pwc-archive/papers-with-abstracts huggingface dataset:
Fully Convolutional Adaptation Networks for Semantic Segmentation
Authors: Yiheng Zhang, Zhaofan Qiu, Ting Yao, Dong Liu, Tao Mei
Date: 2018-04-23 00:00:00
Proceeding: CVPR 2018 6
Tasks: Domain Adaptation, Semantic Segmentation

The recent advances in deep neural networks have convincingly demonstrated
high capability in learning vision models on large datasets. Nevertheless,
collecting expert labeled datasets especially with pixel-level annotations is
an extremely expensive process. An appealing alternative is to render synthetic
data (e.g., computer games) and generate ground truth automatically. However,
simply applying the models learnt on synthetic images may lead to high
generalization error on real images due to domain shift. In this paper, we
facilitate this issue from the perspectives of both visual appearance-level and
representation-level domain adaptation. The former adapts source-domain images
to appear as if drawn from the "style" in the target domain and the latter
attempts to learn domain-invariant representations. Specifically, we present
Fully Convolutional Adaptation Networks (FCAN), a novel deep architecture for
semantic segmentation which combines Appearance Adaptation Networks (AAN) and
Representation Adaptation Networks (RAN). AAN learns a transformation from one
domain to the other in the pixel space and RAN is optimized in an adversarial
learning manner to maximally fool the domain discriminator with the learnt
source and target representations. Extensive experiments are conducted on the
transfer from GTA5 (game videos) to Cityscapes (urban street scenes) on
semantic segmentation and our proposal achieves superior results when comparing
to state-of-the-art unsupervised adaptation techniques. More remarkably, we
obtain a new record: mIoU of 47.5% on BDDS (drive-cam videos) in an
unsupervised setting.
Memory Matching Networks for One-Shot Image Recognition
Authors: Qi Cai, Yingwei Pan, Ting Yao, Chenggang Yan, Tao Mei
Date: 2018-04-23 00:00:00
Proceeding: CVPR 2018 6
Tasks: One-Shot Learning, Philosophy

In this paper, we introduce the new ideas of augmenting Convolutional Neural
Networks (CNNs) with Memory and learning to learn the network parameters for
the unlabelled images on the fly in one-shot learning. Specifically, we present
Memory Matching Networks (MM-Net) --- a novel deep architecture that explores
the training procedure, following the philosophy that training and test
conditions must match. Technically, MM-Net writes the features of a set of
labelled images (support set) into memory and reads from memory when performing
inference to holistically leverage the knowledge in the set. Meanwhile, a
Contextual Learner employs the memory slots in a sequential manner to predict
the parameters of CNNs for unlabelled images. The whole architecture is trained
by once showing only a few examples per class and switching the learning from
minibatch to minibatch, which is tailored for one-shot learning when presented
with a few examples of new categories at test time. Unlike the conventional
one-shot learning approaches, our MM-Net could output one unified model
irrespective of the number of shots and categories. Extensive experiments are
conducted on two public datasets, i.e., Omniglot and \emph{mini}ImageNet, and
superior results are reported when compared to state-of-the-art approaches.
More remarkably, our MM-Net improves one-shot accuracy on Omniglot from 98.95%
to 99.28% and from 49.21% to 53.37% on \emph{mini}ImageNet.
PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and #hashtags
Authors: Ji Ho Park, Peng Xu, Pascale Fung
Date: 2018-04-23 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: BIG-bench Machine Learning, regression, Sentence

This paper describes our system that has been submitted to SemEval-2018 Task
1: Affect in Tweets (AIT) to solve five subtasks. We focus on modeling both
sentence and word level representations of emotion inside texts through large
distantly labeled corpora with emojis and hashtags. We transfer the emotional
knowledge by exploiting neural network models as feature extractors and use
these representations for traditional machine learning models such as support
vector regression (SVR) and logistic regression to solve the competition tasks.
Our system is placed among the Top3 for all subtasks we participated.
Deep Semantic Hashing with Generative Adversarial Networks
Authors: Zhaofan Qiu, Yingwei Pan, Ting Yao, Tao Mei
Date: 2018-04-23 00:00:00
Tasks: General Classification, Image Retrieval, Retrieval, Triplet

