pwc-archive/papers-with-abstracts huggingface dataset:
FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking
Authors: Peng Chu, Haibin Ling
Date: 2019-04-10 00:00:00
Proceeding: ICCV 2019 10
Tasks: Management, Multiple Object Tracking, Object Tracking

Data association-based multiple object tracking (MOT) involves multiple
separated modules processed or optimized differently, which results in complex
method design and requires non-trivial tuning of parameters. In this paper, we
present an end-to-end model, named FAMNet, where Feature extraction, Affinity
estimation and Multi-dimensional assignment are refined in a single network.
All layers in FAMNet are designed differentiable thus can be optimized jointly
to learn the discriminative features and higher-order affinity model for robust
MOT, which is supervised by the loss directly from the assignment ground truth.
We also integrate single object tracking technique and a dedicated target
management scheme into the FAMNet-based tracking system to further recover
false negatives and inhibit noisy target candidates generated by the external
detector. The proposed method is evaluated on a diverse set of benchmarks
including MOT2015, MOT2017, KITTI-Car and UA-DETRAC, and achieves promising
performance on all of them in comparison with state-of-the-arts.
Vision-model-based Real-time Localization of Unmanned Aerial Vehicle for Autonomous Structure Inspection under GPS-denied Environment
Authors: Zhexiong Shang, Zhigang Shen
Date: 2019-04-10 00:00:00

UAVs have been widely used in visual inspections of buildings, bridges and
other structures. In either outdoor autonomous or semi-autonomous flights
missions strong GPS signal is vital for UAV to locate its own positions.
However, strong GPS signal is not always available, and it can degrade or fully
loss underneath large structures or close to power lines, which can cause
serious control issues or even UAV crashes. Such limitations highly restricted
the applications of UAV as a routine inspection tool in various domains. In
this paper a vision-model-based real-time self-positioning method is proposed
to support autonomous aerial inspection without the need of GPS support.
Compared to other localization methods that requires additional onboard
sensors, the proposed method uses a single camera to continuously estimate the
inflight poses of UAV. Each step of the proposed method is discussed in detail,
and its performance is tested through an indoor test case.
A Data Fusion Platform for Supporting Bridge Deck Condition Monitoring by Merging Aerial and Ground Inspection Imagery
Authors: Zhexiong Shang, Chongsheng Cheng, Zhigang Shen
Date: 2019-04-10 00:00:00

UAVs showed great efficiency on scanning bridge decks surface by taking a
single shot or through stitching a couple of overlaid still images. If
potential surface deficits are identified through aerial images, subsequent
ground inspections can be scheduled. This two-phase inspection procedure showed
great potentials on increasing field inspection productivity. Since aerial and
ground inspection images are taken at different scales, a tool to properly fuse
these multi-scale images is needed for improving the current bridge deck
condition monitoring practice. In response to this need a data fusion platform
is introduced in this study. Using this proposed platform multi-scale images
taken by different inspection devices can be fused through geo-referencing. As
part of the platform, a web-based user interface is developed to organize and
visualize those images with inspection notes under users queries. For
illustration purpose, a case study involving multi-scale optical and infrared
images from UAV and ground inspector, and its implementation using the proposed
platform is presented.
Context-Aware Embeddings for Automatic Art Analysis
Authors: Noa Garcia, Benjamin Renoust, Yuta Nakashima
Date: 2019-04-10 00:00:00
Tasks: Art Analysis, Cross-Modal Retrieval, General Classification, Multi-Task Learning, Retrieval

