pwc-archive/papers-with-abstracts huggingface dataset:
Comparison of Possibilistic Fuzzy Local Information C-Means and Possibilistic K-Nearest Neighbors for Synthetic Aperture Sonar Image Segmentation
Authors: Joshua Peeples, Matthew Cook, Daniel Suen, Alina Zare, James Keller
Date: 2019-04-01 00:00:00
Tasks: Image Segmentation, Landmine, Segmentation, Semantic Segmentation

Synthetic aperture sonar (SAS) imagery can generate high resolution images of
the seafloor. Thus, segmentation algorithms can be used to partition the images
into different seafloor environments. In this paper, we compare two
possibilistic segmentation approaches. Possibilistic approaches allow for the
ability to detect novel or outlier environments as well as well known classes.
The Possibilistic Fuzzy Local Information C-Means (PFLICM) algorithm has been
previously applied to segment SAS imagery. Additionally, the Possibilistic
K-Nearest Neighbors (PKNN) algorithm has been used in other domains such as
landmine detection and hyperspectral imagery. In this paper, we compare the
segmentation performance of a semi-supervised approach using PFLICM and a
supervised method using Possibilistic K-NN. We include final segmentation
results on multiple SAS images and a quantitative assessment of each algorithm.
Air Taxi Skyport Location Problem for Airport Access
Authors: Srushti Rath, Joseph Y. J. Chow
Date: 2019-04-01 00:00:00
Tasks: Clustering

Witnessing the rapid progress and accelerated commercialization made in recent years for the introduction of air taxi services in near future across metropolitan cities, our research focuses on one of the most important consideration for such services, i.e., infrastructure planning (also known as skyports). We consider design of skyport locations for air taxis accessing airports, where we present the skyport location problem as a modified single-allocation p-hub median location problem integrating choice-constrained user mode choice behavior into the decision process. Our approach focuses on two alternative objectives i.e., maximizing air taxi ridership and maximizing air taxi revenue. The proposed models in the study incorporate trade-offs between trip length and trip cost based on mode choice behavior of travelers to determine optimal choices of skyports in an urban city. We examine the sensitivity of skyport locations based on two objectives, three air taxi pricing strategies, and varying transfer times at skyports. A case study of New York City is conducted considering a network of 149 taxi zones and 3 airports with over 20 million for-hire-vehicles trip data to the airports to discuss insights around the choice of skyport locations in the city, and demand allocation to different skyports under various parameter settings. Results suggest that a minimum of 9 skyports located between Manhattan, Queens and Brooklyn can adequately accommodate the airport access travel needs and are sufficiently stable against transfer time increases. Findings from this study can help air taxi providers strategize infrastructure design options and investment decisions based on skyport location choices.
On the Vulnerability of CNN Classifiers in EEG-Based BCIs
Authors: Xiao Zhang, Dongrui Wu
Date: 2019-03-31 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Feature Engineering

Deep learning has been successfully used in numerous applications because of
its outstanding performance and the ability to avoid manual feature
engineering. One such application is electroencephalogram (EEG) based
brain-computer interface (BCI), where multiple convolutional neural network
(CNN) models have been proposed for EEG classification. However, it has been
found that deep learning models can be easily fooled with adversarial examples,
which are normal examples with small deliberate perturbations. This paper
proposes an unsupervised fast gradient sign method (UFGSM) to attack three
popular CNN classifiers in BCIs, and demonstrates its effectiveness. We also
verify the transferability of adversarial examples in BCIs, which means we can
perform attacks even without knowing the architecture and parameters of the
target models, or the datasets they were trained on. To our knowledge, this is
the first study on the vulnerability of CNN classifiers in EEG-based BCIs, and
hopefully will trigger more attention on the security of BCI systems.
Spontaneous Facial Micro-Expression Recognition using 3D Spatiotemporal Convolutional Neural Networks
Authors: Sai Prasanna Teja Reddy, Surya Teja Karri, Shiv Ram Dubey, Snehasis Mukherjee
Date: 2019-03-27 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Micro Expression Recognition, Micro-Expression Recognition

Facial expression recognition in videos is an active area of research in
computer vision. However, fake facial expressions are difficult to be
recognized even by humans. On the other hand, facial micro-expressions
generally represent the actual emotion of a person, as it is a spontaneous
reaction expressed through human face. Despite of a few attempts made for
recognizing micro-expressions, still the problem is far from being a solved
problem, which is depicted by the poor rate of accuracy shown by the
state-of-the-art methods. A few CNN based approaches are found in the
literature to recognize micro-facial expressions from still images. Whereas, a
spontaneous micro-expression video contains multiple frames that have to be
processed together to encode both spatial and temporal information. This paper
proposes two 3D-CNN methods: MicroExpSTCNN and MicroExpFuseNet, for spontaneous
facial micro-expression recognition by exploiting the spatiotemporal
information in CNN framework. The MicroExpSTCNN considers the full spatial
information, whereas the MicroExpFuseNet is based on the 3D-CNN feature fusion
of the eyes and mouth regions. The experiments are performed over CAS(ME)^2 and
SMIC micro-expression databases. The proposed MicroExpSTCNN model outperforms
the state-of-the-art methods.
Detection of LDDoS Attacks Based on TCP Connection Parameters
Authors: Michael Siracusano, Stavros Shiaeles, Bogdan Ghita
Date: 2019-03-12 00:00:00

