pwc-archive/papers-with-abstracts huggingface dataset:
VideoBERT: A Joint Model for Video and Language Representation Learning
Authors: Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, Cordelia Schmid
Date: 2019-04-03 00:00:00
Proceeding: ICCV 2019 10
Tasks: Action Classification, General Classification, Language Modeling, Language Modelling, Quantization, Representation Learning, Self-Supervised Learning, speech-recognition, Speech Recognition, Video Captioning

Self-supervised learning has become increasingly important to leverage the abundance of unlabeled data available on platforms like YouTube. Whereas most existing approaches learn low-level representations, we propose a joint visual-linguistic model to learn high-level features without any explicit supervision. In particular, inspired by its recent success in language modeling, we build upon the BERT model to learn bidirectional joint distributions over sequences of visual and linguistic tokens, derived from vector quantization of video data and off-the-shelf speech recognition outputs, respectively. We use VideoBERT in numerous tasks, including action classification and video captioning. We show that it can be applied directly to open-vocabulary classification, and confirm that large amounts of training data and cross-modal information are critical to performance. Furthermore, we outperform the state-of-the-art on video captioning, and quantitative results verify that the model learns high-level semantic features.
Batched Multi-armed Bandits Problem
Authors: Zijun Gao, Yanjun Han, Zhimei Ren, Zhengqing Zhou
Date: 2019-04-03 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Multi-Armed Bandits

In this paper, we study the multi-armed bandit problem in the batched setting where the employed policy must split data into a small number of batches. While the minimax regret for the two-armed stochastic bandits has been completely characterized in \cite{perchet2016batched}, the effect of the number of arms on the regret for the multi-armed case is still open. Moreover, the question whether adaptively chosen batch sizes will help to reduce the regret also remains underexplored. In this paper, we propose the BaSE (batched successive elimination) policy to achieve the rate-optimal regrets (within logarithmic factors) for batched multi-armed bandits, with matching lower bounds even if the batch sizes are determined in an adaptive manner.
Do not Omit Local Minimizer: a Complete Solution for Pose Estimation from 3D Correspondences
Authors: Lipu Zhou, Shengze Wang, Jiamin Ye, Michael Kaess
Date: 2019-04-03 00:00:00
Tasks: Pose Estimation

Estimating pose from given 3D correspondences, including point-to-point,
point-to-line and point-to-plane correspondences, is a fundamental task in
computer vision with many applications. We present a complete solution for this
task, including a solution for the minimal problem and the least-squares
problem of this task. Previous works mainly focused on finding the global
minimizer to address the least-squares problem. However, existing works that
show the ability to achieve global minimizer are still unsuitable for real-time
applications. Furthermore, as one of contributions of this paper, we prove that
there exist ambiguous configurations for any number of lines and planes. These
configurations have several solutions in theory, which makes the correct
solution may come from a local minimizer. Our algorithm is efficient and able
to reveal local minimizers. We employ the Cayley-Gibbs-Rodriguez (CGR)
parameterization of the rotation to derive a general rational cost for the
three cases of 3D correspondences. The main contribution of this paper is to
solve the resulting equation system of the minimal problem and the first-order
optimality conditions of the least-squares problem, both of which are of
complicated rational forms. The central idea of our algorithm is to introduce
intermediate unknowns to simplify the problem. Extensive experimental results
show that our algorithm significantly outperforms previous algorithms when the
number of correspondences is small. Besides, when the global minimizer is the
solution, our algorithm achieves the same accuracy as previous algorithms that
have guaranteed global optimality, but our algorithm is applicable to real-time
applications.
Cross-Entropy Adversarial View Adaptation for Person Re-identification
Authors: Lin Wu, Richang Hong, Yang Wang, Meng Wang
Date: 2019-04-03 00:00:00
Tasks: Person Re-Identification

Person re-identification (re-ID) is a task of matching pedestrians under disjoint camera views. To recognise paired snapshots, it has to cope with large cross-view variations caused by the camera view shift. Supervised deep neural networks are effective in producing a set of non-linear projections that can transform cross-view images into a common feature space. However, they typically impose a symmetric architecture, yielding the network ill-conditioned on its optimisation. In this paper, we learn view-invariant subspace for person re-ID, and its corresponding similarity metric using an adversarial view adaptation approach. The main contribution is to learn coupled asymmetric mappings regarding view characteristics which are adversarially trained to address the view discrepancy by optimising the cross-entropy view confusion objective. To determine the similarity value, the network is empowered with a similarity discriminator to promote features that are highly discriminant in distinguishing positive and negative pairs. The other contribution includes an adaptive weighing on the most difficult samples to address the imbalance of within/between-identity pairs. Our approach achieves notable improved performance in comparison to state-of-the-arts on benchmark datasets.
Exponentially convergent stochastic k-PCA without variance reduction
Authors: Cheng Tang
Date: 2019-04-03 00:00:00
Proceeding: NeurIPS 2019 12