Hashing has been a widely-adopted technique for nearest neighbor search in
large-scale image retrieval tasks. Recent research has shown that leveraging
supervised information can lead to high quality hashing. However, the cost of
annotating data is often an obstacle when applying supervised hashing to a new
domain. Moreover, the results can suffer from the robustness problem as the
data at training and test stage could come from similar but different
distributions. This paper studies the exploration of generating synthetic data
through semi-supervised generative adversarial networks (GANs), which leverages
largely unlabeled and limited labeled training data to produce highly
compelling data with intrinsic invariance and global coherence, for better
understanding statistical structures of natural data. We demonstrate that the
above two limitations can be well mitigated by applying the synthetic data for
hashing. Specifically, a novel deep semantic hashing with GANs (DSH-GANs) is
presented, which mainly consists of four components: a deep convolution neural
networks (CNN) for learning image representations, an adversary stream to
distinguish synthetic images from real ones, a hash stream for encoding image
representations to hash codes and a classification stream. The whole
architecture is trained end-to-end by jointly optimizing three losses, i.e.,
adversarial loss to correct label of synthetic or real for each sample, triplet
ranking loss to preserve the relative similarity ordering in the input
real-synthetic triplets and classification loss to classify each sample
accurately. Extensive experiments conducted on both CIFAR-10 and NUS-WIDE image
benchmarks validate the capability of exploiting synthetic images for hashing.
Our framework also achieves superior results when compared to state-of-the-art
deep hash models.
Jointly Localizing and Describing Events for Dense Video Captioning
Authors: Yehao Li, Ting Yao, Yingwei Pan, Hongyang Chao, Tao Mei
Date: 2018-04-23 00:00:00
Proceeding: CVPR 2018 6
Tasks: Attribute, Dense Video Captioning, Descriptive, global-optimization, Sentence, valid, Video Captioning

Automatically describing a video with natural language is regarded as a
fundamental challenge in computer vision. The problem nevertheless is not
trivial especially when a video contains multiple events to be worthy of
mention, which often happens in real videos. A valid question is how to
temporally localize and then describe events, which is known as "dense video
captioning." In this paper, we present a novel framework for dense video
captioning that unifies the localization of temporal event proposals and
sentence generation of each proposal, by jointly training them in an end-to-end
manner. To combine these two worlds, we integrate a new design, namely
descriptiveness regression, into a single shot detection structure to infer the
descriptive complexity of each detected proposal via sentence generation. This
in turn adjusts the temporal locations of each event proposal. Our model
differs from existing dense video captioning methods since we propose a joint
and global optimization of detection and captioning, and the framework uniquely
capitalizes on an attribute-augmented video captioning architecture. Extensive
experiments are conducted on ActivityNet Captions dataset and our framework
shows clear improvements when compared to the state-of-the-art techniques. More
remarkably, we obtain a new record: METEOR of 12.96% on ActivityNet Captions
official test set.
Assessing the effect of advertising expenditures upon sales: a Bayesian structural time series model
Authors: Víctor Gallego, Pablo Suárez-García, Pablo Angulo, David Gómez-Ullate
Date: 2018-01-09 00:00:00
Tasks: Scheduling, Time Series, Time Series Analysis

We propose a robust implementation of the Nerlove--Arrow model using a Bayesian structural time series model to explain the relationship between advertising expenditures of a country-wide fast-food franchise network with its weekly sales. Thanks to the flexibility and modularity of the model, it is well suited to generalization to other markets or situations. Its Bayesian nature facilitates incorporating \emph{a priori} information (the manager's views), which can be updated with relevant data. This aspect of the model will be used to present a strategy of budget scheduling across time and channels.
Parameter Transfer Unit for Deep Neural Networks
Authors: Yinghua Zhang, Yu Zhang, Qiang Yang
Date: 2018-04-23 00:00:00