Automatic art analysis aims to classify and retrieve artistic representations
from a collection of images by using computer vision and machine learning
techniques. In this work, we propose to enhance visual representations from
neural networks with contextual artistic information. Whereas visual
representations are able to capture information about the content and the style
of an artwork, our proposed context-aware embeddings additionally encode
relationships between different artistic attributes, such as author, school, or
historical period. We design two different approaches for using context in
automatic art analysis. In the first one, contextual data is obtained through a
multi-task learning model, in which several attributes are trained together to
find visual relationships between elements. In the second approach, context is
obtained through an art-specific knowledge graph, which encodes relationships
between artistic attributes. An exhaustive evaluation of both of our models in
several art analysis problems, such as author identification, type
classification, or cross-modal retrieval, show that performance is improved by
up to 7.3% in art classification and 37.24% in retrieval when context-aware
embeddings are used.
Data Priming Network for Automatic Check-Out
Authors: Cong-Cong Li, Dawei Du, Libo Zhang, Tiejian Luo, Yanjun Wu, Qi Tian, Longyin Wen, Siwei Lyu
Date: 2019-04-10 00:00:00
Tasks: Data Augmentation, Domain Adaptation, Image Generation

Automatic Check-Out (ACO) receives increased interests in recent years. An important component of the ACO system is the visual item counting, which recognizes the categories and counts of the items chosen by the customers. However, the training of such a system is challenged by the domain adaptation problem, in which the training data are images from isolated items while the testing images are for collections of items. Existing methods solve this problem with data augmentation using synthesized images, but the image synthesis leads to unreal images that affect the training process. In this paper, we propose a new data priming method to solve the domain adaptation problem. Specifically, we first use pre-augmentation data priming, in which we remove distracting background from the training images using the coarse-to-fine strategy and select images with realistic view angles by the pose pruning method. In the post-augmentation step, we train a data priming network using detection and counting collaborative learning, and select more reliable images from testing data to fine-tune the final visual item tallying network. Experiments on the large scale Retail Product Checkout (RPC) dataset demonstrate the superiority of the proposed method, i.e., we achieve 80.51% checkout accuracy compared with 56.68% of the baseline methods. The source codes can be found in https://isrc.iscas.ac.cn/gitlab/research/acm-mm-2019-ACO.
Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification
Authors: Lingxiao He, Yinggang Wang, Wu Liu, Xingyu Liao, He Zhao, Zhenan Sun, Jiashi Feng
Date: 2019-04-10 00:00:00
Proceeding: ICCV 2019 10
Tasks: Occluded Person Re-Identification, Person Re-Identification

Re-identifying a person across multiple disjoint camera views is important
for intelligent video surveillance, smart retailing and many other
applications. However, existing person re-identification (ReID) methods are
challenged by the ubiquitous occlusion over persons and suffer from performance
degradation. This paper proposes a novel occlusion-robust and alignment-free
model for occluded person ReID and extends its application to realistic and
crowded scenarios. The proposed model first leverages the full convolution
network (FCN) and pyramid pooling to extract spatial pyramid features. Then an
alignment-free matching approach, namely Foreground-aware Pyramid
Reconstruction (FPR), is developed to accurately compute matching scores
between occluded persons, despite their different scales and sizes. FPR uses
the error from robust reconstruction over spatial pyramid features to measure
similarities between two persons. More importantly, we design an
occlusion-sensitive foreground probability generator that focuses more on clean
human body parts to refine the similarity computation with less contamination
from occlusion. The FPR is easily embedded into any end-to-end person ReID
models. The effectiveness of the proposed method is clearly demonstrated by the
experimental results (Rank-1 accuracy) on three occluded person datasets:
Partial REID (78.30\%), Partial iLIDS (68.08\%) and Occluded REID (81.00\%);
and three benchmark person datasets: Market1501 (95.42\%), DukeMTMC (88.64\%)
and CUHK03 (76.08\%)
Decorrelated Adversarial Learning for Age-Invariant Face Recognition
Authors: Hao Wang, Dihong Gong, Zhifeng Li, Wei Liu
Date: 2019-04-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Age-Invariant Face Recognition, Face Recognition, MORPH

