pwc-archive/papers-with-abstracts huggingface dataset:
Gaussian Material Synthesis
Authors: Károly Zsolnai-Fehér, Peter Wonka, Michael Wimmer
Date: 2018-04-23 00:00:00

We present a learning-based system for rapid mass-scale material synthesis
that is useful for novice and expert users alike. The user preferences are
learned via Gaussian Process Regression and can be easily sampled for new
recommendations. Typically, each recommendation takes 40-60 seconds to render
with global illumination, which makes this process impracticable for real-world
workflows. Our neural network eliminates this bottleneck by providing
high-quality image predictions in real time, after which it is possible to pick
the desired materials from a gallery and assign them to a scene in an intuitive
manner. Workflow timings against Disney's "principled" shader reveal that our
system scales well with the number of sought materials, thus empowering even
novice users to generate hundreds of high-quality material models without any
expertise in material modeling. Similarly, expert users experience a
significant decrease in the total modeling time when populating a scene with
materials. Furthermore, our proposed solution also offers controllable
recommendations and a novel latent space variant generation step to enable the
real-time fine-tuning of materials without requiring any domain expertise.
Multi-focus Image Fusion using dictionary learning and Low-Rank Representation
Authors: Hui Li, Xiao-Jun Wu
Date: 2018-04-23 00:00:00
Tasks: Dictionary Learning, Multi Focus Image Fusion, Representation Learning

Among the representation learning, the low-rank representation (LRR) is one
of the hot research topics in many fields, especially in image processing and
pattern recognition. Although LRR can capture the global structure, the ability
of local structure preservation is limited because LRR lacks dictionary
learning. In this paper, we propose a novel multi-focus image fusion method
based on dictionary learning and LRR to get a better performance in both global
and local structure. Firstly, the source images are divided into several
patches by sliding window technique. Then, the patches are classified according
to the Histogram of Oriented Gradient (HOG) features. And the sub-dictionaries
of each class are learned by K-singular value decomposition (K-SVD) algorithm.
Secondly, a global dictionary is constructed by combining these
sub-dictionaries. Then, we use the global dictionary in LRR to obtain the LRR
coefficients vector for each patch. Finally, the l_1-norm and choose-max fuse
strategy for each coefficients vector is adopted to reconstruct fused image
from the fused LRR coefficients and the global dictionary. Experimental results
demonstrate that the proposed method can obtain state-of-the-art performance in
both qualitative and quantitative evaluations compared with serval classical
methods and novel methods.The Code of our fusion method is available at
https://github.com/hli1221/imagefusion_dllrr
Distributed Distributional Deterministic Policy Gradients
Authors: Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy Lillicrap
Date: 2018-04-23 00:00:00
Proceeding: ICLR 2018 1
Tasks: continuous-control, Continuous Control, Reinforcement Learning, Reinforcement Learning (RL)

This work adopts the very successful distributional perspective on
reinforcement learning and adapts it to the continuous control setting. We
combine this within a distributed framework for off-policy learning in order to
develop what we call the Distributed Distributional Deep Deterministic Policy
Gradient algorithm, D4PG. We also combine this technique with a number of
additional, simple improvements such as the use of $N$-step returns and
prioritized experience replay. Experimentally we examine the contribution of
each of these individual components, and show how they interact, as well as
their combined contributions. Our results show that across a wide variety of
simple control tasks, difficult manipulation tasks, and a set of hard
obstacle-based locomotion tasks the D4PG algorithm achieves state of the art
performance.
Deep Facial Expression Recognition: A Survey
Authors: Shan Li, Weihong Deng
Date: 2018-04-23 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Survey

