pwc-archive/papers-with-abstracts huggingface dataset:
Spatial and Temporal Mutual Promotion for Video-based Person Re-identification
Authors: Yiheng Liu, Zhenxun Yuan, Wengang Zhou, Houqiang Li
Date: 2018-12-26 00:00:00
Tasks: Person Re-Identification, Video-Based Person Re-Identification

Video-based person re-identification is a crucial task of matching video
sequences of a person across multiple camera views. Generally, features
directly extracted from a single frame suffer from occlusion, blur,
illumination and posture changes. This leads to false activation or missing
activation in some regions, which corrupts the appearance and motion
representation. How to explore the abundant spatial-temporal information in
video sequences is the key to solve this problem. To this end, we propose a
Refining Recurrent Unit (RRU) that recovers the missing parts and suppresses
noisy parts of the current frame's features by referring historical frames.
With RRU, the quality of each frame's appearance representation is improved.
Then we use the Spatial-Temporal clues Integration Module (STIM) to mine the
spatial-temporal information from those upgraded features. Meanwhile, the
multi-level training objective is used to enhance the capability of RRU and
STIM. Through the cooperation of those modules, the spatial and temporal
features mutually promote each other and the final spatial-temporal feature
representation is more discriminative and robust. Extensive experiments are
conducted on three challenging datasets, i.e., iLIDS-VID, PRID-2011 and MARS.
The experimental results demonstrate that our approach outperforms existing
state-of-the-art methods of video-based person re-identification on iLIDS-VID
and MARS and achieves favorable results on PRID-2011.
An Investigation of Supervised Learning Methods for Authorship Attribution in Short Hinglish Texts using Char & Word N-grams
Authors: Abhay Sharma, Ananya Nandan, Reetika Ralhan
Date: 2018-12-26 00:00:00
Tasks: Authorship Attribution

The writing style of a person can be affirmed as a unique identity indicator;
the words used, and the structuring of the sentences are clear measures which
can identify the author of a specific work. Stylometry and its subset -
Authorship Attribution, have a long history beginning from the 19th century,
and we can still find their use in modern times. The emergence of the Internet
has shifted the application of attribution studies towards non-standard texts
that are comparatively shorter to and different from the long texts on which
most research has been done. The aim of this paper focuses on the study of
short online texts, retrieved from messaging application called WhatsApp and
studying the distinctive features of a macaronic language (Hinglish), using
supervised learning methods and then comparing the models. Various features
such as word n-gram and character n-gram are compared via methods viz., Naive
Bayes Classifier, Support Vector Machine, Conditional Tree, and Random Forest,
to find the best discriminator for such corpora. Our results showed that SVM
attained a test accuracy of up to 95.079% while similarly, Naive Bayes attained
an accuracy of up to 94.455% for the dataset. Conditional Tree & Random Forest
failed to perform as well as expected. We also found that word unigram and
character 3-grams features were more likely to distinguish authors accurately
than other features.
A Survey of Deep Facial Attribute Analysis
Authors: Xin Zheng, Yanqing Guo, Huaibo Huang, Yi Li, Ran He
Date: 2018-12-26 00:00:00
Tasks: Attribute, Survey

Facial attribute analysis has received considerable attention when deep learning techniques made remarkable breakthroughs in this field over the past few years. Deep learning based facial attribute analysis consists of two basic sub-issues: facial attribute estimation (FAE), which recognizes whether facial attributes are present in given images, and facial attribute manipulation (FAM), which synthesizes or removes desired facial attributes. In this paper, we provide a comprehensive survey of deep facial attribute analysis from the perspectives of both estimation and manipulation. First, we summarize a general pipeline that deep facial attribute analysis follows, which comprises two stages: data preprocessing and model construction. Additionally, we introduce the underlying theories of this two-stage pipeline for both FAE and FAM. Second, the datasets and performance metrics commonly used in facial attribute analysis are presented. Third, we create a taxonomy of state-of-the-art methods and review deep FAE and FAM algorithms in detail. Furthermore, several additional facial attribute related issues are introduced, as well as relevant real-world applications. Finally, we discuss possible challenges and promising future research directions.
The CORAL+ Algorithm for Unsupervised Domain Adaptation of PLDA
Authors: Kong Aik Lee, Qiongqiong Wang, Takafumi Koshinaka
Date: 2018-12-26 00:00:00
Tasks: Domain Adaptation, Speaker Recognition, Unsupervised Domain Adaptation