There has been an increasing research interest in age-invariant face
recognition. However, matching faces with big age gaps remains a challenging
problem, primarily due to the significant discrepancy of face appearances
caused by aging. To reduce such a discrepancy, in this paper we propose a novel
algorithm to remove age-related components from features mixed with both
identity and age information. Specifically, we factorize a mixed face feature
into two uncorrelated components: identity-dependent component and
age-dependent component, where the identity-dependent component includes
information that is useful for face recognition. To implement this idea, we
propose the Decorrelated Adversarial Learning (DAL) algorithm, where a
Canonical Mapping Module (CMM) is introduced to find the maximum correlation
between the paired features generated by a backbone network, while the backbone
network and the factorization module are trained to generate features reducing
the correlation. Thus, the proposed model learns the decomposed features of age
and identity whose correlation is significantly reduced. Simultaneously, the
identity-dependent feature and the age-dependent feature are respectively
supervised by ID and age preserving signals to ensure that they both contain
the correct information. Extensive experiments are conducted on popular
public-domain face aging datasets (FG-NET, MORPH Album 2, and CACD-VS) to
demonstrate the effectiveness of the proposed approach.
CondConv: Conditionally Parameterized Convolutions for Efficient Inference
Authors: Brandon Yang, Gabriel Bender, Quoc V. Le, Jiquan Ngiam
Date: 2019-04-10 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: General Classification, Image Classification, Object Detection

Convolutional layers are one of the basic building blocks of modern deep neural networks. One fundamental assumption is that convolutional kernels should be shared for all examples in a dataset. We propose conditionally parameterized convolutions (CondConv), which learn specialized convolutional kernels for each example. Replacing normal convolutions with CondConv enables us to increase the size and capacity of a network, while maintaining efficient inference. We demonstrate that scaling networks with CondConv improves the performance and inference cost trade-off of several existing convolutional neural network architectures on both classification and detection tasks. On ImageNet classification, our CondConv approach applied to EfficientNet-B0 achieves state-of-the-art performance of 78.3% accuracy with only 413M multiply-adds. Code and checkpoints for the CondConv Tensorflow layer and CondConv-EfficientNet models are available at: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv.
BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering
Authors: Yu Cao, Meng Fang, DaCheng Tao
Date: 2019-04-10 00:00:00
Proceeding: NAACL 2019 6
Tasks: Question Answering

Multi-hop reasoning question answering requires deep comprehension of
relationships between various documents and queries. We propose a
Bi-directional Attention Entity Graph Convolutional Network (BAG), leveraging
relationships between nodes in an entity graph and attention information
between a query and the entity graph, to solve this task. Graph convolutional
networks are used to obtain a relation-aware representation of nodes for entity
graphs built from documents with multi-level features. Bidirectional attention
is then applied on graphs and queries to generate a query-aware nodes
representation, which will be used for the final prediction. Experimental
evaluation shows BAG achieves state-of-the-art accuracy performance on the
QAngaroo WIKIHOP dataset.
Image Quality Assessment for Omnidirectional Cross-reference Stitching
Authors: Kaiwen Yu, Jia Li, Yu Zhang, Yifan Zhao, Long Xu
Date: 2019-04-10 00:00:00
Tasks: Image Quality Assessment, Image Stitching

Along with the development of virtual reality (VR), omnidirectional images
play an important role in producing multimedia content with immersive
experience. However, despite various existing approaches for omnidirectional
image stitching, how to quantitatively assess the quality of stitched images is
still insufficiently explored. To address this problem, we establish a novel
omnidirectional image dataset containing stitched images as well as
dual-fisheye images captured from standard quarters of 0$^\circ$, 90$^\circ$,
180$^\circ$ and 270$^\circ$. In this manner, when evaluating the quality of an
image stitched from a pair of fisheye images (e.g., 0$^\circ$ and 180$^\circ$),
the other pair of fisheye images (e.g., 90$^\circ$ and 270$^\circ$) can be used
as the cross-reference to provide ground-truth observations of the stitching
regions. Based on this dataset, we further benchmark six widely used stitching
models with seven evaluation metrics for IQA. To the best of our knowledge, it
is the first dataset that focuses on assessing the stitching quality of
omnidirectional images.
On zero-shot recognition of generic objects
Authors: Tristan Hascoet, Yasuo Ariki, Tetsuya Takiguchi
Date: 2019-04-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object Recognition, Zero-Shot Learning