Parameters in deep neural networks which are trained on large-scale databases
can generalize across multiple domains, which is referred as "transferability".
Unfortunately, the transferability is usually defined as discrete states and it
differs with domains and network architectures. Existing works usually
heuristically apply parameter-sharing or fine-tuning, and there is no
principled approach to learn a parameter transfer strategy. To address the gap,
a parameter transfer unit (PTU) is proposed in this paper. The PTU learns a
fine-grained nonlinear combination of activations from both the source and the
target domain networks, and subsumes hand-crafted discrete transfer states. In
the PTU, the transferability is controlled by two gates which are artificial
neurons and can be learned from data. The PTU is a general and flexible module
which can be used in both CNNs and RNNs. Experiments are conducted with various
network architectures and multiple transfer domain pairs. Results demonstrate
the effectiveness of the PTU as it outperforms heuristic parameter-sharing and
fine-tuning in most settings.
NLITrans at SemEval-2018 Task 12: Transfer of Semantic Knowledge for Argument Comprehension
Authors: Tim Niven, Hung-Yu Kao
Date: 2018-04-23 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Position, Sentence, World Knowledge

The Argument Reasoning Comprehension Task requires significant language
understanding and complex reasoning over world knowledge. We focus on transfer
of a sentence encoder to bootstrap more complicated models given the small size
of the dataset. Our best model uses a pre-trained BiLSTM to encode input
sentences, learns task-specific features for the argument and warrants, then
performs independent argument-warrant matching. This model achieves mean test
set accuracy of 64.43%. Encoder transfer yields a significant gain to our best
model over random initialization. Independent warrant matching effectively
doubles the size of the dataset and provides additional regularization. We
demonstrate that regularization comes from ignoring statistical correlations
between warrant features and position. We also report an experiment with our
best model that only matches warrants to reasons, ignoring claims. Relatively
low performance degradation suggests that our model is not necessarily learning
the intended task.
State-Space Abstractions for Probabilistic Inference: A Systematic Review
Authors: Stefan Lüdtke, Max Schröder, Frank Krüger, Sebastian Bader, Thomas Kirste
Date: 2018-04-18 00:00:00
Tasks: Multiple Object Tracking, Object Tracking, Systematic Literature Review

Tasks such as social network analysis, human behavior recognition, or
modeling biochemical reactions, can be solved elegantly by using the
probabilistic inference framework. However, standard probabilistic inference
algorithms work at a propositional level, and thus cannot capture the
symmetries and redundancies that are present in these tasks. Algorithms that
exploit those symmetries have been devised in different research fields, for
example by the lifted inference-, multiple object tracking-, and modeling and
simulation-communities. The common idea, that we call state space abstraction,
is to perform inference over compact representations of sets of symmetric
states. Although they are concerned with a similar topic, the relationship
between these approaches has not been investigated systematically. This survey
provides the following contributions. We perform a systematic literature review
to outline the state of the art in probabilistic inference methods exploiting
symmetries. From an initial set of more than 4,000 papers, we identify 116
relevant papers. Furthermore, we provide new high-level categories that
classify the approaches, based on common properties of the approaches. The
research areas underlying each of the categories are introduced concisely.
Researchers from different fields that are confronted with a state space
explosion problem in a probabilistic system can use this classification to
identify possible solutions. Finally, based on this conceptualization, we
identify potentials for future research, as some relevant application domains
are not addressed by current approaches.
To Create What You Tell: Generating Videos from Captions
Authors: Yingwei Pan, Zhaofan Qiu, Ting Yao, Houqiang Li, Tao Mei
Date: 2018-04-23 00:00:00
Tasks: Philosophy