With the transition of facial expression recognition (FER) from
laboratory-controlled to challenging in-the-wild conditions and the recent
success of deep learning techniques in various fields, deep neural networks
have increasingly been leveraged to learn discriminative representations for
automatic FER. Recent deep FER systems generally focus on two important issues:
overfitting caused by a lack of sufficient training data and
expression-unrelated variations, such as illumination, head pose and identity
bias. In this paper, we provide a comprehensive survey on deep FER, including
datasets and algorithms that provide insights into these intrinsic problems.
First, we describe the standard pipeline of a deep FER system with the related
background knowledge and suggestions of applicable implementations for each
stage. We then introduce the available datasets that are widely used in the
literature and provide accepted data selection and evaluation principles for
these datasets. For the state of the art in deep FER, we review existing novel
deep neural networks and related training strategies that are designed for FER
based on both static images and dynamic image sequences, and discuss their
advantages and limitations. Competitive performances on widely used benchmarks
are also summarized in this section. We then extend our survey to additional
related issues and application scenarios. Finally, we review the remaining
challenges and corresponding opportunities in this field as well as future
directions for the design of robust deep FER systems.
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
Authors: Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le
Date: 2018-04-23 00:00:00
Proceeding: ICLR 2018 1
Tasks: Machine Translation, Question Answering, Reading Comprehension, Translation

Current end-to-end machine reading and question answering (Q\&A) models are
primarily based on recurrent neural networks (RNNs) with attention. Despite
their success, these models are often slow for both training and inference due
to the sequential nature of RNNs. We propose a new Q\&A architecture called
QANet, which does not require recurrent networks: Its encoder consists
exclusively of convolution and self-attention, where convolution models local
interactions and self-attention models global interactions. On the SQuAD
dataset, our model is 3x to 13x faster in training and 4x to 9x faster in
inference, while achieving equivalent accuracy to recurrent models. The
speed-up gain allows us to train the model with much more data. We hence
combine our model with data generated by backtranslation from a neural machine
translation model. On the SQuAD dataset, our single model, trained with
augmented data, achieves 84.6 F1 score on the test set, which is significantly
better than the best published F1 score of 81.8.
Semantic Parsing with Syntax- and Table-Aware SQL Generation
Authors: Yibo Sun, Duyu Tang, Nan Duan, Jianshu ji, Guihong Cao, Xiaocheng Feng, Bing Qin, Ting Liu, Ming Zhou
Date: 2018-04-23 00:00:00
Proceeding: ACL 2018 7
Tasks: Semantic Parsing

We present a generative model to map natural language questions into SQL
queries. Existing neural network based approaches typically generate a SQL
query word-by-word, however, a large portion of the generated results are
incorrect or not executable due to the mismatch between question words and
table contents. Our approach addresses this problem by considering the
structure of table and the syntax of SQL language. The quality of the generated
SQL query is significantly improved through (1) learning to replicate content
from column names, cells or SQL keywords; and (2) improving the generation of
WHERE clause by leveraging the column-cell relation. Experiments are conducted
on WikiSQL, a recently released dataset with the largest question-SQL pairs.
Our approach significantly improves the state-of-the-art execution accuracy
from 69.0% to 74.4%.
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
Authors: Takayuki Nishio, Ryo Yonetani
Date: 2018-04-23 00:00:00
Tasks: Edge-computing, Federated Learning, Privacy Preserving

We envision a mobile edge computing (MEC) framework for machine learning (ML)
technologies, which leverages distributed client data and computation resources
for training high-performance ML models while preserving client privacy. Toward
this future goal, this work aims to extend Federated Learning (FL), a
decentralized learning framework that enables privacy-preserving training of
models, to work with heterogeneous clients in a practical cellular network. The
FL protocol iteratively asks random clients to download a trainable model from
a server, update it with own data, and upload the updated model to the server,
while asking the server to aggregate multiple client updates to further improve
the model. While clients in this protocol are free from disclosing own private
data, the overall training process can become inefficient when some clients are
with limited computational resources (i.e. requiring longer update time) or
under poor wireless channel conditions (longer upload time). Our new FL
protocol, which we refer to as FedCS, mitigates this problem and performs FL
efficiently while actively managing clients based on their resource conditions.
Specifically, FedCS solves a client selection problem with resource
constraints, which allows the server to aggregate as many client updates as
possible and to accelerate performance improvement in ML models. We conducted
an experimental evaluation using publicly-available large-scale image datasets
to train deep neural networks on MEC environment simulations. The experimental
results show that FedCS is able to complete its training process in a
significantly shorter time compared to the original FL protocol.
Taskonomy: Disentangling Task Transfer Learning
Authors: Amir Zamir, Alexander Sax, William Shen, Leonidas Guibas, Jitendra Malik, Silvio Savarese
Date: 2018-04-23 00:00:00
Proceeding: CVPR 2018 6
Tasks: Multi-Task Learning, Transfer Learning

