based system to process all four modules. But the same video took nearly 3060
minutes for one human annotator to narrate the scene in the given video. Thus
we claim that our proposed automatic video annotation tool is reasonably fast
(about 1200 times in a GPU system) and accurate.
Multiple receptive fields and small-object-focusing weakly-supervised segmentation network for fast object detection
Authors: Siyang Sun, Yingjie Yin, Xingang Wang, De Xu, Yuan Zhao, Haifeng Shen
Date: 2019-04-19 00:00:00
Tasks: Object, object-detection, Object Detection, Real-Time Object Detection, Small Object Detection, Weakly supervised segmentation

Object detection plays an important role in various visual applications. However, the precision and speed of detector are usually contradictory. One main reason for fast detectors' precision reduction is that small objects are hard to be detected. To address this problem, we propose a multiple receptive field and small-object-focusing weakly-supervised segmentation network (MRFSWSnet) to achieve fast object detection. In MRFSWSnet, multiple receptive fields block (MRF) is used to pay attention to the object and its adjacent background's different spatial location with different weights to enhance the feature's discriminability. In addition, in order to improve the accuracy of small object detection, a small-object-focusing weakly-supervised segmentation module which only focuses on small object instead of all objects is integrated into the detection network for auxiliary training to improve the precision of small object detection. Extensive experiments show the effectiveness of our method on both PASCAL VOC and MS COCO detection datasets. In particular, with a lower resolution version of 300x300, MRFSWSnet achieves 80.9% mAP on VOC2007 test with an inference speed of 15 milliseconds per frame, which is the state-of-the-art detector among real-time detectors.
AnonymousNet: Natural Face De-Identification with Measurable Privacy
Authors: Tao Li, Lei Lin
Date: 2019-04-19 00:00:00
Tasks: Attribute, counterfactual, De-identification, Image Generation

With billions of personal images being generated from social media and
cameras of all sorts on a daily basis, security and privacy are unprecedentedly
challenged. Although extensive attempts have been made, existing face image
de-identification techniques are either insufficient in photo-reality or
incapable of balancing privacy and usability qualitatively and quantitatively,
i.e., they fail to answer counterfactual questions such as "is it private
now?", "how private is it?", and "can it be more private?" In this paper, we
propose a novel framework called AnonymousNet, with an effort to address these
issues systematically, balance usability, and enhance privacy in a natural and
measurable manner. The framework encompasses four stages: facial attribute
estimation, privacy-metric-oriented face obfuscation, directed natural image
synthesis, and adversarial perturbation. Not only do we achieve the
state-of-the-arts in terms of image quality and attribute prediction accuracy,
we are also the first to show that facial privacy is measurable, can be
factorized, and accordingly be manipulated in a photo-realistic fashion to
fulfill different requirements and application scenarios. Experiments further
demonstrate the effectiveness of the proposed framework.
Talk Proposal: Towards the Realistic Evaluation of Evasion Attacks using CARLA
Authors: Cory Cornelius, Shang-Tse Chen, Jason Martin, Duen Horng Chau
Date: 2019-04-18 00:00:00

In this talk we describe our content-preserving attack on object detectors,
ShapeShifter, and demonstrate how to evaluate this threat in realistic
scenarios. We describe how we use CARLA, a realistic urban driving simulator,
to create these scenarios, and how we use ShapeShifter to generate
content-preserving attacks against those scenarios.
Crowd Management in Open Spaces
Authors: Tauseef Ali, Ahmed B. Altamimi
Date: 2019-04-18 00:00:00
Tasks: Management

Crowd analysis and management is a challenging problem to ensure public
safety and security. For this purpose, many techniques have been proposed to
cope with various problems. However, the generalization capabilities of these
techniques is limited due to ignoring the fact that the density of crowd
changes from low to extreme high depending on the scene under observation. We
propose robust feature based approach to deal with the problem of crowd
management for people safety and security. We have evaluated our method using a
benchmark dataset and have presented details analysis.
Catch Me If You Can
Authors: Antoine Viscardi, Casey Juanxi Li, Thomas Hollis
Date: 2019-04-18 00:00:00
Tasks: Disentanglement