Low-rate application layer distributed denial of service (LDDoS) attacks are
both powerful and stealthy. They force vulnerable webservers to open all
available connections to the adversary, denying resources to real users.
Mitigation advice focuses on solutions that potentially degrade quality of
service for legitimate connections. Furthermore, without accurate detection
mechanisms, distributed attacks can bypass these defences. A methodology for
detection of LDDoS attacks, based on characteristics of malicious TCP flows, is
proposed within this paper. Research will be conducted using combinations of
two datasets: one generated from a simulated network, the other from the
publically available CIC DoS dataset. Both contain the attacks slowread,
slowheaders and slowbody, alongside legitimate web browsing. TCP flow features
are extracted from all connections. Experimentation was carried out using six
supervised AI algorithms to categorise attack from legitimate flows. Decision
trees and k-NN accurately classified up to 99.99% of flows, with exceptionally
low false positive and false negative rates, demonstrating the potential of AI
in LDDoS detection.
Normalized Information Distance
Authors: Paul M. B. Vitanyi, Frank J. Balbach, Rudi L. Cilibrasi, Ming Li
Date: 2008-09-15 00:00:00
Tasks: Clustering, Machine Translation, Translation

The normalized information distance is a universal distance measure for
objects of all kinds. It is based on Kolmogorov complexity and thus
uncomputable, but there are ways to utilize it. First, compression algorithms
can be used to approximate the Kolmogorov complexity if the objects have a
string representation. Second, for names and abstract concepts, page count
statistics from the World Wide Web can be used. These practical realizations of
the normalized information distance can then be applied to machine learning
tasks, expecially clustering, to perform feature-free and parameter-free data
mining. This chapter discusses the theoretical foundations of the normalized
information distance and both practical realizations. It presents numerous
examples of successful real-world applications based on these distance
measures, ranging from bioinformatics to music clustering to machine
translation.
Unsupervised Deep Tracking
Authors: Ning Wang, Yibing Song, Chao Ma, Wengang Zhou, Wei Liu, Houqiang Li
Date: 2019-04-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Visual Tracking

We propose an unsupervised visual tracking method in this paper. Different
from existing approaches using extensive annotated data for supervised
learning, our CNN model is trained on large-scale unlabeled videos in an
unsupervised manner. Our motivation is that a robust tracker should be
effective in both the forward and backward predictions (i.e., the tracker can
forward localize the target object in successive frames and backtrace to its
initial position in the first frame). We build our framework on a Siamese
correlation filter network, which is trained using unlabeled raw videos.
Meanwhile, we propose a multiple-frame validation method and a cost-sensitive
loss to facilitate unsupervised learning. Without bells and whistles, the
proposed unsupervised tracker achieves the baseline accuracy of fully
supervised trackers, which require complete and accurate labels during
training. Furthermore, unsupervised framework exhibits a potential in
leveraging unlabeled or weakly labeled data to further improve the tracking
accuracy.
Target-Aware Deep Tracking
Authors: Xin Li, Chao Ma, Baoyuan Wu, Zhenyu He, Ming-Hsuan Yang
Date: 2019-04-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object, Object Recognition, Visual Tracking

Existing deep trackers mainly use convolutional neural networks pre-trained
for generic object recognition task for representations. Despite demonstrated
successes for numerous vision tasks, the contributions of using pre-trained
deep features for visual tracking are not as significant as that for object
recognition. The key issue is that in visual tracking the targets of interest
can be arbitrary object class with arbitrary forms. As such, pre-trained deep
features are less effective in modeling these targets of arbitrary forms for
distinguishing them from the background. In this paper, we propose a novel
scheme to learn target-aware features, which can better recognize the targets
undergoing significant appearance variations than pre-trained deep features. To
this end, we develop a regression loss and a ranking loss to guide the
generation of target-active and scale-sensitive features. We identify the
importance of each convolutional filter according to the back-propagated
gradients and select the target-aware features based on activations for
representing the targets. The target-aware features are integrated with a
Siamese matching network for visual tracking. Extensive experimental results
show that the proposed algorithm performs favorably against the
state-of-the-art methods in terms of accuracy and speed.
CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysis
Authors: Ahti Kalervo, Juha Ylioinas, Markus Häikiö, Antti Karhu, Juho Kannala
Date: 2019-04-03 00:00:00