Many recent advances in computer vision are the result of a healthy
competition among researchers on high quality, task-specific, benchmarks. After
a decade of active research, zero-shot learning (ZSL) models accuracy on the
Imagenet benchmark remains far too low to be considered for practical object
recognition applications. In this paper, we argue that the main reason behind
this apparent lack of progress is the poor quality of this benchmark. We
highlight major structural flaws of the current benchmark and analyze different
factors impacting the accuracy of ZSL models. We show that the actual
classification accuracy of existing ZSL models is significantly higher than was
previously thought as we account for these flaws. We then introduce the notion
of structural bias specific to ZSL datasets. We discuss how the presence of
this new form of bias allows for a trivial solution to the standard benchmark
and conclude on the need for a new benchmark. We then detail the semi-automated
construction of a new benchmark to address these flaws.
Distributed Deep Learning Strategies For Automatic Speech Recognition
Authors: Wei Zhang, Xiaodong Cui, Ulrich Finkler, Brian Kingsbury, George Saon, David Kung, Michael Picheny
Date: 2019-04-10 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Deep Learning, speech-recognition, Speech Recognition

In this paper, we propose and investigate a variety of distributed deep
learning strategies for automatic speech recognition (ASR) and evaluate them
with a state-of-the-art Long short-term memory (LSTM) acoustic model on the
2000-hour Switchboard (SWB2000), which is one of the most widely used datasets
for ASR performance benchmark. We first investigate what are the proper
hyper-parameters (e.g., learning rate) to enable the training with sufficiently
large batch size without impairing the model accuracy. We then implement
various distributed strategies, including Synchronous (SYNC), Asynchronous
Decentralized Parallel SGD (ADPSGD) and the hybrid of the two HYBRID, to study
their runtime/accuracy trade-off. We show that we can train the LSTM model
using ADPSGD in 14 hours with 16 NVIDIA P100 GPUs to reach a 7.6% WER on the
Hub5- 2000 Switchboard (SWB) test set and a 13.1% WER on the CallHome (CH) test
set. Furthermore, we can train the model using HYBRID in 11.5 hours with 32
NVIDIA V100 GPUs without loss in accuracy.
Handling temporality of clinical events with application to Adverse Drug Event detection in Electronic Health Records: A scoping review
Authors: Maria Bampa
Date: 2019-04-09 00:00:00
Tasks: Articles, Event Detection, Pharmacovigilance, Time Series, Time Series Analysis

The increased adoption of Electronic Health Records(EHRs) has brought changes
to the way the patient care is carried out. The rich heterogeneous and temporal
data space stored in EHRs can be leveraged by machine learning models to
capture the underlying information and make clinically relevant predictions.
This can be exploited to support public health activities such as
pharmacovigilance and specifically mitigate the public health issue of Adverse
Drug Events(ADEs). The aim of this article is, therefore, to investigate the
various ways of handling temporal data for the purpose of detecting ADEs. Based
on a review of the existing literature, 11 articles from the last 10 years were
chosen to be studied. According to the literature retrieved the main methods
were found to fall into 5 different approaches: based on temporal abstraction,
graph-based, learning weights and data tables containing time series of
different length. To that end, EHRs are a valuable source that has led current
research to the automatic detection of ADEs. Yet there still exists a great
deal of challenges that concerns the exploitation of the heterogeneous, data
types with temporal information included in EHRs for predicting ADEs.
Adaptive Learning Expert System for Diagnosis and Management of Viral Hepatitis
Authors: Henok Yared Agizew
Date: 2019-04-09 00:00:00
Tasks: Management