As advances in signature recognition have reached a new plateau of
performance at around 2% error rate, it is interesting to investigate
alternative approaches. The approach detailed in this paper looks at using
Variational Auto-Encoders (VAEs) to learn a latent space representation of
genuine signatures. This is then used to pass unlabelled signatures such that
only the genuine ones will successfully be reconstructed by the VAE. This
latent space representation and the reconstruction loss is subsequently used by
random forest and kNN classifiers for prediction. Subsequently, VAE
disentanglement and the possibility of posterior collapse are ascertained and
analysed. The final results suggest that while this method performs less well
than existing alternatives, further work may allow this to be used as part of
an ensemble for future models.
Computational Attention System for Children, Adults and Elderly
Authors: Onkar Krishna, Kiyoharu Aizawa, Go Irie
Date: 2019-04-18 00:00:00

The existing computational visual attention systems have focused on the
objective to basically simulate and understand the concept of visual attention
system in adults. Consequently, the impact of observer's age in scene viewing
behavior has rarely been considered. This study quantitatively analyzed the
age-related differences in gaze landings during scene viewing for three
different class of images: naturals, man-made, and fractals. Observer's of
different age-group have shown different scene viewing tendencies independent
to the class of the image viewed. Several interesting observations are drawn
from the results. First, gaze landings for man-made dataset showed that whereas
child observers focus more on the scene foreground, i.e., locations that are
near, elderly observers tend to explore the scene background, i.e., locations
farther in the scene. Considering this result a framework is proposed in this
paper to quantitatively measure the depth bias tendency across age groups.
Second, the quantitative analysis results showed that children exhibit the
lowest exploratory behavior level but the highest central bias tendency among
the age groups and across the different scene categories. Third,
inter-individual similarity metrics reveal that an adult had significantly
lower gaze consistency with children and elderly compared to other adults for
all the scene categories. Finally, these analysis results were consequently
leveraged to develop a more accurate age-adapted saliency model independent to
the image type. The prediction accuracy suggests that our model fits better to
the collected eye-gaze data of the observers belonging to different age groups
than the existing models do.
Beauty Learning and Counterfactual Inference
Authors: Tao Li
Date: 2019-04-24 00:00:00
Tasks: Causal Discovery, Causal Inference, counterfactual, Counterfactual Inference

This work showcases a new approach for causal discovery by leveraging user experiments and recent advances in photo-realistic image editing, demonstrating a potential of identifying causal factors and understanding complex systems counterfactually. We introduce the beauty learning problem as an example, which has been discussed metaphysically for centuries and been proved exists, is quantifiable, and can be learned by deep models in our recent paper, where we utilize a natural image generator coupled with user studies to infer causal effects from facial semantics to beauty outcomes, the results of which also align with existing empirical studies. We expect the proposed framework for a broader application in causal inference.
Detecting inter-sectional accuracy differences in driver drowsiness detection algorithms
Authors: Mkhuseli Ngxande, Jule-Raymond Tapamo, Michael Burke
Date: 2019-04-23 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection

Convolutional Neural Networks (CNNs) have been used successfully across a
broad range of areas including data mining, object detection, and in business.
The dominance of CNNs follows a breakthrough by Alex Krizhevsky which showed
improvements by dramatically reducing the error rate obtained in a general
image classification task from 26.2% to 15.4%. In road safety, CNNs have been
applied widely to the detection of traffic signs, obstacle detection, and lane
departure checking. In addition, CNNs have been used in data mining systems
that monitor driving patterns and recommend rest breaks when appropriate. This
paper presents a driver drowsiness detection system and shows that there are
potential social challenges regarding the application of these techniques, by
highlighting problems in detecting dark-skinned driver's faces. This is a
particularly important challenge in African contexts, where there are more
dark-skinned drivers. Unfortunately, publicly available datasets are often
captured in different cultural contexts, and therefore do not cover all
ethnicities, which can lead to false detections or racially biased models. This
work evaluates the performance obtained when training convolutional neural
network models on commonly used driver drowsiness detection datasets and
testing on datasets specifically chosen for broader representation. Results
show that models trained using publicly available datasets suffer extensively
from over-fitting, and can exhibit racial bias, as shown by testing on a more
representative dataset. We propose a novel visualisation technique that can
assist in identifying groups of people where there might be the potential of
discrimination, using Principal Component Analysis (PCA) to produce a grid of
faces sorted by similarity, and combining these with a model accuracy overlay.
Context-Aware Zero-Shot Learning for Object Recognition
Authors: Eloi Zablocki, Patrick Bordes, Benjamin Piwowarski, Laure Soulier, Patrick Gallinari
Date: 2019-04-24 00:00:00
Tasks: Object, Object Recognition, Zero-Shot Learning