We are creating multimedia contents everyday and everywhere. While automatic
content generation has played a fundamental challenge to multimedia community
for decades, recent advances of deep learning have made this problem feasible.
For example, the Generative Adversarial Networks (GANs) is a rewarding approach
to synthesize images. Nevertheless, it is not trivial when capitalizing on GANs
to generate videos. The difficulty originates from the intrinsic structure
where a video is a sequence of visually coherent and semantically dependent
frames. This motivates us to explore semantic and temporal coherence in
designing GANs to generate videos. In this paper, we present a novel Temporal
GANs conditioning on Captions, namely TGANs-C, in which the input to the
generator network is a concatenation of a latent noise vector and caption
embedding, and then is transformed into a frame sequence with 3D
spatio-temporal convolutions. Unlike the naive discriminator which only judges
pairs as fake or real, our discriminator additionally notes whether the video
matches the correct caption. In particular, the discriminator network consists
of three discriminators: video discriminator classifying realistic videos from
generated ones and optimizes video-caption matching, frame discriminator
discriminating between real and fake frames and aligning frames with the
conditioning caption, and motion discriminator emphasizing the philosophy that
the adjacent frames in the generated videos should be smoothly connected as in
real ones. We qualitatively demonstrate the capability of our TGANs-C to
generate plausible videos conditioning on the given captions on two synthetic
datasets (SBMG and TBMG) and one real-world dataset (MSVD). Moreover,
quantitative experiments on MSVD are performed to validate our proposal via
Generative Adversarial Metric and human study.
On the Diachronic Stability of Irregularity in Inflectional Morphology
Authors: Ryan Cotterell, Christo Kirov, Mans Hulden, Jason Eisner
Date: 2018-04-23 00:00:00
Tasks: Relation

Many languages' inflectional morphological systems are replete with
irregulars, i.e., words that do not seem to follow standard inflectional rules.
In this work, we quantitatively investigate the conditions under which
irregulars can survive in a language over the course of time. Using recurrent
neural networks to simulate language learners, we test the diachronic relation
between frequency of words and their irregularity.
Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
Authors: Zhongliang Yang, Yongfeng Huang, Yiran Jiang, Yuxi Sun, Yu-Jin Zhan, Pengcheng Luo
Date: 2018-04-23 00:00:00

Automatically extracting useful information from electronic medical records
along with conducting disease diagnoses is a promising task for both clinical
decision support(CDS) and neural language processing(NLP). Most of the existing
systems are based on artificially constructed knowledge bases, and then
auxiliary diagnosis is done by rule matching. In this study, we present a
clinical intelligent decision approach based on Convolutional Neural
Networks(CNN), which can automatically extract high-level semantic information
of electronic medical records and then perform automatic diagnosis without
artificial construction of rules or knowledge bases. We use collected 18,590
copies of the real-world clinical electronic medical records to train and test
the proposed model. Experimental results show that the proposed model can
achieve 98.67\% accuracy and 96.02\% recall, which strongly supports that using
convolutional neural network to automatically learn high-level semantic
features of electronic medical records and then conduct assist diagnosis is
feasible and effective.
Network classification with applications to brain connectomics
Authors: Jesús D. Arroyo-Relión, Daniel Kessler, Elizaveta Levina, Stephan F. Taylor
Date: 2017-01-27 00:00:00
Tasks: Classification, Functional Connectivity, General Classification, Graph Classification

While statistical analysis of a single network has received a lot of
attention in recent years, with a focus on social networks, analysis of a
sample of networks presents its own challenges which require a different set of
analytic tools. Here we study the problem of classification of networks with
labeled nodes, motivated by applications in neuroimaging. Brain networks are
constructed from imaging data to represent functional connectivity between
regions of the brain, and previous work has shown the potential of such
networks to distinguish between various brain disorders, giving rise to a
network classification problem. Existing approaches tend to either treat all
edge weights as a long vector, ignoring the network structure, or focus on
graph topology as represented by summary measures while ignoring the edge
weights. Our goal is to design a classification method that uses both the
individual edge information and the network structure of the data in a
computationally efficient way, and that can produce a parsimonious and
interpretable representation of differences in brain connectivity patterns
between classes. We propose a graph classification method that uses edge
weights as predictors but incorporates the network nature of the data via
penalties that promote sparsity in the number of nodes, in addition to the
usual sparsity penalties that encourage selection of edges. We implement the
method via efficient convex optimization and provide a detailed analysis of
data from two fMRI studies of schizophrenia.
Progressive refinement: a method of coarse-to-fine image parsing using stacked network
Authors: Jiagao Hu, Zhengxing Sun, Yunhan Sun, Jinlong Shi
Date: 2018-04-23 00:00:00
Tasks: Face Parsing, Human Parsing, Segmentation, Semantic Segmentation