State-of-the-art speaker recognition systems comprise an x-vector (or i-vector) speaker embedding front-end followed by a probabilistic linear discriminant analysis (PLDA) backend. The effectiveness of these components relies on the availability of a large collection of labeled training data. In practice, it is common that the domains (e.g., language, demographic) in which the system are deployed differs from that we trained the system. To close the gap due to the domain mismatch, we propose an unsupervised PLDA adaptation algorithm to learn from a small amount of unlabeled in-domain data. The proposed method was inspired by a prior work on feature-based domain adaptation technique known as the correlation alignment (CORAL). We refer to the model-based adaptation technique proposed in this paper as CORAL+. The efficacy of the proposed technique is experimentally validated on the recent NIST 2016 and 2018 Speaker Recognition Evaluation (SRE'16, SRE'18) datasets.
A Whole Slide Image Grading Benchmark and Tissue Classification for Cervical Cancer Precursor Lesions with Inter-Observer Variability
Authors: Abdulkadir Albayrak, Asli Unlu, Nurullah Calik, Abdulkerim Capar, Gokhan Bilgin, Behcet Ugur Toreyin, Bahar Muezzinoglu, Ilknur Turkmen, Lutfiye Durak-Ata
Date: 2018-12-26 00:00:00
Tasks: General Classification

The cervical cancer developing from the precancerous lesions caused by the
Human Papilloma Virus (HPV) has been one of the preventable cancers with the
help of periodic screening. There are two types of grading conventions widely
accepted among pathologists. On the other hand, inter-observer variability is
an important issue for final diagnosis. In this paper, a whole-slide image
grading benchmark for cervical cancer precursor lesions is introduced. The
papillae of the cervical epithelium and overlapping cell problems are handled
and a tissue classification method with a novel morphological feature
exploiting the relative orientation between the BM and the major axis of all
nuclei is developed and its performance is evaluated. Besides, the
inter-observer variability is also revealed by a thorough comparison among
pathologists' decisions, as well as, the final diagnosis.
Optimizing Market Making using Multi-Agent Reinforcement Learning
Authors: Yagna Patel
Date: 2018-12-26 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, reinforcement learning is applied to the problem of optimizing
market making. A multi-agent reinforcement learning framework is used to
optimally place limit orders that lead to successful trades. The framework
consists of two agents. The macro-agent optimizes on making the decision to
buy, sell, or hold an asset. The micro-agent optimizes on placing limit orders
within the limit order book. For the context of this paper, the proposed
framework is applied and studied on the Bitcoin cryptocurrency market. The goal
of this paper is to show that reinforcement learning is a viable strategy that
can be applied to complex problems (with complex environments) such as market
making.
Deep learning electromagnetic inversion with convolutional neural networks
Authors: Vladimir Puzyrev
Date: 2018-12-26 00:00:00
Tasks: Deep Learning, Uncertainty Quantification