Zero-Shot Learning (ZSL) aims at classifying unlabeled objects by leveraging
auxiliary knowledge, such as semantic representations. A limitation of previous
approaches is that only intrinsic properties of objects, e.g. their visual
appearance, are taken into account while their context, e.g. the surrounding
objects in the image, is ignored. Following the intuitive principle that
objects tend to be found in certain contexts but not others, we propose a new
and challenging approach, context-aware ZSL, that leverages semantic
representations in a new way to model the conditional likelihood of an object
to appear in a given context. Finally, through extensive experiments conducted
on Visual Genome, we show that contextual information can substantially improve
the standard ZSL approach and is robust to unbalanced classes.
Inner-Imaging Networks: Put Lenses into Convolutional Structure
Authors: Yang Hu, Guihua Wen, Mingnan Luo, Dan Dai, Wenming Cao, Zhiwen Yu, Wendy Hall
Date: 2019-04-22 00:00:00

Despite the tremendous success in computer vision, deep convolutional networks suffer from serious computation costs and redundancies. Although previous works address this issue by enhancing diversities of filters, they have not considered the complementarity and the completeness of the internal structure of the convolutional network. To deal with these problems, a novel Inner-Imaging architecture is proposed in this paper, which allows relationships between channels to meet the above requirement. Specifically, we organize the channel signal points in groups using convolutional kernels to model both the intra-group and inter-group relationships simultaneously. The convolutional filter is a powerful tool for modeling spatial relations and organizing grouped signals, so the proposed methods map the channel signals onto a pseudo-image, like putting a lens into convolution internal structure. Consequently, not only the diversity of channels is increased, but also the complementarity and completeness can be explicitly enhanced. The proposed architecture is lightweight and easy to be implemented. It provides an efficient self-organization strategy for convolutional networks so as to improve their efficiency and performance. Extensive experiments are conducted on multiple benchmark image recognition data sets including CIFAR, SVHN and ImageNet. Experimental results verify the effectiveness of the Inner-Imaging mechanism with the most popular convolutional networks as the backbones.
TextCohesion: Detecting Text for Arbitrary Shapes
Authors: Weijia Wu, Jici Xing, Hong Zhou
Date: 2019-04-22 00:00:00
Tasks: Curved Text Detection, Scene Text Detection, Text Detection

In this paper, we propose a pixel-wise method named TextCohesion for scene
text detection, which splits a text instance into five key components: a Text
Skeleton and four Directional Pixel Regions. These components are easier to
handle than the entire text instance. A confidence scoring mechanism is
designed to filter characters that are similar to text. Our method can
integrate text contexts intensively when backgrounds are complex. Experiments
on two curved challenging benchmarks demonstrate that TextCohesion outperforms
state-of-the-art methods, achieving the F-measure of 84.6% on Total-Text and
bfseries86.3% on SCUT-CTW1500.
MSDC-Net: Multi-Scale Dense and Contextual Networks for Automated Disparity Map for Stereo Matching
Authors: Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, Renjie He
Date: 2019-04-25 00:00:00
Tasks: Autonomous Driving, object-detection, Object Detection, Stereo Matching, Stereo Matching Hand

