pwc-archive/papers-with-abstracts huggingface dataset:
Path-Restore: Learning Network Path Selection for Image Restoration
Authors: Ke Yu, Xintao Wang, Chao Dong, Xiaoou Tang, Chen Change Loy
Date: 2019-04-23 00:00:00
Tasks: Denoising, Image Restoration, Pathfinder, Reinforcement Learning

Very deep Convolutional Neural Networks (CNNs) have greatly improved the performance on various image restoration tasks. However, this comes at a price of increasing computational burden, hence limiting their practical usages. We observe that some corrupted image regions are inherently easier to restore than others since the distortion and content vary within an image. To leverage this, we propose Path-Restore, a multi-path CNN with a pathfinder that can dynamically select an appropriate route for each image region. We train the pathfinder using reinforcement learning with a difficulty-regulated reward. This reward is related to the performance, complexity and "the difficulty of restoring a region". A policy mask is further investigated to jointly process all the image regions. We conduct experiments on denoising and mixed restoration tasks. The results show that our method achieves comparable or superior performance to existing approaches with less computational cost. In particular, Path-Restore is effective for real-world denoising, where the noise distribution varies across different regions on a single image. Compared to the state-of-the-art RIDNet, our method achieves comparable performance and runs 2.7x faster on the realistic Darmstadt Noise Dataset.
Privacy Preserving Group Membership Verification and Identification
Authors: Marzieh Gheisari, Teddy Furon, Laurent Amsaleg
Date: 2019-04-23 00:00:00
Tasks: Face Recognition, Privacy Preserving

When convoking privacy, group membership verification checks if a biometric
trait corresponds to one member of a group without revealing the identity of
that member. Similarly, group membership identification states which group the
individual belongs to, without knowing his/her identity. A recent contribution
provides privacy and security for group membership protocols through the joint
use of two mechanisms: quantizing biometric templates into discrete embeddings
and aggregating several templates into one group representation. This paper
significantly improves that contribution because it jointly learns how to embed
and aggregate instead of imposing fixed and hard coded rules. This is
demonstrated by exposing the mathematical underpinnings of the learning stage
before showing the improvements through an extensive series of experiments
targeting face recognition. Overall, experiments show that learning yields an
excellent trade-off between security /privacy and verification /identification
performances.
VITAMIN-E: VIsual Tracking And MappINg with Extremely Dense Feature Points
Authors: Masashi Yokozuka, Shuji Oishi, Thompson Simon, Atsuhiko Banno
Date: 2019-04-23 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D geometry, Computational Efficiency, CPU, Visual Tracking

In this paper, we propose a novel indirect monocular SLAM algorithm called "VITAMIN-E," which is highly accurate and robust as a result of tracking extremely dense feature points. Typical indirect methods have difficulty in reconstructing dense geometry because of their careful feature point selection for accurate matching. Unlike conventional methods, the proposed method processes an enormous number of feature points by tracking the local extrema of curvature informed by dominant flow estimation. Because this may lead to high computational cost during bundle adjustment, we propose a novel optimization technique, the "subspace Gauss--Newton method", that significantly improves the computational efficiency of bundle adjustment by partially updating the variables. We concurrently generate meshes from the reconstructed points and merge them for an entire 3D model. The experimental results on the SLAM benchmark dataset EuRoC demonstrated that the proposed method outperformed state-of-the-art SLAM methods, such as DSO, ORB-SLAM, and LSD-SLAM, both in terms of accuracy and robustness in trajectory estimation. The proposed method simultaneously generated significantly detailed 3D geometry from the dense feature points in real time using only a CPU.
Transferable Semi-supervised 3D Object Detection from RGB-D Data
Authors: Yew Siang Tang, Gim Hee Lee
Date: 2019-04-23 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Object Detection, Object, object-detection, Object Detection

