pwc-archive/papers-with-abstracts huggingface dataset:
3D Human Body Reconstruction from a Single Image via Volumetric Regression
Authors: Aaron S. Jackson, Chris Manafas, Georgios Tzimiropoulos
Date: 2018-09-11 00:00:00
Tasks: 3D geometry, regression

This paper proposes the use of an end-to-end Convolutional Neural Network for
direct reconstruction of the 3D geometry of humans via volumetric regression.
The proposed method does not require the fitting of a shape model and can be
trained to work from a variety of input types, whether it be landmarks, images
or segmentation masks. Additionally, non-visible parts, either self-occluded or
otherwise, are still reconstructed, which is not the case with depth map
regression. We present results that show that our method can handle both pose
variation and detailed reconstruction given appropriate datasets for training.
Non-blind Image Restoration Based on Convolutional Neural Network
Authors: Kazutaka Uchida, Masayuki Tanaka, Masatoshi Okutomi
Date: 2018-09-11 00:00:00
Tasks: Image Restoration

Blind image restoration processors based on convolutional neural network
(CNN) are intensively researched because of their high performance. However,
they are too sensitive to the perturbation of the degradation model. They
easily fail to restore the image whose degradation model is slightly different
from the trained degradation model. In this paper, we propose a non-blind
CNN-based image restoration processor, aiming to be robust against a
perturbation of the degradation model compared to the blind restoration
processor. Experimental comparisons demonstrate that the proposed non-blind
CNN-based image restoration processor can robustly restore images compared to
existing blind CNN-based image restoration processors.
Does it care what you asked? Understanding Importance of Verbs in Deep Learning QA System
Authors: Barbara Rychalska, Dominika Basaj, Przemyslaw Biecek, Anna Wroblewska
Date: 2018-09-11 00:00:00
Proceeding: WS 2018 11

In this paper we present the results of an investigation of the importance of
verbs in a deep learning QA system trained on SQuAD dataset. We show that main
verbs in questions carry little influence on the decisions made by the system -
in over 90% of researched cases swapping verbs for their antonyms did not
change system decision. We track this phenomenon down to the insides of the
net, analyzing the mechanism of self-attention and values contained in hidden
layers of RNN. Finally, we recognize the characteristics of the SQuAD dataset
as the source of the problem. Our work refers to the recently popular topic of
adversarial examples in NLP, combined with investigating deep net structure.
Visualization of High-dimensional Scalar Functions Using Principal Parameterizations
Authors: Rafael Ballester-Ripoll, Renato Pajarola
Date: 2018-09-11 00:00:00
Tasks: Dimensionality Reduction, Sensitivity, Tensor Decomposition, Vocal Bursts Intensity Prediction

Insightful visualization of multidimensional scalar fields, in particular
parameter spaces, is key to many fields in computational science and
engineering. We propose a principal component-based approach to visualize such
fields that accurately reflects their sensitivity to input parameters. The
method performs dimensionality reduction on the vast $L^2$ Hilbert space formed
by all possible partial functions (i.e., those defined by fixing one or more
input parameters to specific values), which are projected to low-dimensional
parameterized manifolds such as 3D curves, surfaces, and ensembles thereof. Our
mapping provides a direct geometrical and visual interpretation in terms of
Sobol's celebrated method for variance-based sensitivity analysis. We
furthermore contribute a practical realization of the proposed method by means
of tensor decomposition, which enables accurate yet interactive integration and
multilinear principal component analysis of high-dimensional models.
How much should you ask? On the question structure in QA systems
Authors: Dominika Basaj, Barbara Rychalska, Przemyslaw Biecek, Anna Wroblewska
Date: 2018-09-11 00:00:00
Tasks: Question Answering, valid

Datasets that boosted state-of-the-art solutions for Question Answering (QA)
systems prove that it is possible to ask questions in natural language manner.
However, users are still used to query-like systems where they type in keywords
to search for answer. In this study we validate which parts of questions are
essential for obtaining valid answer. In order to conclude that, we take
advantage of LIME - a framework that explains prediction by local
approximation. We find that grammar and natural language is disregarded by QA.
State-of-the-art model can answer properly even if 'asked' only with a few
words with high coefficients calculated with LIME. According to our knowledge,
it is the first time that QA model is being explained by LIME.
Deep Asymmetric Networks with a Set of Node-wise Variant Activation Functions
Authors: Jinhyeok Jang, Hyunjoong Cho, Jaehong Kim, Jaeyeon Lee, Seungjoon Yang
Date: 2018-09-11 00:00:00

