pwc-archive/papers-with-abstracts huggingface dataset:
Depth Coefficients for Depth Completion
Authors: Saif Imran, Yunfei Long, Xiaoming Liu, Daniel Morris
Date: 2019-03-13 00:00:00
Proceeding: CVPR 2019 6
Tasks: Depth Completion, object-detection, Object Detection

Depth completion involves estimating a dense depth image from sparse depth
measurements, often guided by a color image. While linear upsampling is
straight forward, it results in artifacts including depth pixels being
interpolated in empty space across discontinuities between objects. Current
methods use deep networks to upsample and "complete" the missing depth pixels.
Nevertheless, depth smearing between objects remains a challenge. We propose a
new representation for depth called Depth Coefficients (DC) to address this
problem. It enables convolutions to more easily avoid inter-object depth
mixing. We also show that the standard Mean Squared Error (MSE) loss function
can promote depth mixing, and thus propose instead to use cross-entropy loss
for DC. With quantitative and qualitative evaluation on benchmarks, we show
that switching out sparse depth input and MSE loss with our DC representation
and cross-entropy loss is a simple way to improve depth completion performance,
and reduce pixel depth mixing, which leads to improved depth-based object
detection.
Visual Semantic Information Pursuit: A Survey
Authors: Daqi Liu, Miroslaw Bober, Josef Kittler
Date: 2019-03-13 00:00:00
Tasks: Graph Generation, object-detection, Object Detection, Relationship Detection, Scene Graph Generation, Semantic Segmentation, Survey, Visual Relationship Detection

Visual semantic information comprises two important parts: the meaning of
each visual semantic unit and the coherent visual semantic relation conveyed by
these visual semantic units. Essentially, the former one is a visual perception
task while the latter one corresponds to visual context reasoning. Remarkable
advances in visual perception have been achieved due to the success of deep
learning. In contrast, visual semantic information pursuit, a visual scene
semantic interpretation task combining visual perception and visual context
reasoning, is still in its early stage. It is the core task of many different
computer vision applications, such as object detection, visual semantic
segmentation, visual relationship detection or scene graph generation. Since it
helps to enhance the accuracy and the consistency of the resulting
interpretation, visual context reasoning is often incorporated with visual
perception in current deep end-to-end visual semantic information pursuit
methods. However, a comprehensive review for this exciting area is still
lacking. In this survey, we present a unified theoretical paradigm for all
these methods, followed by an overview of the major developments and the future
trends in each potential direction. The common benchmark datasets, the
evaluation metrics and the comparisons of the corresponding methods are also
introduced.
Towards Accurate Camera Geopositioning by Image Matching
Authors: Raffaele Imbriaco, Clint Sebastian, Egor Bondarev, Peter de With
Date: 2019-03-13 00:00:00
Tasks: Clustering, Position

In this work, we present a camera geopositioning system based on matching a
query image against a database with panoramic images. For matching, our system
uses memory vectors aggregated from global image descriptors based on
convolutional features to facilitate fast searching in the database. To speed
up searching, a clustering algorithm is used to balance geographical
positioning and computation time. We refine the obtained position from the
query image using a new outlier removal algorithm. The matching of the query
image is obtained with a recall@5 larger than 90% for panorama-to-panorama
matching. We cluster available panoramas from geographically adjacent locations
into a single compact representation and observe computational gains of
approximately 50% at the cost of only a small (approximately 3%) recall loss.
Finally, we present a coordinate estimation algorithm that reduces the median
geopositioning error by up to 20%.
Connection Sensitive Attention U-NET for Accurate Retinal Vessel Segmentation
Authors: Ruirui Li, Mingming Li, Jiacheng Li, Yating Zhou
Date: 2019-03-13 00:00:00
Tasks: Retinal Vessel Segmentation, Segmentation, Semantic Segmentation