Geophysical inversion attempts to estimate the distribution of physical
properties in the Earth's interior from observations collected at or above the
surface. Inverse problems are commonly posed as least-squares optimization
problems in high-dimensional parameter spaces. Existing approaches are largely
based on deterministic gradient-based methods, which are limited by
nonlinearity and nonuniqueness of the inverse problem. Probabilistic inversion
methods, despite their great potential in uncertainty quantification, still
remain a formidable computational task. In this paper, I explore the potential
of deep learning methods for electromagnetic inversion. This approach does not
require calculation of the gradient and provides results instantaneously. Deep
neural networks based on fully convolutional architecture are trained on large
synthetic datasets obtained by full 3-D simulations. The performance of the
method is demonstrated on models of strong practical relevance representing an
onshore controlled source electromagnetic CO2 monitoring scenario. The
pre-trained networks can reliably estimate the position and lateral dimensions
of the anomalies, as well as their resistivity properties. Several fully
convolutional network architectures are compared in terms of their accuracy,
generalization, and cost of training. Examples with different survey geometry
and noise levels confirm the feasibility of the deep learning inversion,
opening the possibility to estimate the subsurface resistivity distribution in
real time.
Towards a Theoretical Understanding of Hashing-Based Neural Nets
Authors: Yibo Lin, Zhao Song, Lin F. Yang
Date: 2018-12-26 00:00:00

Parameter reduction has been an important topic in deep learning due to the
ever-increasing size of deep neural network models and the need to train and
run them on resource limited machines. Despite many efforts in this area, there
were no rigorous theoretical guarantees on why existing neural net compression
methods should work. In this paper, we provide provable guarantees on some
hashing-based parameter reduction methods in neural nets. First, we introduce a
neural net compression scheme based on random linear sketching (which is
usually implemented efficiently via hashing), and show that the sketched
(smaller) network is able to approximate the original network on all input data
coming from any smooth and well-conditioned low-dimensional manifold. The
sketched network can also be trained directly via back-propagation. Next, we
study the previously proposed HashedNets architecture and show that the
optimization landscape of one-hidden-layer HashedNets has a local strong
convexity property similar to a normal fully connected neural network. We
complement our theoretical results with empirical verifications.
Studying the Plasticity in Deep Convolutional Neural Networks using Random Pruning
Authors: Deepak Mittal, Shweta Bhardwaj, Mitesh M. Khapra, Balaraman Ravindran
Date: 2018-12-26 00:00:00
Tasks: image-classification, Image Classification, Image Segmentation, object-detection, Object Detection, Semantic Segmentation

Recently there has been a lot of work on pruning filters from deep
convolutional neural networks (CNNs) with the intention of reducing
computations.The key idea is to rank the filters based on a certain criterion
(say, l1-norm) and retain only the top ranked filters. Once the low scoring
filters are pruned away the remainder of the network is fine tuned and is shown
to give performance comparable to the original unpruned network. In this work,
we report experiments which suggest that the comparable performance of the
pruned network is not due to the specific criterion chosen but due to the
inherent plasticity of deep neural networks which allows them to recover from
the loss of pruned filters once the rest of the filters are fine-tuned.
Specifically we show counter-intuitive results wherein by randomly pruning
25-50% filters from deep CNNs we are able to obtain the same performance as
obtained by using state-of-the-art pruning methods. We empirically validate our
claims by doing an exhaustive evaluation with VGG-16 and ResNet-50. We also
evaluate a real world scenario where a CNN trained on all 1000 ImageNet classes
needs to be tested on only a small set of classes at test time (say, only
animals). We create a new benchmark dataset from ImageNet to evaluate such
class specific pruning and show that even here a random pruning strategy gives
close to state-of-the-art performance. Unlike existing approaches which mainly
focus on the task of image classification, in this work we also report results
on object detection and image segmentation. We show that using a simple random
pruning strategy we can achieve significant speed up in object detection (74%
improvement in fps) while retaining the same accuracy as that of the original
Faster RCNN model. Similarly we show that the performance of a pruned
Segmentation Network (SegNet) is actually very similar to that of the original
unpruned SegNet.
A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling
Authors: Yu Wang, Yilin Shen, Hongxia Jin
Date: 2018-12-26 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decoder, Intent Detection, model, Semantic Frame Parsing, slot-filling, Slot Filling, Spoken Language Understanding