Viral hepatitis is the regularly found health problem throughout the world
among other easily transmitted diseases, such as tuberculosis, human immune
virus, malaria and so on. Among all hepatitis viruses, the uppermost numbers of
deaths are result from the long-lasting hepatitis C infection or long-lasting
hepatitis B. In order to develop this system, the knowledge is acquired using
both structured and semi-structured interviews from internists of St.Paul
Hospital. Once the knowledge is acquired, it is modeled and represented using
rule based reasoning techniques. Both forward and backward chaining is used to
infer the rules and provide appropriate advices in the developed expert system.
For the purpose of developing the prototype expert system SWI-prolog editor
also used. The proposed system has the ability to adapt with dynamic knowledge
by generalizing rules and discover new rules through learning the newly arrived
knowledge from domain experts adaptively without any help from the knowledge
engineer.
  Keywords: Expert System, Diagnosis and Management of Viral Hepatitis,
Adaptive Learning, Discovery and Generalization Mechanism
Towards High-fidelity Nonlinear 3D Face Morphable Model
Authors: Luan Tran, Feng Liu, Xiaoming Liu
Date: 2019-04-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Face Reconstruction, Face Reconstruction, model, Vocal Bursts Intensity Prediction

Embedding 3D morphable basis functions into deep neural networks opens great
potential for models with better representation power. However, to faithfully
learn those models from an image collection, it requires strong regularization
to overcome ambiguities involved in the learning process. This critically
prevents us from learning high fidelity face models which are needed to
represent face images in high level of details. To address this problem, this
paper presents a novel approach to learn additional proxies as means to
side-step strong regularizations, as well as, leverages to promote detailed
shape/albedo. To ease the learning, we also propose to use a dual-pathway
network, a carefully-designed architecture that brings a balance between global
and local-based models. By improving the nonlinear 3D morphable model in both
learning objective and network architecture, we present a model which is
superior in capturing higher level of details than the linear or its precedent
nonlinear counterparts. As a result, our model achieves state-of-the-art
performance on 3D face reconstruction by solely optimizing latent
representations.
Gait Recognition via Disentangled Representation Learning
Authors: Ziyuan Zhang, Luan Tran, Xi Yin, Yousef Atoum, Xiaoming Liu, Jian Wan, Nanxin Wang
Date: 2019-04-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: Computational Efficiency, Disentanglement, Gait Recognition, Representation Learning

Gait, the walking pattern of individuals, is one of the most important
biometrics modalities. Most of the existing gait recognition methods take
silhouettes or articulated body models as the gait features. These methods
suffer from degraded recognition performance when handling confounding
variables, such as clothing, carrying and view angle. To remedy this issue, we
propose a novel AutoEncoder framework to explicitly disentangle pose and
appearance features from RGB imagery and the LSTM-based integration of pose
features over time produces the gait feature. In addition, we collect a
Frontal-View Gait (FVG) dataset to focus on gait recognition from frontal-view
walking, which is a challenging problem since it contains minimal gait cues
compared to other views. FVG also includes other important variations, e.g.,
walking speed, carrying, and clothing. With extensive experiments on CASIA-B,
USF and FVG datasets, our method demonstrates superior performance to the state
of the arts quantitatively, the ability of feature disentanglement
qualitatively, and promising computational efficiency.
Novel Uncertainty Framework for Deep Learning Ensembles
Authors: Tal Kachman, Michal Moshkovitz, Michal Rosen-Zvi
Date: 2019-04-09 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Gaussian Processes, General Classification, Variational Inference

Deep neural networks have become the default choice for many of the machine
learning tasks such as classification and regression. Dropout, a method
commonly used to improve the convergence of deep neural networks, generates an
ensemble of thinned networks with extensive weight sharing. Recent studies that
dropout can be viewed as an approximate variational inference in Gaussian
processes, and used as a practical tool to obtain uncertainty estimates of the
network. We propose a novel statistical mechanics based framework to dropout
and use this framework to propose a new generic algorithm that focuses on
estimates of the variance of the loss as measured by the ensemble of thinned
networks. Our approach can be applied to a wide range of deep neural network
architectures and machine learning tasks. In classification, this algorithm
allows the generation of a don't-know answer to be generated, which can
increase the reliability of the classifier. Empirically we demonstrate
state-of-the-art AUC results on publicly available benchmarks.