We develop a connection sensitive attention U-Net(CSAU) for accurate retinal
vessel segmentation. This method improves the recent attention U-Net for
semantic segmentation with four key improvements: (1) connection sensitive loss
that models the structure properties to improve the accuracy of pixel-wise
segmentation; (2) attention gate with novel neural network structure and
concatenating DOWN-Link to effectively learn better attention weights on fine
vessels; (3) integration of connection sensitive loss and attention gate to
further improve the accuracy on detailed vessels by additionally concatenating
attention weights to features before output; (4) metrics of connection
sensitive accuracy to reflect the segmentation performance on boundaries and
thin vessels.
  Our method can effectively improve state-of-the-art vessel segmentation
methods that suffer from difficulties in presence of abnormalities, bifurcation
and microvascular. This connection sensitive loss tightly integrates with the
proposed attention U-Net to accurately (i) segment retinal vessels, and (ii)
reserve the connectivity of thin vessels by modeling the structural properties.
Our method achieves the leading position on DRIVE, STARE and HRF datasets among
the state-of-the-art methods.
Privacy Preserving Image-Based Localization
Authors: Pablo Speciale, Johannes L. Schönberger, Sing Bing Kang, Sudipta N. Sinha, Marc Pollefeys
Date: 2019-03-13 00:00:00
Proceeding: CVPR 2019 6
Tasks: Camera Pose Estimation, Image-Based Localization, Mixed Reality, Pose Estimation, Privacy Preserving

Image-based localization is a core component of many augmented/mixed reality
(AR/MR) and autonomous robotic systems. Current localization systems rely on
the persistent storage of 3D point clouds of the scene to enable camera pose
estimation, but such data reveals potentially sensitive scene information. This
gives rise to significant privacy risks, especially as for many applications 3D
mapping is a background process that the user might not be fully aware of. We
pose the following question: How can we avoid disclosing confidential
information about the captured 3D scene, and yet allow reliable camera pose
estimation? This paper proposes the first solution to what we call privacy
preserving image-based localization. The key idea of our approach is to lift
the map representation from a 3D point cloud to a 3D line cloud. This novel
representation obfuscates the underlying scene geometry while providing
sufficient geometric constraints to enable robust and accurate 6-DOF camera
pose estimation. Extensive experiments on several datasets and localization
scenarios underline the high practical relevance of our proposed approach.
Two-Stream Action Recognition-Oriented Video Super-Resolution
Authors: Haochen Zhang, Dong Liu, Zhiwei Xiong
Date: 2019-03-13 00:00:00
Proceeding: ICCV 2019 10
Tasks: Action Recognition, Optical Flow Estimation, Super-Resolution, Temporal Action Localization, Video Super-Resolution, Vocal Bursts Valence Prediction

We study the video super-resolution (SR) problem for facilitating video analytics tasks, e.g. action recognition, instead of for visual quality. The popular action recognition methods based on convolutional networks, exemplified by two-stream networks, are not directly applicable on video of low spatial resolution. This can be remedied by performing video SR prior to recognition, which motivates us to improve the SR procedure for recognition accuracy. Tailored for two-stream action recognition networks, we propose two video SR methods for the spatial and temporal streams respectively. On the one hand, we observe that regions with action are more important to recognition, and we propose an optical-flow guided weighted mean-squared-error loss for our spatial-oriented SR (SoSR) network to emphasize the reconstruction of moving objects. On the other hand, we observe that existing video SR methods incur temporal discontinuity between frames, which also worsens the recognition accuracy, and we propose a siamese network for our temporal-oriented SR (ToSR) training that emphasizes the temporal continuity between consecutive frames. We perform experiments using two state-of-the-art action recognition networks and two well-known datasets--UCF101 and HMDB51. Results demonstrate the effectiveness of our proposed SoSR and ToSR in improving recognition accuracy.
Hyperspectral Data Augmentation
Authors: Jakub Nalepa, Michal Myller, Michal Kawulok
Date: 2019-03-13 00:00:00
Tasks: Data Augmentation