We investigate the direction of training a 3D object detector for new object
classes from only 2D bounding box labels of these new classes, while
simultaneously transferring information from 3D bounding box labels of the
existing classes. To this end, we propose a transferable semi-supervised 3D
object detection model that learns a 3D object detector network from training
data with two disjoint sets of object classes - a set of strong classes with
both 2D and 3D box labels, and another set of weak classes with only 2D box
labels. In particular, we suggest a relaxed reprojection loss, box prior loss
and a Box-to-Point Cloud Fit network that allow us to effectively transfer
useful 3D information from the strong classes to the weak classes during
training, and consequently, enable the network to detect 3D objects in the weak
classes during inference. Experimental results show that our proposed algorithm
outperforms baseline approaches and achieves promising results compared to
fully-supervised approaches on the SUN-RGBD and KITTI datasets. Furthermore, we
show that our Box-to-Point Cloud Fit network improves performances of the
fully-supervised approaches on both datasets.
Wasserstein-Fisher-Rao Document Distance
Authors: Zihao Wang, Datong Zhou, Yong Zhang, Hao Wu, Chenglong Bao
Date: 2019-04-23 00:00:00
Tasks: GPU, Semantic Similarity, Semantic Textual Similarity

As a fundamental problem of natural language processing, it is important to measure the distance between different documents. Among the existing methods, the Word Mover's Distance (WMD) has shown remarkable success in document semantic matching for its clear physical insight as a parameter-free model. However, WMD is essentially based on the classical Wasserstein metric, thus it often fails to robustly represent the semantic similarity between texts of different lengths. In this paper, we apply the newly developed Wasserstein-Fisher-Rao (WFR) metric from unbalanced optimal transport theory to measure the distance between different documents. The proposed WFR document distance maintains the great interpretability and simplicity as WMD. We demonstrate that the WFR document distance has significant advantages when comparing the texts of different lengths. In addition, an accelerated Sinkhorn based algorithm with GPU implementation has been developed for the fast computation of WFR distances. The KNN classification results on eight datasets have shown its clear improvement over WMD.
Attention-guided Network for Ghost-free High Dynamic Range Imaging
Authors: Qingsen Yan, Dong Gong, Qinfeng Shi, Anton Van Den Hengel, Chunhua Shen, Ian Reid, Yanning Zhang
Date: 2019-04-23 00:00:00
Proceeding: CVPR 2019 6
Tasks: Optical Flow Estimation, Vocal Bursts Intensity Prediction

Ghosting artifacts caused by moving objects or misalignments is a key
challenge in high dynamic range (HDR) imaging for dynamic scenes. Previous
methods first register the input low dynamic range (LDR) images using optical
flow before merging them, which are error-prone and cause ghosts in results. A
very recent work tries to bypass optical flows via a deep network with
skip-connections, however, which still suffers from ghosting artifacts for
severe movement. To avoid the ghosting from the source, we propose a novel
attention-guided end-to-end deep neural network (AHDRNet) to produce
high-quality ghost-free HDR images. Unlike previous methods directly stacking
the LDR images or features for merging, we use attention modules to guide the
merging according to the reference image. The attention modules automatically
suppress undesired components caused by misalignments and saturation and
enhance desirable fine details in the non-reference images. In addition to the
attention model, we use dilated residual dense block (DRDB) to make full use of
the hierarchical features and increase the receptive field for hallucinating
the missing details. The proposed AHDRNet is a non-flow-based method, which can
also avoid the artifacts generated by optical-flow estimation error.
Experiments on different datasets show that the proposed AHDRNet can achieve
state-of-the-art quantitative and qualitative results.
Quaternion Knowledge Graph Embeddings
Authors: Shuai Zhang, Yi Tay, Lina Yao, Qi Liu
Date: 2019-04-23 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Graph Embedding, Knowledge Graph Completion, Knowledge Graph Embedding, Knowledge Graph Embeddings, Knowledge Graphs, Link Prediction, Representation Learning