Disparity prediction from stereo images is essential to computer vision
applications including autonomous driving, 3D model reconstruction, and object
detection. To predict accurate disparity map, we propose a novel deep learning
architecture for detectingthe disparity map from a rectified pair of stereo
images, called MSDC-Net. Our MSDC-Net contains two modules: multi-scale fusion
2D convolution and multi-scale residual 3D convolution modules. The multi-scale
fusion 2D convolution module exploits the potential multi-scale features, which
extracts and fuses the different scale features by Dense-Net. The multi-scale
residual 3D convolution module learns the different scale geometry context from
the cost volume which aggregated by the multi-scale fusion 2D convolution
module. Experimental results on Scene Flow and KITTI datasets demonstrate that
our MSDC-Net significantly outperforms other approaches in the non-occluded
region.
PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras
Authors: Bharath Ramesh, Andres Ussa, Luca Della Vedova, Hong Yang, Garrick Orchard
Date: 2019-04-24 00:00:00
Tasks: Dimensionality Reduction, feature selection, Object, Object Categorization, object-detection, Object Detection, Object Recognition

We present the first purely event-based, energy-efficient approach for object
detection and categorization using an event camera. Compared to traditional
frame-based cameras, choosing event cameras results in high temporal resolution
(order of microseconds), low power consumption (few hundred mW) and wide
dynamic range (120 dB) as attractive properties. However, event-based object
recognition systems are far behind their frame-based counterparts in terms of
accuracy. To this end, this paper presents an event-based feature extraction
method devised by accumulating local activity across the image frame and then
applying principal component analysis (PCA) to the normalized neighborhood
region. Subsequently, we propose a backtracking-free k-d tree mechanism for
efficient feature matching by taking advantage of the low-dimensionality of the
feature representation. Additionally, the proposed k-d tree mechanism allows
for feature selection to obtain a lower-dimensional dictionary representation
when hardware resources are limited to implement dimensionality reduction.
Consequently, the proposed system can be realized on a field-programmable gate
array (FPGA) device leading to high performance over resource ratio. The
proposed system is tested on real-world event-based datasets for object
categorization, showing superior classification performance and relevance to
state-of-the-art algorithms. Additionally, we verified the object detection
method and real-time FPGA performance in lab settings under non-controlled
illumination conditions with limited training data and ground truth
annotations.
Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss
Authors: Mhd Hasan Sarhan, Shadi Albarqouni, Mehmet Yigitsoy, Nassir Navab, Abouzar Eslami
Date: 2019-04-18 00:00:00
Tasks: Classification, Deep Learning, Diabetic Retinopathy Detection, General Classification, Segmentation, Triplet

Deep learning techniques are recently being used in fundus image analysis and diabetic retinopathy detection. Microaneurysms are an important indicator of diabetic retinopathy progression. We introduce a two-stage deep learning approach for microaneurysms segmentation using multiple scales of the input with selective sampling and embedding triplet loss. The model first segments on two scales and then the segmentations are refined with a classification model. To enhance the discriminative power of the classification model, we incorporate triplet embedding loss with a selective sampling routine. The model is evaluated quantitatively to assess the segmentation performance and qualitatively to analyze the model predictions. This approach introduces a 30.29% relative improvement over the fully convolutional neural network.
Registration of retinal images from Public Health by minimising an error between vessels using an affine model with radial distortions
Authors: Guillaume Noyel, R. Thomas, S Iles, G Bhakta, A Crowder, D. Owens, P. Boyle
Date: 2019-04-17 00:00:00

In order to estimate a registration model of eye fundus images made of an
affinity and two radial distortions, we introduce an estimation criterion based
on an error between the vessels. In [1], we estimated this model by minimising
the error between characteristics points. In this paper, the detected vessels
are selected using the circle and ellipse equations of the overlap area
boundaries deduced from our model. Our method successfully registers 96 % of
the 271 pairs in a Public Health dataset acquired mostly with different
cameras. This is better than our previous method [1] and better than three
other state-of-the-art methods. On a publicly available dataset, ours still
better register the images than the reference method.