We present Matrix Krasulina, an algorithm for online k-PCA, by generalizing
the classic Krasulina's method (Krasulina, 1969) from vector to matrix case. We
show, both theoretically and empirically, that the algorithm naturally adapts
to data low-rankness and converges exponentially fast to the ground-truth
principal subspace. Notably, our result suggests that despite various recent
efforts to accelerate the convergence of stochastic-gradient based methods by
adding a O(n)-time variance reduction step, for the k-PCA problem, a truly
online SGD variant suffices to achieve exponential convergence on intrinsically
low-rank data.
Fully Using Classifiers for Weakly Supervised Semantic Segmentation with Modified Cues
Authors: Ting Sun, Lei Tai, Zhihan Gao, Ming Liu, Dit-yan Yeung
Date: 2019-04-03 00:00:00
Tasks: Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

This paper proposes a novel weakly-supervised semantic segmentation method using image-level label only. The class-specific activation maps from the well-trained classifiers are used as cues to train a segmentation network. The well-known defects of these cues are coarseness and incompleteness. We use super-pixel to refine them, and fuse the cues extracted from both a color image trained classifier and a gray image trained classifier to compensate for their incompleteness. The conditional random field is adapted to regulate the training process and to refine the outputs further. Besides initializing the segmentation network, the previously trained classifier is also used in the testing phase to suppress the non-existing classes. Experimental results on the PASCAL VOC 2012 dataset illustrate the effectiveness of our method.
Evaluation of the Spatio-Temporal features and GAN for Micro-expression Recognition System
Authors: Sze-Teng Liong, Y. S. Gan, Danna Zheng, Shu-Meng Lic, Hao-Xuan Xua, Han-Zhe Zhang, Ran-Ke Lyu, Kun-Hong Liu
Date: 2019-04-03 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Micro Expression Recognition, Micro-Expression Recognition, Optical Flow Estimation

Owing to the development and advancement of artificial intelligence, numerous
works were established in the human facial expression recognition system.
Meanwhile, the detection and classification of micro-expressions are attracting
attentions from various research communities in the recent few years. In this
paper, we first review the processes of a conventional optical-flow-based
recognition system, which comprised of facial landmarks annotations, optical
flow guided images computation, features extraction and emotion class
categorization. Secondly, a few approaches have been proposed to improve the
feature extraction part, such as exploiting GAN to generate more image samples.
Particularly, several variations of optical flow are computed in order to
generate optimal images to lead to high recognition accuracy. Next, GAN, a
combination of Generator and Discriminator, is utilized to generate new "fake"
images to increase the sample size. Thirdly, a modified state-of-the-art
Convolutional neural networks is proposed. To verify the effectiveness of the
the proposed method, the results are evaluated on spontaneous micro-expression
databases, namely SMIC, CASME II and SAMM. Both the F1-score and accuracy
performance metrics are reported in this paper.
On Better Exploring and Exploiting Task Relationships in Multi-Task Learning: Joint Model and Feature Learning
Authors: Ya Li, Xinmei Tian, Tongliang Liu, DaCheng Tao
Date: 2019-04-03 00:00:00
Tasks: Multi-Task Learning

Multitask learning (MTL) aims to learn multiple tasks simultaneously through
the interdependence between different tasks. The way to measure the relatedness
between tasks is always a popular issue. There are mainly two ways to measure
relatedness between tasks: common parameters sharing and common features
sharing across different tasks. However, these two types of relatedness are
mainly learned independently, leading to a loss of information. In this paper,
we propose a new strategy to measure the relatedness that jointly learns shared
parameters and shared feature representations. The objective of our proposed
method is to transform the features from different tasks into a common feature
space in which the tasks are closely related and the shared parameters can be
better optimized. We give a detailed introduction to our proposed multitask
learning method. Additionally, an alternating algorithm is introduced to
optimize the nonconvex objection. A theoretical bound is given to demonstrate
that the relatedness between tasks can be better measured by our proposed
multitask learning algorithm. We conduct various experiments to verify the
superiority of the proposed joint model and feature a multitask learning
method.
FaceQnet: Quality Assessment for Face Recognition based on Deep Learning
Authors: Javier Hernandez-Ortega, Javier Galbally, Julian Fierrez, Rudolf Haraksim, Laurent Beslay
Date: 2019-04-03 00:00:00
Tasks: Deep Learning, Face Recognition