Do visual tasks have a relationship, or are they unrelated? For instance,
could having surface normals simplify estimating the depth of an image?
Intuition answers these questions positively, implying existence of a structure
among visual tasks. Knowing this structure has notable values; it is the
concept underlying transfer learning and provides a principled way for
identifying redundancies across tasks, e.g., to seamlessly reuse supervision
among related tasks or solve many tasks in one system without piling up the
complexity.
  We proposes a fully computational approach for modeling the structure of
space of visual tasks. This is done via finding (first and higher-order)
transfer learning dependencies across a dictionary of twenty six 2D, 2.5D, 3D,
and semantic tasks in a latent space. The product is a computational taxonomic
map for task transfer learning. We study the consequences of this structure,
e.g. nontrivial emerged relationships, and exploit them to reduce the demand
for labeled data. For example, we show that the total number of labeled
datapoints needed for solving a set of 10 tasks can be reduced by roughly 2/3
(compared to training independently) while keeping the performance nearly the
same. We provide a set of tools for computing and probing this taxonomical
structure including a solver that users can employ to devise efficient
supervision policies for their use cases.
Depth Information Guided Crowd Counting for Complex Crowd Scenes
Authors: Mingliang Xu, Zhaoyang Ge, Xiaoheng Jiang, Gaoge Cui, Pei Lv, Bing Zhou, Changsheng Xu
Date: 2018-03-03 00:00:00
Tasks: Crowd Counting

It is important to monitor and analyze crowd events for the sake of city
safety. In an EDOF (extended depth of field) image with a crowded scene, the
distribution of people is highly imbalanced. People far away from the camera
look much smaller and often occlude each other heavily, while people close to
the camera look larger. In such a case, it is difficult to accurately estimate
the number of people by using one technique. In this paper, we propose a Depth
Information Guided Crowd Counting (DigCrowd) method to deal with crowded EDOF
scenes. DigCrowd first uses the depth information of an image to segment the
scene into a far-view region and a near-view region. Then Digcrowd maps the
far-view region to its crowd density map and uses a detection method to count
the people in the near-view region. In addition, we introduce a new crowd
dataset that contains 1000 images. Experimental results demonstrate the
effectiveness of our DigCrowd method
Evaluating neural network explanation methods using hybrid documents and morphological agreement
Authors: Nina Poerner, Benjamin Roth, Hinrich Schütze
Date: 2018-01-19 00:00:00

The behavior of deep neural networks (DNNs) is hard to understand. This makes it necessary to explore post hoc explanation methods. We conduct the first comprehensive evaluation of explanation methods for NLP. To this end, we design two novel evaluation paradigms that cover two important classes of NLP problems: small context and large context problems. Both paradigms require no manual annotation and are therefore broadly applicable. We also introduce LIMSSE, an explanation method inspired by LIME that is designed for NLP. We show empirically that LIMSSE, LRP and DeepLIFT are the most effective explanation methods and recommend them for explaining DNNs in NLP.
Bilingual Embeddings with Random Walks over Multilingual Wordnets
Authors: J. Goikoetxea, A. Soroa, E. Agirre
Date: 2018-04-23 00:00:00
Tasks: Machine Translation, Word Embeddings, Word Similarity