Data augmentation is a popular technique which helps improve generalization
capabilities of deep neural networks. It plays a pivotal role in remote-sensing
scenarios in which the amount of high-quality ground truth data is limited, and
acquiring new examples is costly or impossible. This is a common problem in
hyperspectral imaging, where manual annotation of image data is difficult,
expensive, and prone to human bias. In this letter, we propose online data
augmentation of hyperspectral data which is executed during the inference
rather than before the training of deep networks. This is in contrast to all
other state-of-the-art hyperspectral augmentation algorithms which increase the
size (and representativeness) of training sets. Additionally, we introduce a
new principal component analysis based augmentation. The experiments revealed
that our data augmentation algorithms improve generalization of deep networks,
work in real-time, and the online approach can be effectively combined with
offline techniques to enhance the classification accuracy.
LiDAR-assisted Large-scale Privacy Protection in Street-view Cycloramas
Authors: Clint Sebastian, Bas Boom, Egor Bondarev, Peter H. N. de With
Date: 2019-03-13 00:00:00

Recently, privacy has a growing importance in several domains, especially in
street-view images. The conventional way to achieve this is to automatically
detect and blur sensitive information from these images. However, the
processing cost of blurring increases with the ever-growing resolution of
images. We propose a system that is cost-effective even after increasing the
resolution by a factor of 2.5. The new system utilizes depth data obtained from
LiDAR to significantly reduce the search space for detection, thereby reducing
the processing cost. Besides this, we test several detectors after reducing the
detection space and provide an alternative solution based on state-of-the-art
deep learning detectors to the existing HoG-SVM-Deep system that is faster and
has a higher performance.
RVOS: End-to-End Recurrent Network for Video Object Segmentation
Authors: Carles Ventura, Miriam Bellver, Andreu Girbau, Amaia Salvador, Ferran Marques, Xavier Giro-i-Nieto
Date: 2019-03-13 00:00:00
Proceeding: CVPR 2019 6
Tasks: GPU, Object, One-shot visual object segmentation, Segmentation, Semi-Supervised Video Object Segmentation, Unsupervised Video Object Segmentation, Video Object Segmentation, Zero-Shot Video Object Segmentation

Multiple object video object segmentation is a challenging task, specially for the zero-shot case, when no object mask is given at the initial frame and the model has to find the objects to be segmented along the sequence. In our work, we propose a Recurrent network for multiple object Video Object Segmentation (RVOS) that is fully end-to-end trainable. Our model incorporates recurrence on two different domains: (i) the spatial, which allows to discover the different object instances within a frame, and (ii) the temporal, which allows to keep the coherence of the segmented objects along time. We train RVOS for zero-shot video object segmentation and are the first ones to report quantitative results for DAVIS-2017 and YouTube-VOS benchmarks. Further, we adapt RVOS for one-shot video object segmentation by using the masks obtained in previous time steps as inputs to be processed by the recurrent module. Our model reaches comparable results to state-of-the-art techniques in YouTube-VOS benchmark and outperforms all previous video object segmentation methods not using online learning in the DAVIS-2017 benchmark. Moreover, our model achieves faster inference runtimes than previous methods, reaching 44ms/frame on a P100 GPU.
Tracking without bells and whistles
Authors: Philipp Bergmann, Tim Meinhardt, Laura Leal-Taixe
Date: 2019-03-13 00:00:00
Proceeding: ICCV 2019 10
Tasks: Motion Compensation, motion prediction, Multi-Object Tracking, Object, Object Tracking

The problem of tracking multiple objects in a video sequence poses several challenging tasks. For tracking-by-detection, these include object re-identification, motion prediction and dealing with occlusions. We present a tracker (without bells and whistles) that accomplishes tracking without specifically targeting any of these tasks, in particular, we perform no training or optimization on tracking data. To this end, we exploit the bounding box regression of an object detector to predict the position of an object in the next frame, thereby converting a detector into a Tracktor. We demonstrate the potential of Tracktor and provide a new state-of-the-art on three multi-object tracking benchmarks by extending it with a straightforward re-identification and camera motion compensation. We then perform an analysis on the performance and failure cases of several state-of-the-art tracking methods in comparison to our Tracktor. Surprisingly, none of the dedicated tracking methods are considerably better in dealing with complex tracking scenarios, namely, small and occluded objects or missing detections. However, our approach tackles most of the easy tracking scenarios. Therefore, we motivate our approach as a new tracking paradigm and point out promising future research directions. Overall, Tracktor yields superior tracking performance than any current tracking method and our analysis exposes remaining and unsolved tracking challenges to inspire future research directions.
Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
Authors: Qi Mao, Hsin-Ying Lee, Hung-Yu Tseng, Siwei Ma, Ming-Hsuan Yang
Date: 2019-03-13 00:00:00
Proceeding: CVPR 2019 6
Tasks: Diversity, Image Generation, Image-to-Image Translation, Multimodal Unsupervised Image-To-Image Translation, Translation