Intent detection and slot filling are two main tasks for building a spoken
language understanding(SLU) system. Multiple deep learning based models have
demonstrated good results on these tasks . The most effective algorithms are
based on the structures of sequence to sequence models (or "encoder-decoder"
models), and generate the intents and semantic tags either using separate
models or a joint model. Most of the previous studies, however, either treat
the intent detection and slot filling as two separate parallel tasks, or use a
sequence to sequence model to generate both semantic tags and intent. Most of
these approaches use one (joint) NN based model (including encoder-decoder
structure) to model two tasks, hence may not fully take advantage of the
cross-impact between them. In this paper, new Bi-model based RNN semantic frame
parsing network structures are designed to perform the intent detection and
slot filling tasks jointly, by considering their cross-impact to each other
using two correlated bidirectional LSTMs (BLSTM). Our Bi-model structure with a
decoder achieves state-of-the-art result on the benchmark ATIS data, with about
0.5$\%$ intent accuracy improvement and 0.9 $\%$ slot filling improvement.
A New Concept of Deep Reinforcement Learning based Augmented General Sequence Tagging System
Authors: Yu Wang, Abhishek Patel, Hongxia Jin
Date: 2018-12-26 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, a new deep reinforcement learning based augmented general
sequence tagging system is proposed. The new system contains two parts: a deep
neural network (DNN) based sequence tagging model and a deep reinforcement
learning (DRL) based augmented tagger. The augmented tagger helps improve
system performance by modeling the data with minority tags. The new system is
evaluated on SLU and NLU sequence tagging tasks using ATIS and CoNLL-2003
benchmark datasets, to demonstrate the new system's outstanding performance on
general tagging tasks. Evaluated by F1 scores, it shows that the new system
outperforms the current state-of-the-art model on ATIS dataset by 1.9% and that
on CoNLL-2003 dataset by 1.4%.
An Investigation of Few-Shot Learning in Spoken Term Classification
Authors: Yangbin Chen, Tom Ko, Lifeng Shang, Xiao Chen, Xin Jiang, Qing Li
Date: 2018-12-26 00:00:00
Tasks: Few-Shot Learning, General Classification, Keyword Spotting, Meta-Learning

In this paper, we investigate the feasibility of applying few-shot learning algorithms to a speech task. We formulate a user-defined scenario of spoken term classification as a few-shot learning problem. In most few-shot learning studies, it is assumed that all the N classes are new in a N-way problem. We suggest that this assumption can be relaxed and define a N+M-way problem where N and M are the number of new classes and fixed classes respectively. We propose a modification to the Model-Agnostic Meta-Learning (MAML) algorithm to solve the problem. Experiments on the Google Speech Commands dataset show that our approach outperforms the conventional supervised learning approach and the original MAML.
Learning to Refine Source Representations for Neural Machine Translation
Authors: Xinwei Geng, Long-Yue Wang, Xing Wang, Bing Qin, Ting Liu, Zhaopeng Tu
Date: 2018-12-26 00:00:00
Tasks: Decoder, Machine Translation, NMT, Reinforcement Learning, Sentence, Translation

Neural machine translation (NMT) models generally adopt an encoder-decoder
architecture for modeling the entire translation process. The encoder
summarizes the representation of input sentence from scratch, which is
potentially a problem if the sentence is ambiguous. When translating a text,
humans often create an initial understanding of the source sentence and then
incrementally refine it along the translation on the target side. Starting from
this intuition, we propose a novel encoder-refiner-decoder framework, which
dynamically refines the source representations based on the generated
target-side information at each decoding step. Since the refining operations
are time-consuming, we propose a strategy, leveraging the power of
reinforcement learning models, to decide when to refine at specific decoding
steps. Experimental results on both Chinese-English and English-German
translation tasks show that the proposed approach significantly and
consistently improves translation performance over the standard encoder-decoder
framework. Furthermore, when refining strategy is applied, results still show
reasonable improvement over the baseline without much decrease in decoding
speed.
3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-identification
Authors: Lin Wu, Yang Wang, Ling Shao, Meng Wang
Date: 2018-12-26 00:00:00
Tasks: Person Re-Identification, Video-Based Person Re-Identification