Bilingual word embeddings represent words of two languages in the same space,
and allow to transfer knowledge from one language to the other without machine
translation. The main approach is to train monolingual embeddings first and
then map them using bilingual dictionaries. In this work, we present a novel
method to learn bilingual embeddings based on multilingual knowledge bases (KB)
such as WordNet. Our method extracts bilingual information from multilingual
wordnets via random walks and learns a joint embedding space in one go. We
further reinforce cross-lingual equivalence adding bilingual con- straints in
the loss function of the popular skipgram model. Our experiments involve twelve
cross-lingual word similarity and relatedness datasets in six lan- guage pairs
covering four languages, and show that: 1) random walks over mul- tilingual
wordnets improve results over just using dictionaries; 2) multilingual wordnets
on their own improve over text-based systems in similarity datasets; 3) the
good results are consistent for large wordnets (e.g. English, Spanish), smaller
wordnets (e.g. Basque) or loosely aligned wordnets (e.g. Italian); 4) the
combination of wordnets and text yields the best results, above mapping-based
approaches. Our method can be applied to richer KBs like DBpedia or Babel- Net,
and can be easily extended to multilingual embeddings. All software and
resources are open source.
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks
Authors: Diego Marcheggiani, Jasmijn Bastings, Ivan Titov
Date: 2018-04-23 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, Sentence, Translation

Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. In this work, we are the first to incorporate information about predicate-argument structure of source sentences (namely, semantic-role representations) into neural machine translation. We use Graph Convolutional Networks (GCNs) to inject a semantic bias into sentence encoders and achieve improvements in BLEU scores over the linguistic-agnostic and syntax-aware versions on the English--German language pair.
Block-Normalized Gradient Method: An Empirical Study for Training Deep Neural Network
Authors: Adams Wei Yu, Lei Huang, Qihang Lin, Ruslan Salakhutdinov, Jaime Carbonell
Date: 2017-07-16 00:00:00
Proceeding: ICLR 2018 1

In this paper, we propose a generic and simple strategy for utilizing
stochastic gradient information in optimization. The technique essentially
contains two consecutive steps in each iteration: 1) computing and normalizing
each block (layer) of the mini-batch stochastic gradient; 2) selecting
appropriate step size to update the decision variable (parameter) towards the
negative of the block-normalized gradient. We conduct extensive empirical
studies on various non-convex neural network optimization problems, including
multi-layer perceptron, convolution neural networks and recurrent neural
networks. The results indicate the block-normalized gradient can help
accelerate the training of neural networks. In particular, we observe that the
normalized gradient methods having constant step size with occasionally decay,
such as SGD with momentum, have better performance in the deep convolution
neural networks, while those with adaptive step sizes, such as Adam, perform
better in recurrent neural networks. Besides, we also observe this line of
methods can lead to solutions with better generalization properties, which is
confirmed by the performance improvement over strong baselines.
SFace: An Efficient Network for Face Detection in Large Scale Variations
Authors: Jianfeng Wang, Ye Yuan, Boxun Li, Gang Yu, Sun Jian
Date: 2018-04-18 00:00:00
Tasks: 4k, Face Detection, Face Recognition

Face detection serves as a fundamental research topic for many applications
like face recognition. Impressive progress has been made especially with the
recent development of convolutional neural networks. However, the issue of
large scale variations, which widely exists in high resolution images/videos,
has not been well addressed in the literature. In this paper, we present a
novel algorithm called SFace, which efficiently integrates the anchor-based
method and anchor-free method to address the scale issues. A new dataset called
4K-Face is also introduced to evaluate the performance of face detection with
extreme large scale variations. The SFace architecture shows promising results
on the new 4K-Face benchmarks. In addition, our method can run at 50 frames per
second (fps) with an accuracy of 80% AP on the standard WIDER FACE dataset,
which outperforms the state-of-art algorithms by almost one order of magnitude
in speed while achieves comparative performance.
Deep Generative Model for Joint Alignment and Word Representation
Authors: Miguel Rios, Wilker Aziz, Khalil Sima'an
Date: 2018-02-16 00:00:00
Proceeding: NAACL 2018 6
Tasks: Natural Language Inference, text similarity, Translation