In this work, we move beyond the traditional complex-valued representations, introducing more expressive hypercomplex representations to model entities and relations for knowledge graph embeddings. More specifically, quaternion embeddings, hypercomplex-valued embeddings with three imaginary components, are utilized to represent entities. Relations are modelled as rotations in the quaternion space. The advantages of the proposed approach are: (1) Latent inter-dependencies (between all components) are aptly captured with Hamilton product, encouraging a more compact interaction between entities and relations; (2) Quaternions enable expressive rotation in four-dimensional space and have more degree of freedom than rotation in complex plane; (3) The proposed framework is a generalization of ComplEx on hypercomplex space while offering better geometrical interpretations, concurrently satisfying the key desiderata of relational representation learning (i.e., modeling symmetry, anti-symmetry and inversion). Experimental results demonstrate that our method achieves state-of-the-art performance on four well-established knowledge graph completion benchmarks.
Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control
Authors: Róbert Csordás, Jürgen Schmidhuber
Date: 2019-04-23 00:00:00
Tasks: Question Answering

The Differentiable Neural Computer (DNC) can learn algorithmic and question answering tasks. An analysis of its internal activation patterns reveals three problems: Most importantly, the lack of key-value separation makes the address distribution resulting from content-based look-up noisy and flat, since the value influences the score calculation, although only the key should. Second, DNC's de-allocation of memory results in aliasing, which is a problem for content-based look-up. Thirdly, chaining memory reads with the temporal linkage matrix exponentially degrades the quality of the address distribution. Our proposed fixes of these problems yield improved performance on arithmetic tasks, and also improve the mean error rate on the bAbI question answering dataset by 43%.
Sequential modeling of Sessions using Recurrent Neural Networks for Skip Prediction
Authors: Sainath Adapa
Date: 2019-04-23 00:00:00
Tasks: Decoder, Position, Recommendation Systems, Sequential skip prediction

Recommender systems play an essential role in music streaming services,
prominently in the form of personalized playlists. Exploring the user
interactions within these listening sessions can be beneficial to understanding
the user preferences in the context of a single session. In the 'Spotify
Sequential Skip Prediction Challenge', WSDM, and Spotify are challenging people
to understand the way users sequentially interact with music. We describe our
solution approach in this paper and also state proposals for further
improvements to the model. The proposed model initially generates a fixed
vector representation of the session, and this additional information is
incorporated into an Encoder-Decoder style architecture. This method achieved
the seventh position in the competition, with a mean average accuracy of 0.604
on the test set. The solution code is available at
https://github.com/sainathadapa/spotify-sequential-skip-prediction.
CPM-sensitive AUC for CTR prediction
Authors: Zhaocheng Liu, Guangxue Yin
Date: 2019-04-23 00:00:00
Tasks: Click-Through Rate Prediction, Prediction

The prediction of click-through rate (CTR) is crucial for industrial
applications, such as online advertising. AUC is a commonly used evaluation
indicator for CTR models. For advertising platforms, online performance is
generally evaluated by CPM. However, in practice, AUC often improves in offline
evaluation, but online CPM does not. As a result, a huge waste of precious
online traffic and human costs has been caused. This is because there is a gap
between offline AUC and online CPM. AUC can only reflect the order on CTR, but
it does not reflect the order of CTR*Bid. Moreover, the bids of different
advertisements are different, so the loss of income caused by different
reverse-order pair is also different. For this reason, we propose the
CPM-sensitive AUC (csAUC) to solve all these problems. We also give the csAUC
calculation method based on dynamic programming. It can fully support the
calculation of csAUC on large-scale data in real-world applications.
Improving benchmarks for autonomous vehicles testing using synthetically generated images
Authors: Aleksander Lukashou
Date: 2019-04-23 00:00:00
Tasks: Autonomous Vehicles

Nowadays autonomous technologies are a very heavily explored area and
particularly computer vision as the main component of vehicle perception. The
quality of the whole vision system based on neural networks relies on the
dataset it was trained on. It is extremely difficult to find traffic sign
datasets from most of the counties of the world. Meaning autonomous vehicle
from the USA will not be able to drive though Lithuania recognizing all road
signs on the way. In this paper, we propose a solution on how to update model
using a small dataset from the country vehicle will be used in. It is important
to mention that is not panacea, rather small upgrade which can boost autonomous
car development in countries with limited data access. We achieved about 10
percent quality raise and expect even better results during future experiments.
End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets
Authors: Ahmed Imtiaz Humayun, Asif Shahriyar Sushmit, Taufiq Hasan, Mohammed Imamul Hassan Bhuiyan
Date: 2019-04-23 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Sleep Staging