To parse images into fine-grained semantic parts, the complex fine-grained
elements will put it in trouble when using off-the-shelf semantic segmentation
networks. In this paper, for image parsing task, we propose to parse images
from coarse to fine with progressively refined semantic classes. It is achieved
by stacking the segmentation layers in a segmentation network several times.
The former segmentation module parses images at a coarser-grained level, and
the result will be feed to the following one to provide effective contextual
clues for the finer-grained parsing. To recover the details of small
structures, we add skip connections from shallow layers of the network to
fine-grained parsing modules. As for the network training, we merge classes in
groundtruth to get coarse-to-fine label maps, and train the stacked network
with these hierarchical supervision end-to-end. Our coarse-to-fine stacked
framework can be injected into many advanced neural networks to improve the
parsing results. Extensive evaluations on several public datasets including
face parsing and human parsing well demonstrate the superiority of our method.
A Comprehensive Implementation of Conceptual Spaces
Authors: Lucas Bechberger, Kai-Uwe Kühnberger
Date: 2017-07-14 00:00:00

The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points and concepts
are represented by regions in a (potentially) high-dimensional space. Based on
our recent formalization, we present a comprehensive implementation of the
conceptual spaces framework that is not only capable of representing concepts
with inter-domain correlations, but that also offers a variety of operations on
these concepts.
Constructing Locally Dense Point Clouds Using OpenSfM and ORB-SLAM2
Authors: Fouad Amer, Zixu Zhao, Siwei Tang, Wilfredo Torres
Date: 2018-04-23 00:00:00
Tasks: Position

This paper aims at finding a method to register two different point clouds
constructed by ORB-SLAM2 and OpenSfM. To do this, we post some tags with unique
textures in the scene and take videos and photos of that area. Then we take
short videos of only the tags to extract their features. By matching the ORB
feature of the tags with their corresponding features in the scene, it is then
possible to localize the position of these tags both in point clouds
constructed by ORB-SLAM2 and OpenSfM. Thus, the best transformation matrix
between two point clouds can be calculated, and the two point clouds can be
aligned.
BigDL: A Distributed Deep Learning Framework for Big Data
Authors: Jason Dai, Yiheng Wang, Xin Qiu, Ding Ding, Yao Zhang, Yanzhang Wang, Xianyan Jia, Cherry Zhang, Yan Wan, Zhichao Li, Jiao Wang, Shengsheng Huang, Zhongyuan Wu, Yang Wang, Yuhao Yang, Bowen She, Dongjie Shi, Qi Lu, Kai Huang, Guoqiong Song
Date: 2018-04-16 00:00:00
Tasks: Deep Learning, Fraud Detection, Management, Object Detection

This paper presents BigDL (a distributed deep learning framework for Apache Spark), which has been used by a variety of users in the industry for building deep learning applications on production big data platforms. It allows deep learning applications to run on the Apache Hadoop/Spark cluster so as to directly process the production data, and as a part of the end-to-end data analysis pipeline for deployment and management. Unlike existing deep learning frameworks, BigDL implements distributed, data parallel training directly on top of the functional compute model (with copy-on-write and coarse-grained operations) of Spark. We also share real-world experience and "war stories" of users that have adopted BigDL to address their challenges(i.e., how to easily build end-to-end data analysis and deep learning pipelines for their production data).