In this paper we develop a Quality Assessment approach for face recognition
based on deep learning. The method consists of a Convolutional Neural Network,
FaceQnet, that is used to predict the suitability of a specific input image for
face recognition purposes. The training of FaceQnet is done using the VGGFace2
database. We employ the BioLab-ICAO framework for labeling the VGGFace2 images
with quality information related to their ICAO compliance level. The
groundtruth quality labels are obtained using FaceNet to generate comparison
scores. We employ the groundtruth data to fine-tune a ResNet-based CNN, making
it capable of returning a numerical quality measure for each input image.
Finally, we verify if the FaceQnet scores are suitable to predict the expected
performance when employing a specific image for face recognition with a COTS
face recognition system. Several conclusions can be drawn from this work, most
notably: 1) we managed to employ an existing ICAO compliance framework and a
pretrained CNN to automatically label data with quality information, 2) we
trained FaceQnet for quality estimation by fine-tuning a pre-trained face
recognition network (ResNet-50), and 3) we have shown that the predictions from
FaceQnet are highly correlated with the face recognition accuracy of a
state-of-the-art commercial system not used during development. FaceQnet is
publicly available in GitHub.
SADIH: Semantic-Aware DIscrete Hashing
Authors: Zheng Zhang, Guo-Sen Xie, Yang Li, Sheng Li, Zi Huang
Date: 2019-04-03 00:00:00
Tasks: Retrieval

Due to its low storage cost and fast query speed, hashing has been recognized
to accomplish similarity search in large-scale multimedia retrieval
applications. Particularly supervised hashing has recently received
considerable research attention by leveraging the label information to preserve
the pairwise similarities of data points in the Hamming space. However, there
still remain two crucial bottlenecks: 1) the learning process of the full
pairwise similarity preservation is computationally unaffordable and unscalable
to deal with big data; 2) the available category information of data are not
well-explored to learn discriminative hash functions. To overcome these
challenges, we propose a unified Semantic-Aware DIscrete Hashing (SADIH)
framework, which aims to directly embed the transformed semantic information
into the asymmetric similarity approximation and discriminative hashing
function learning. Specifically, a semantic-aware latent embedding is
introduced to asymmetrically preserve the full pairwise similarities while
skillfully handle the cumbersome n times n pairwise similarity matrix.
Meanwhile, a semantic-aware autoencoder is developed to jointly preserve the
data structures in the discriminative latent semantic space and perform data
reconstruction. Moreover, an efficient alternating optimization algorithm is
proposed to solve the resulting discrete optimization problem. Extensive
experimental results on multiple large-scale datasets demonstrate that our
SADIH can clearly outperform the state-of-the-art baselines with the additional
benefit of lower computational costs.
Testing Self-Organizing Multiagent Systems
Authors: Nathalia Nascimento, Carlos Lucena, Paulo Alencar, Carlos Juliano Viana
Date: 2019-04-03 00:00:00

Multiagent Systems (MASs) involve different characteristics, such as
autonomy, asynchronous and social features, which make these systems more
difficult to understand. Thus, there is a lack of procedures guaranteeing that
multiagent systems would behave as desired. Further complicating the situation
is the fact that current agent-based approaches may also involve
non-deterministic characteristics, such as learning, self-adaptation and
self-organization (SASO). Nonetheless, there is a gap in the literature
regarding the testing of systems with these features. This paper presents a
publish-subscribe-based approach to develop test applications that facilitate
the process of failure diagnosis in a self-organizing MAS. These tests are able
to detect failures at the global behavior of the system or at the local
properties of its parts. To illustrate the use of this approach, we developed a
self-organizing MAS system based on the context of the Internet of Things
(IoT), which simulates a set of smart street lights, and we performed
functional ad-hoc tests. The street lights need to interact with each other in
order to achieve the global goals of reducing the energy consumption and
maintaining the maximum visual comfort in illuminated areas. To achieve these
global behaviors, the street lights develop local behaviors automatically
through a self-organizing process based on machine learning algorithms.
Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce
Authors: Jian-Guo Zhang, Pengcheng Zou, Zhao Li, Yao Wan, Xiuming Pan, Yu Gong, Philip S. Yu
Date: 2019-04-03 00:00:00
Proceeding: NAACL 2019 6
Tasks: Attribute, Generative Adversarial Network, Reinforcement Learning

Nowadays, more and more customers browse and purchase products in favor of
using mobile E-Commerce Apps such as Taobao and Amazon. Since merchants are
usually inclined to describe redundant and over-informative product titles to
attract attentions from customers, it is important to concisely display short
product titles on limited screen of mobile phones. To address this discrepancy,
previous studies mainly consider textual information of long product titles and
lacks of human-like view during training and evaluation process. In this paper,
we propose a Multi-Modal Generative Adversarial Network (MM-GAN) for short
product title generation in E-Commerce, which innovatively incorporates image
information and attribute tags from product, as well as textual information
from original long titles. MM-GAN poses short title generation as a
reinforcement learning process, where the generated titles are evaluated by the
discriminator in a human-like view. Extensive experiments on a large-scale
E-Commerce dataset demonstrate that our algorithm outperforms other
state-of-the-art methods. Moreover, we deploy our model into a real-world
online E-Commerce environment and effectively boost the performance of click
through rate and click conversion rate by 1.66% and 1.87%, respectively.
Deep Policy Hashing Network with Listwise Supervision
Authors: Shaoying Wang, Haijiang Lai, Yifan Yang, Jian Yin
Date: 2019-04-03 00:00:00
Tasks: Deep Hashing, Image Retrieval, Retrieval, Triplet