Humans approximately spend a third of their life sleeping, which makes
monitoring sleep an integral part of well-being. In this paper, a 34-layer deep
residual ConvNet architecture for end-to-end sleep staging is proposed. The
network takes raw single channel electroencephalogram (Fpz-Cz) signal as input
and yields hypnogram annotations for each 30s segments as output. Experiments
are carried out for two different scoring standards (5 and 6 stage
classification) on the expanded PhysioNet Sleep-EDF dataset, which contains
multi-source data from hospital and household polysomnography setups. The
performance of the proposed network is compared with that of the
state-of-the-art algorithms in patient independent validation tasks. The
experimental results demonstrate the superiority of the proposed network
compared to the best existing method, providing a relative improvement in
epoch-wise average accuracy of 6.8% and 6.3% on the household data and
multi-source data, respectively. Codes are made publicly available on Github.
Free-form Video Inpainting with 3D Gated Convolution and Temporal PatchGAN
Authors: Ya-Liang Chang, Zhe Yu Liu, Kuan-Ying Lee, Winston Hsu
Date: 2019-04-23 00:00:00
Proceeding: ICCV 2019 10
Tasks: Form, Video Editing, Video Inpainting

Free-form video inpainting is a very challenging task that could be widely used for video editing such as text removal. Existing patch-based methods could not handle non-repetitive structures such as faces, while directly applying image-based inpainting models to videos will result in temporal inconsistency (see http://bit.ly/2Fu1n6b ). In this paper, we introduce a deep learn-ing based free-form video inpainting model, with proposed 3D gated convolutions to tackle the uncertainty of free-form masks and a novel Temporal PatchGAN loss to enhance temporal consistency. In addition, we collect videos and design a free-form mask generation algorithm to build the free-form video inpainting (FVI) dataset for training and evaluation of video inpainting models. We demonstrate the benefits of these components and experiments on both the FaceForensics and our FVI dataset suggest that our method is superior to existing ones. Related source code, full-resolution result videos and the FVI dataset could be found on Github https://github.com/amjltc295/Free-Form-Video-Inpainting .
Ethics of Artificial Intelligence Demarcations
Authors: Anders Braarud Hanssen, Stefano Nichele
Date: 2019-04-23 00:00:00
Tasks: Ethics

In this paper we present a set of key demarcations, particularly important when discussing ethical and societal issues of current AI research and applications. Properly distinguishing issues and concerns related to Artificial General Intelligence and weak AI, between symbolic and connectionist AI, AI methods, data and applications are prerequisites for an informed debate. Such demarcations would not only facilitate much-needed discussions on ethics on current AI technologies and research. In addition sufficiently establishing such demarcations would also enhance knowledge-sharing and support rigor in interdisciplinary research between technical and social sciences.
Statistical Learning and Estimation of Piano Fingering
Authors: Eita Nakamura, Yasuyuki Saito, Kazuyoshi Yoshii
Date: 2019-04-23 00:00:00

Automatic estimation of piano fingering is important for understanding the computational process of music performance and applicable to performance assistance and education systems. While a natural way to formulate the quality of fingerings is to construct models of the constraints/costs of performance, it is generally difficult to find appropriate parameter values for these models. Here we study an alternative data-driven approach based on statistical modeling in which the appropriateness of a given fingering is described by probabilities. Specifically, we construct two types of hidden Markov models (HMMs) and their higher-order extensions. We also study deep neural network (DNN)-based methods for comparison. Using a newly released dataset of fingering annotations, we conduct systematic evaluations of these models as well as a representative constraint-based method. We find that the methods based on high-order HMMs outperform the other methods in terms of estimation accuracies. We also quantitatively study individual difference of fingering and propose evaluation measures that can be used with multiple ground truth data. We conclude that the HMM-based methods are currently state of the art and generate acceptable fingerings in most parts and that they have certain limitations such as ignorance of phrase boundaries and interdependence of the two hands.
A new Edge Detector Based on Parametric Surface Model: Regression Surface Descriptor
Authors: Rémi Cogranne, Rémi Slysz, Laurence Moreau, Houman Borouchaki
Date: 2019-04-23 00:00:00
Tasks: Edge Detection, regression