Better understanding and modelling of building interiors and the emergence of
more impressive AR/VR technology has brought up the need for automatic parsing
of floorplan images. However, there is a clear lack of representative datasets
to investigate the problem further. To address this shortcoming, this paper
presents a novel image dataset called CubiCasa5K, a large-scale floorplan image
dataset containing 5000 samples annotated into over 80 floorplan object
categories. The dataset annotations are performed in a dense and versatile
manner by using polygons for separating the different objects. Diverging from
the classical approaches based on strong heuristics and low-level pixel
operations, we present a method relying on an improved multi-task convolutional
neural network. By releasing the novel dataset and our implementations, this
study significantly boosts the research on automatic floorplan image analysis
as it provides a richer set of tools for investigating the problem in a more
comprehensive manner.
Activity Driven Weakly Supervised Object Detection
Authors: Zhenheng Yang, Dhruv Mahajan, Deepti Ghadiyaram, Ram Nevatia, Vignesh Ramanathan
Date: 2019-04-02 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Classification, Object, object-detection, Object Detection, Weakly Supervised Object Detection

Weakly supervised object detection aims at reducing the amount of supervision
required to train detection models. Such models are traditionally learned from
images/videos labelled only with the object class and not the object bounding
box. In our work, we try to leverage not only the object class labels but also
the action labels associated with the data. We show that the action depicted in
the image/video can provide strong cues about the location of the associated
object. We learn a spatial prior for the object dependent on the action (e.g.
"ball" is closer to "leg of the person" in "kicking ball"), and incorporate
this prior to simultaneously train a joint object detection and action
classification model. We conducted experiments on both video datasets and image
datasets to evaluate the performance of our weakly supervised object detection
model. Our approach outperformed the current state-of-the-art (SOTA) method by
more than 6% in mAP on the Charades video dataset.
Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction
Authors: Jason Ku, Alex D. Pon, Steven L. Waslander
Date: 2019-04-02 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Object Detection, Monocular 3D Object Detection, Object, object-detection, Object Detection, Vehicle Pose Estimation

We present MonoPSR, a monocular 3D object detection method that leverages
proposals and shape reconstruction. First, using the fundamental relations of a
pinhole camera model, detections from a mature 2D object detector are used to
generate a 3D proposal per object in a scene. The 3D location of these
proposals prove to be quite accurate, which greatly reduces the difficulty of
regressing the final 3D bounding box detection. Simultaneously, a point cloud
is predicted in an object centered coordinate system to learn local scale and
shape information. However, the key challenge is how to exploit shape
information to guide 3D localization. As such, we devise aggregate losses,
including a novel projection alignment loss, to jointly optimize these tasks in
the neural network to improve 3D localization accuracy. We validate our method
on the KITTI benchmark where we set new state-of-the-art results among
published monocular methods, including the harder pedestrian and cyclist
classes, while maintaining efficient run-time.
Conditional Adversarial Generative Flow for Controllable Image Synthesis
Authors: Rui Liu, Yu Liu, Xinyu Gong, Xiaogang Wang, Hongsheng Li
Date: 2019-04-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Generation

Flow-based generative models show great potential in image synthesis due to
its reversible pipeline and exact log-likelihood target, yet it suffers from
weak ability for conditional image synthesis, especially for multi-label or
unaware conditions. This is because the potential distribution of image
conditions is hard to measure precisely from its latent variable $z$. In this
paper, based on modeling a joint probabilistic density of an image and its
conditions, we propose a novel flow-based generative model named conditional
adversarial generative flow (CAGlow). Instead of disentangling attributes from
latent space, we blaze a new trail for learning an encoder to estimate the
mapping from condition space to latent space in an adversarial manner. Given a
specific condition $c$, CAGlow can encode it to a sampled $z$, and then enable
robust conditional image synthesis in complex situations like combining person
identity with multiple attributes. The proposed CAGlow can be implemented in
both supervised and unsupervised manners, thus can synthesize images with
conditional information like categories, attributes, and even some unknown
properties. Extensive experiments show that CAGlow ensures the independence of
different conditions and outperforms regular Glow to a significant extent.
SFNet: Learning Object-aware Semantic Correspondence
Authors: Junghyup Lee, Dohyung Kim, Jean Ponce, Bumsub Ham
Date: 2019-04-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object, Semantic correspondence