This work exploits translation data as a source of semantically relevant
learning signal for models of word representation. In particular, we exploit
equivalence through translation as a form of distributed context and jointly
learn how to embed and align with a deep generative model. Our EmbedAlign model
embeds words in their complete observed context and learns by marginalisation
of latent lexical alignments. Besides, it embeds words as posterior probability
densities, rather than point estimates, which allows us to compare words in
context using a measure of overlap between distributions (e.g. KL divergence).
We investigate our model's performance on a range of lexical semantics tasks
achieving competitive results on several standard benchmarks including natural
language inference, paraphrasing, and text similarity.
Deep cross-domain building extraction for selective depth estimation from oblique aerial imagery
Authors: Boitumelo Ruf, Laurenz Thiel, Martin Weinmann
Date: 2018-04-23 00:00:00
Tasks: 3D Reconstruction, Depth Estimation, object-detection, Object Detection, Scene Understanding, Transfer Learning

With the technological advancements of aerial imagery and accurate 3d reconstruction of urban environments, more and more attention has been paid to the automated analyses of urban areas. In our work, we examine two important aspects that allow live analysis of building structures in city models given oblique aerial imagery, namely automatic building extraction with convolutional neural networks (CNNs) and selective real-time depth estimation from aerial imagery. We use transfer learning to train the Faster R-CNN method for real-time deep object detection, by combining a large ground-based dataset for urban scene understanding with a smaller number of images from an aerial dataset. We achieve an average precision (AP) of about 80% for the task of building extraction on a selected evaluation dataset. Our evaluation focuses on both dataset-specific learning and transfer learning. Furthermore, we present an algorithm that allows for multi-view depth estimation from aerial imagery in real-time. We adopt the semi-global matching (SGM) optimization strategy to preserve sharp edges at object boundaries. In combination with the Faster R-CNN, it allows a selective reconstruction of buildings, identified with regions of interest (RoIs), from oblique aerial imagery.
Representational Issues in the Debate on the Standard Model of the Mind
Authors: Antonio Chella, Marcello Frixione, Antonio Lieto
Date: 2018-04-23 00:00:00

In this paper we discuss some of the issues concerning the Memory and Content
aspects in the recent debate on the identification of a Standard Model of the
Mind (Laird, Lebiere, and Rosenbloom in press). In particular, we focus on the
representational models concerning the Declarative Memories of current
Cognitive Architectures (CAs). In doing so we outline some of the main problems
affecting the current CAs and suggest that the Conceptual Spaces, a
representational framework developed by Gardenfors, is worth-considering to
address such problems. Finally, we briefly analyze the alternative
representational assumptions employed in the three CAs constituting the current
baseline for the Standard Model (i.e. SOAR, ACT-R and Sigma). In doing so, we
point out the respective differences and discuss their implications in the
light of the analyzed problems.
MVTec D2S: Densely Segmented Supermarket Dataset
Authors: Patrick Follmann, Tobias Böttger, Philipp Härtinger, Rebecca König, Markus Ulrich
Date: 2018-04-23 00:00:00
Proceeding: ECCV 2018 9
Tasks: Data Augmentation, Diversity, Instance Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation

We introduce the Densely Segmented Supermarket (D2S) dataset, a novel
benchmark for instance-aware semantic segmentation in an industrial domain. It
contains 21,000 high-resolution images with pixel-wise labels of all object
instances. The objects comprise groceries and everyday products from 60
categories. The benchmark is designed such that it resembles the real-world
setting of an automatic checkout, inventory, or warehouse system. The training
images only contain objects of a single class on a homogeneous background,
while the validation and test sets are much more complex and diverse. To
further benchmark the robustness of instance segmentation methods, the scenes
are acquired with different lightings, rotations, and backgrounds. We ensure
that there are no ambiguities in the labels and that every instance is labeled
comprehensively. The annotations are pixel-precise and allow using crops of
single instances for articial data augmentation. The dataset covers several
challenges highly relevant in the field, such as a limited amount of training
data and a high diversity in the test and validation sets. The evaluation of
state-of-the-art object detection and instance segmentation methods on D2S
reveals significant room for improvement.