Most conditional generation tasks expect diverse outputs given a single conditional context. However, conditional generative adversarial networks (cGANs) often focus on the prior conditional information and ignore the input noise vectors, which contribute to the output variations. Recent attempts to resolve the mode collapse issue for cGANs are usually task-specific and computationally expensive. In this work, we propose a simple yet effective regularization term to address the mode collapse issue for cGANs. The proposed method explicitly maximizes the ratio of the distance between generated images with respect to the corresponding latent codes, thus encouraging the generators to explore more minor modes during training. This mode seeking regularization term is readily applicable to various conditional generation tasks without imposing training overhead or modifying the original network structures. We validate the proposed algorithm on three conditional image synthesis tasks including categorical generation, image-to-image translation, and text-to-image synthesis with different baseline models. Both qualitative and quantitative results demonstrate the effectiveness of the proposed regularization method for improving diversity without loss of quality.
A Visually Plausible Grasping System for Object Manipulation and Interaction in Virtual Reality Environments
Authors: Sergiu Oprea, Pablo Martinez-Gonzalez, Alberto Garcia-Garcia, John Alejandro Castro-Vargas, Sergio Orts-Escolano, Jose Garcia-Rodriguez
Date: 2019-03-12 00:00:00
Tasks: Object

Interaction in virtual reality (VR) environments is essential to achieve a
pleasant and immersive experience. Most of the currently existing VR
applications, lack of robust object grasping and manipulation, which are the
cornerstone of interactive systems. Therefore, we propose a realistic, flexible
and robust grasping system that enables rich and real-time interactions in
virtual environments. It is visually realistic because it is completely
user-controlled, flexible because it can be used for different hand
configurations, and robust because it allows the manipulation of objects
regardless their geometry, i.e. hand is automatically fitted to the object
shape. In order to validate our proposal, an exhaustive qualitative and
quantitative performance analysis has been carried out. On the one hand,
qualitative evaluation was used in the assessment of the abstract aspects such
as: hand movement realism, interaction realism and motor control. On the other
hand, for the quantitative evaluation a novel error metric has been proposed to
visually analyze the performed grips. This metric is based on the computation
of the distance from the finger phalanges to the nearest contact point on the
object surface. These contact points can be used with different application
purposes, mainly in the field of robotics. As a conclusion, system evaluation
reports a similar performance between users with previous experience in virtual
reality applications and inexperienced users, referring to a steep learning
curve.
Bottleneck Supervised U-Net for Pixel-wise Liver and Tumor Segmentation
Authors: Song Li, Geoffrey Kwok Fai Tso
Date: 2018-10-16 00:00:00
Tasks: Segmentation, Tumor Segmentation

In this paper, we propose a bottleneck supervised (BS) U-Net model for liver
and tumor segmentation. Our main contributions are: first, we propose a
variation of the original U-Net that incorporates dense modules, inception
modules and dilated convolution in the encoding path; second, we propose a
bottleneck supervised (BS) U-Net that contains an encoding U-Net and a
segmentation U-Net. To train the BS U-Net, the encoding U-Net is first trained
to get encodings of the label maps that contain the anatomical information
(shape and location). Subsequently, this information is used to guide the
training of the segmentation U-Net so as to reserve the anatomical features of
the target objects. More specifically, the loss function for segmentation U-Net
is set to be the weighted average of the dice loss and the MSE loss between the
encodings and the bottleneck feature vectors. The model is applied to a public
liver and tumor CT scan dataset. Experimental results show that besides
achieving excellent overall segmentation performance, BS U-Net also works great
in controlling shape distortion, reducing false positive and false negative
cases.
SciLens: Evaluating the Quality of Scientific News Articles Using Social Media and Scientific Literature Indicators
Authors: Panayiotis Smeros, Carlos Castillo, Karl Aberer
Date: 2019-03-13 00:00:00
Tasks: Articles