In this paper, we introduce a global video representation to video-based
person re-identification (re-ID) that aggregates local 3D features across the
entire video extent. Most of the existing methods rely on 2D convolutional
networks (ConvNets) to extract frame-wise deep features which are pooled
temporally to generate the video-level representations. However, 2D ConvNets
lose temporal input information immediately after the convolution, and a
separate temporal pooling is limited in capturing human motion in shorter
sequences. To this end, we present a \textit{global} video representation (3D
PersonVLAD), complementary to 3D ConvNets as a novel layer to capture the
appearance and motion dynamics in full-length videos. However, encoding each
video frame in its entirety and computing an aggregate global representation
across all frames is tremendously challenging due to occlusions and
misalignments. To resolve this, our proposed network is further augmented with
3D part alignment module to learn local features through soft-attention module.
These attended features are statistically aggregated to yield
identity-discriminative representations. Our global 3D features are
demonstrated to achieve state-of-the-art results on three benchmark datasets:
MARS \cite{MARS}, iLIDS-VID \cite{VideoRanking}, and PRID 2011
Seeing isn't Believing: Practical Adversarial Attack Against Object Detectors
Authors: Yue Zhao, Hong Zhu, Ruigang Liang, Qintao Shen, Shengzhi Zhang, Kai Chen
Date: 2018-12-26 00:00:00
Tasks: Adversarial Attack, Autonomous Driving, Object

In this paper, we presented systematic solutions to build robust and practical AEs against real world object detectors. Particularly, for Hiding Attack (HA), we proposed the feature-interference reinforcement (FIR) method and the enhanced realistic constraints generation (ERG) to enhance robustness, and for Appearing Attack (AA), we proposed the nested-AE, which combines two AEs together to attack object detectors in both long and short distance. We also designed diverse styles of AEs to make AA more surreptitious. Evaluation results show that our AEs can attack the state-of-the-art real-time object detectors (i.e., YOLO V3 and faster-RCNN) at the success rate up to 92.4% with varying distance from 1m to 25m and angles from -60{\deg} to 60{\deg}. Our AEs are also demonstrated to be highly transferable, capable of attacking another three state-of-the-art black-box models with high success rate.
End-to-End Latent Fingerprint Search
Authors: Kai Cao, Dinh-Luan Nguyen, Cori Tymoszek, A. K. Jain
Date: 2018-12-26 00:00:00
Tasks: Quantization

A system for identifying latent fingerprints. Created at Michigan State University by Anil K. Jain, Kai Cao, Dinh-Luan Nguyen, and Cori Tymoszek.
RegNet: Learning the Optimization of Direct Image-to-Image Pose Registration
Authors: Lei Han, Mengqi Ji, Lu Fang, Matthias Nießner
Date: 2018-12-26 00:00:00

Direct image-to-image alignment that relies on the optimization of
photometric error metrics suffers from limited convergence range and
sensitivity to lighting conditions. Deep learning approaches has been applied
to address this problem by learning better feature representations using
convolutional neural networks, yet still require a good initialization. In this
paper, we demonstrate that the inaccurate numerical Jacobian limits the
convergence range which could be improved greatly using learned approaches.
Based on this observation, we propose a novel end-to-end network, RegNet, to
learn the optimization of image-to-image pose registration. By jointly learning
feature representation for each pixel and partial derivatives that replace
handcrafted ones (e.g., numerical differentiation) in the optimization step,
the neural network facilitates end-to-end optimization. The energy landscape is
constrained on both the feature representation and the learned Jacobian, hence
providing more flexibility for the optimization as a consequence leads to more
robust and faster convergence. In a series of experiments, including a broad
ablation study, we demonstrate that RegNet is able to converge for
large-baseline image pairs with fewer iterations.