We address the problem of semantic correspondence, that is, establishing a
dense flow field between images depicting different instances of the same
object or scene category. We propose to use images annotated with binary
foreground masks and subjected to synthetic geometric deformations to train a
convolutional neural network (CNN) for this task. Using these masks as part of
the supervisory signal offers a good compromise between semantic flow methods,
where the amount of training data is limited by the cost of manually selecting
point correspondences, and semantic alignment ones, where the regression of a
single global geometric transformation between images may be sensitive to
image-specific details such as background clutter. We propose a new CNN
architecture, dubbed SFNet, which implements this idea. It leverages a new and
differentiable version of the argmax function for end-to-end training, with a
loss that combines mask and flow consistency with smoothness terms.
Experimental results demonstrate the effectiveness of our approach, which
significantly outperforms the state of the art on standard benchmarks.
Learning Context Graph for Person Search
Authors: Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang
Date: 2019-04-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Graph Learning, Person Re-Identification, Person Search

Person re-identification has achieved great progress with deep convolutional
neural networks. However, most previous methods focus on learning individual
appearance feature embedding, and it is hard for the models to handle difficult
situations with different illumination, large pose variance and occlusion. In
this work, we take a step further and consider employing context information
for person search. For a probe-gallery pair, we first propose a contextual
instance expansion module, which employs a relative attention module to search
and filter useful context information in the scene. We also build a graph
learning framework to effectively employ context pairs to update target
similarity. These two modules are built on top of a joint detection and
instance feature learning framework, which improves the discriminativeness of
the learned features. The proposed framework achieves state-of-the-art
performance on two widely used person search datasets.
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation
Authors: Shanshan Zhao, Huan Fu, Mingming Gong, DaCheng Tao
Date: 2019-04-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Depth Estimation, Depth Prediction, Domain Adaptation, Monocular Depth Estimation, Style Transfer

Supervised depth estimation has achieved high accuracy due to the advanced
deep network architectures. Since the groundtruth depth labels are hard to
obtain, recent methods try to learn depth estimation networks in an
unsupervised way by exploring unsupervised cues, which are effective but less
reliable than true labels. An emerging way to resolve this dilemma is to
transfer knowledge from synthetic images with ground truth depth via domain
adaptation techniques. However, these approaches overlook specific geometric
structure of the natural images in the target domain (i.e., real data), which
is important for high-performing depth prediction. Motivated by the
observation, we propose a geometry-aware symmetric domain adaptation framework
(GASDA) to explore the labels in the synthetic data and epipolar geometry in
the real data jointly. Moreover, by training two image style translators and
depth estimators symmetrically in an end-to-end network, our model achieves
better image style transfer and generates high-quality depth maps. The
experimental results demonstrate the effectiveness of our proposed method and
comparable performance against the state-of-the-art. Code will be publicly
available at: https://github.com/sshan-zhao/GASDA.
Beyond Tracking: Selecting Memory and Refining Poses for Deep Visual Odometry
Authors: Fei Xue, Xin Wang, Shunkai Li, Qiuyuan Wang, Junqiu Wang, Hongbin Zha
Date: 2019-04-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Visual Odometry

Most previous learning-based visual odometry (VO) methods take VO as a pure
tracking problem. In contrast, we present a VO framework by incorporating two
additional components called Memory and Refining. The Memory component
preserves global information by employing an adaptive and efficient selection
strategy. The Refining component ameliorates previous results with the contexts
stored in the Memory by adopting a spatial-temporal attention mechanism for
feature distilling. Experiments on the KITTI and TUM-RGBD benchmark datasets
demonstrate that our method outperforms state-of-the-art learning-based methods
by a large margin and produces competitive results against classic monocular VO
approaches. Especially, our model achieves outstanding performance in
challenging scenarios such as texture-less regions and abrupt motions, where
classic VO algorithms tend to fail.
Character Region Awareness for Text Detection
Authors: Youngmin Baek, Bado Lee, Dongyoon Han, Sangdoo Yun, Hwalsuk Lee
Date: 2019-04-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Scene Text Detection, Text Detection

Scene text detection methods based on neural networks have emerged recently
and have shown promising results. Previous methods trained with rigid
word-level bounding boxes exhibit limitations in representing the text region
in an arbitrary shape. In this paper, we propose a new scene text detection
method to effectively detect text area by exploring each character and affinity
between characters. To overcome the lack of individual character level
annotations, our proposed framework exploits both the given character-level
annotations for synthetic images and the estimated character-level
ground-truths for real images acquired by the learned interim model. In order
to estimate affinity between characters, the network is trained with the newly
proposed representation for affinity. Extensive experiments on six benchmarks,
including the TotalText and CTW-1500 datasets which contain highly curved texts
in natural images, demonstrate that our character-level text detection
significantly outperforms the state-of-the-art detectors. According to the
results, our proposed method guarantees high flexibility in detecting
complicated scene text images, such as arbitrarily-oriented, curved, or
deformed texts.