This paper describes, develops, and validates SciLens, a method to evaluate
the quality of scientific news articles. The starting point for our work are
structured methodologies that define a series of quality aspects for manually
evaluating news. Based on these aspects, we describe a series of indicators of
news quality. According to our experiments, these indicators help non-experts
evaluate more accurately the quality of a scientific news article, compared to
non-experts that do not have access to these indicators. Furthermore, SciLens
can also be used to produce a completely automated quality score for an
article, which agrees more with expert evaluators than manual evaluations done
by non-experts. One of the main elements of SciLens is the focus on both
content and context of articles, where context is provided by (1) explicit and
implicit references on the article to scientific literature, and (2) reactions
in social media referencing the article. We show that both contextual elements
can be valuable sources of information for determining article quality. The
validation of SciLens, done through a combination of expert and non-expert
annotation, demonstrates its effectiveness for both semi-automatic and
automatic quality evaluation of scientific news.
Scaling Multi-Domain Dialogue State Tracking via Query Reformulation
Authors: Pushpendre Rastogi, Arpit Gupta, Tongfei Chen, Lambert Mathias
Date: 2019-03-12 00:00:00
Proceeding: NAACL 2019 6
Tasks: Dialogue State Tracking, Multi-domain Dialogue State Tracking, Multi-Task Learning, Referring Expression, Spoken Language Understanding

We present a novel approach to dialogue state tracking and referring
expression resolution tasks. Successful contextual understanding of multi-turn
spoken dialogues requires resolving referring expressions across turns and
tracking the entities relevant to the conversation across turns. Tracking
conversational state is particularly challenging in a multi-domain scenario
when there exist multiple spoken language understanding (SLU) sub-systems, and
each SLU sub-system operates on its domain-specific meaning representation.
While previous approaches have addressed the disparate schema issue by learning
candidate transformations of the meaning representation, in this paper, we
instead model the reference resolution as a dialogue context-aware user query
reformulation task -- the dialog state is serialized to a sequence of natural
language tokens representing the conversation. We develop our model for query
reformulation using a pointer-generator network and a novel multi-task learning
setup. In our experiments, we show a significant improvement in absolute F1 on
an internal as well as a, soon to be released, public benchmark respectively.
Topological Analysis of Syntactic Structures
Authors: Alexander Port, Taelin Karidi, Matilde Marcolli
Date: 2019-03-12 00:00:00
Tasks: Clustering, Topological Data Analysis

We use the persistent homology method of topological data analysis and
dimensional analysis techniques to study data of syntactic structures of world
languages. We analyze relations between syntactic parameters in terms of
dimensionality, of hierarchical clustering structures, and of non-trivial
loops. We show there are relations that hold across language families and
additional relations that are family-specific. We then analyze the trees
describing the merging structure of persistent connected components for
languages in different language families and we show that they partly correlate
to historical phylogenetic trees but with significant differences. We also show
the existence of interesting non-trivial persistent first homology groups in
various language families. We give examples where explicit generators for the
persistent first homology can be identified, some of which appear to correspond
to homoplasy phenomena, while others may have an explanation in terms of
historical linguistics, corresponding to known cases of syntactic borrowing
across different language subfamilies.
"Hang in There": Lexical and Visual Analysis to Identify Posts Warranting Empathetic Responses
Authors: Mimansa Jaiswal, Sairam Tabibu, Erik Cambria
Date: 2019-03-12 00:00:00

In the past few years, social media has risen as a platform where people
express and share personal incidences about abuse, violence and mental health
issues. There is a need to pinpoint such posts and learn the kind of response
expected. For this purpose, we understand the sentiment that a personal story
elicits on different posts present on different social media sites, on the
topics of abuse or mental health. In this paper, we propose a method supported
by hand-crafted features to judge if the post requires an empathetic response.
The model is trained upon posts from various web-pages and corresponding
comments, on both the captions and the images. We were able to obtain 80%
accuracy in tagging posts requiring empathetic responses.