Deep-networks-based hashing has become a leading approach for large-scale
image retrieval, which learns a similarity-preserving network to map similar
images to nearby hash codes. The pairwise and triplet losses are two widely
used similarity preserving manners for deep hashing. These manners ignore the
fact that hashing is a prediction task on the list of binary codes. However,
learning deep hashing with listwise supervision is challenging in 1) how to
obtain the rank list of whole training set when the batch size of the deep
network is always small and 2) how to utilize the listwise supervision. In this
paper, we present a novel deep policy hashing architecture with two systems are
learned in parallel: a query network and a shared and slowly changing database
network. The following three steps are repeated until convergence: 1) the
database network encodes all training samples into binary codes to obtain a
whole rank list, 2) the query network is trained based on policy learning to
maximize a reward that indicates the performance of the whole ranking list of
binary codes, e.g., mean average precision (MAP), and 3) the database network
is updated as the query network. Extensive evaluations on several benchmark
datasets show that the proposed method brings substantial improvements over
state-of-the-art hashing methods.
Stratum: A Serverless Framework for Lifecycle Management of Machine Learning based Data Analytics Tasks
Authors: Anirban Bhattacharjee, Yogesh Barve, Shweta Khare, Shunxing Bao, Aniruddha Gokhale, Thomas Damiano
Date: 2019-04-03 00:00:00
Tasks: BIG-bench Machine Learning, Edge-computing, Management

With the proliferation of machine learning (ML) libraries and frameworks, and
the programming languages that they use, along with operations of data loading,
transformation, preparation and mining, ML model development is becoming a
daunting task. Furthermore, with a plethora of cloud-based ML model development
platforms, heterogeneity in hardware, increased focus on exploiting edge
computing resources for low-latency prediction serving and often a lack of a
complete understanding of resources required to execute ML workflows
efficiently, ML model deployment demands expertise for managing the lifecycle
of ML workflows efficiently and with minimal cost. To address these challenges,
we propose an end-to-end data analytics, a serverless platform called Stratum.
Stratum can deploy, schedule and dynamically manage data ingestion tools, live
streaming apps, batch analytics tools, ML-as-a-service (for inference jobs),
and visualization tools across the cloud-fog-edge spectrum. This paper
describes the Stratum architecture highlighting the problems it resolves.
Using Google Analytics to Support Cybersecurity Forensics
Authors: Han Qin, Kit Riehle, Haozhen Zhao
Date: 2019-04-03 00:00:00
Tasks: Anomaly Detection, Marketing

Web traffic is a valuable data source, typically used in the marketing space
to track brand awareness and advertising effectiveness. However, web traffic is
also a rich source of information for cybersecurity monitoring efforts. To
better understand the threat of malicious cyber actors, this study develops a
methodology to monitor and evaluate web activity using data archived from
Google Analytics. Google Analytics collects and aggregates web traffic,
including information about web visitors' location, date and time of visit,
visited webpages, and searched keywords. This study seeks to streamline
analysis of this data and uses rule-based anomaly detection and predictive
modeling to identify web traffic that deviates from normal patterns. Rather
than evaluating pieces of web traffic individually, the methodology seeks to
emulate real user behavior by creating a new unit of analysis: the user
session. User sessions group individual pieces of traffic from the same
location and date, which transforms the available information from single
point-in-time snapshots to dynamic sessions showing users' trajectory and
intent. The result is faster and better insight into large volumes of noisy web
traffic.
Empirical Study of Deep Learning for Text Classification in Legal Document Review
Authors: Fusheng Wei, Han Qin, Shi Ye, Haozhen Zhao
Date: 2019-04-03 00:00:00
Tasks: Deep Learning, General Classification, regression, text-classification, Text Classification

Predictive coding has been widely used in legal matters to find relevant or
privileged documents in large sets of electronically stored information. It
saves the time and cost significantly. Logistic Regression (LR) and Support
Vector Machines (SVM) are two popular machine learning algorithms used in
predictive coding. Recently, deep learning received a lot of attentions in many
industries. This paper reports our preliminary studies in using deep learning
in legal document review. Specifically, we conducted experiments to compare
deep learning results with results obtained using a SVM algorithm on the four
datasets of real legal matters. Our results showed that CNN performed better
with larger volume of training dataset and should be a fit method in the text
classification in legal industry.