In this paper we present a new methodology for edge detection in digital
images. The first originality of the proposed method is to consider image
content as a parametric surface. Then, an original parametric local model of
this surface representing image content is proposed. The few parameters
involved in the proposed model are shown to be very sensitive to
discontinuities in surface which correspond to edges in image content. This
naturally leads to the design of an efficient edge detector. Moreover, a
thorough analysis of the proposed model also allows us to explain how these
parameters can be used to obtain edge descriptors such as orientations and
curvatures.
  In practice, the proposed methodology offers two main advantages. First, it
has high customization possibilities in order to be adjusted to a wide range of
different problems, from coarse to fine scale edge detection. Second, it is
very robust to blurring process and additive noise. Numerical results are
presented to emphasis these properties and to confirm efficiency of the
proposed method through a comparative study with other edge detectors.
A Large RGB-D Dataset for Semi-supervised Monocular Depth Estimation
Authors: Jaehoon Cho, Dongbo Min, Youngjung Kim, Kwanghoon Sohn
Date: 2019-04-23 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation, Semantic Segmentation

Current self-supervised methods for monocular depth estimation are largely based on deeply nested convolutional networks that leverage stereo image pairs or monocular sequences during a training phase. However, they often exhibit inaccurate results around occluded regions and depth boundaries. In this paper, we present a simple yet effective approach for monocular depth estimation using stereo image pairs. The study aims to propose a student-teacher strategy in which a shallow student network is trained with the auxiliary information obtained from a deeper and more accurate teacher network. Specifically, we first train the stereo teacher network by fully utilizing the binocular perception of 3-D geometry and then use the depth predictions of the teacher network to train the student network for monocular depth inference. This enables us to exploit all available depth data from massive unlabeled stereo pairs. We propose a strategy that involves the use of a data ensemble to merge the multiple depth predictions of the teacher network to improve the training samples by collecting non-trivial knowledge beyond a single prediction. To refine the inaccurate depth estimation that is used when training the student network, we further propose stereo confidence-guided regression loss that handles the unreliable pseudo depth values in occlusion, texture-less region, and repetitive pattern. To complement the existing dataset comprising outdoor driving scenes, we built a novel large-scale dataset consisting of one million outdoor stereo images taken using hand-held stereo cameras. Finally, we demonstrate that the monocular depth estimation network provides feature representations that are suitable for high-level vision tasks. The experimental results for various outdoor scenarios demonstrate the effectiveness and flexibility of our approach, which outperforms state-of-the-art approaches.
Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis
Authors: Hala Mulki, Hatem Haddad, Mourad Gridach, Ismail Babaoglu
Date: 2019-04-23 00:00:00
Tasks: Arabic Sentiment Analysis, Sentiment Analysis

Social media reflects the public attitudes towards specific events. Events
are often related to persons, locations or organizations, the so-called Named
Entities. This can define Named Entities as sentiment-bearing components. In
this paper, we dive beyond Named Entities recognition to the exploitation of
sentiment-annotated Named Entities in Arabic sentiment analysis. Therefore, we
develop an algorithm to detect the sentiment of Named Entities based on the
majority of attitudes towards them. This enabled tagging Named Entities with
proper tags and, thus, including them in a sentiment analysis framework of two
models: supervised and lexicon-based. Both models were applied on datasets of
multi-dialectal content. The results revealed that Named Entities have no
considerable impact on the supervised model, while employing them in the
lexicon-based model improved the classification performance and outperformed
most of the baseline systems.