This work presents deep asymmetric networks with a set of node-wise variant activation functions. The nodes' sensitivities are affected by activation function selections such that the nodes with smaller indices become increasingly more sensitive. As a result, features learned by the nodes are sorted by the node indices in the order of their importance. Asymmetric networks not only learn input features but also the importance of those features. Nodes of lesser importance in asymmetric networks can be pruned to reduce the complexity of the networks, and the pruned networks can be retrained without incurring performance losses. We validate the feature-sorting property using both shallow and deep asymmetric networks as well as deep asymmetric networks transferred from famous networks.
Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions
Authors: M. Wagner, H. Basevi, R. Shetty, W. Li, M. Malinowski, M. Fritz, A. Leonardis
Date: 2018-09-11 00:00:00
Tasks: Question Answering, Scene Understanding

In-depth scene descriptions and question answering tasks have greatly
increased the scope of today's definition of scene understanding. While such
tasks are in principle open ended, current formulations primarily focus on
describing only the current state of the scenes under consideration. In
contrast, in this paper, we focus on the future states of the scenes which are
also conditioned on actions. We posit this as a question answering task, where
an answer has to be given about a future scene state, given observations of the
current scene, and a question that includes a hypothetical action. Our solution
is a hybrid model which integrates a physics engine into a question answering
architecture in order to anticipate future scene states resulting from
object-object interactions caused by an action. We demonstrate first results on
this challenging new problem and compare to baselines, where we outperform
fully data-driven end-to-end learning approaches.
Bio-LSTM: A Biomechanically Inspired Recurrent Neural Network for 3D Pedestrian Pose and Gait Prediction
Authors: Xiaoxiao Du, Ram Vasudevan, Matthew Johnson-Roberson
Date: 2018-09-11 00:00:00
Tasks: Autonomous Driving

In applications such as autonomous driving, it is important to understand, infer, and anticipate the intention and future behavior of pedestrians. This ability allows vehicles to avoid collisions and improve ride safety and quality. This paper proposes a biomechanically inspired recurrent neural network (Bio-LSTM) that can predict the location and 3D articulated body pose of pedestrians in a global coordinate frame, given 3D poses and locations estimated in prior frames with inaccuracy. The proposed network is able to predict poses and global locations for multiple pedestrians simultaneously, for pedestrians up to 45 meters from the cameras (urban intersection scale). The outputs of the proposed network are full-body 3D meshes represented in Skinned Multi-Person Linear (SMPL) model parameters. The proposed approach relies on a novel objective function that incorporates the periodicity of human walking (gait), the mirror symmetry of the human body, and the change of ground reaction forces in a human gait cycle. This paper presents prediction results on the PedX dataset, a large-scale, in-the-wild data set collected at real urban intersections with heavy pedestrian traffic. Results show that the proposed network can successfully learn the characteristics of pedestrian gait and produce accurate and consistent 3D pose predictions.
Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding
Authors: Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Michael C. Mozer, Chris Pal, Yoshua Bengio
Date: 2018-09-11 00:00:00
Tasks: Temporal Sequences

Learning long-term dependencies in extended temporal sequences requires
credit assignment to events far back in the past. The most common method for
training recurrent neural networks, back-propagation through time (BPTT),
requires credit information to be propagated backwards through every single
step of the forward computation, potentially over thousands or millions of time
steps. This becomes computationally expensive or even infeasible when used with
long sequences. Importantly, biological brains are unlikely to perform such
detailed reverse replay over very long sequences of internal states (consider
days, months, or years.) However, humans are often reminded of past memories or
mental states which are associated with the current mental state. We consider
the hypothesis that such memory associations between past and present could be
used for credit assignment through arbitrarily long sequences, propagating the
credit assigned to the current state to the associated past state. Based on
this principle, we study a novel algorithm which only back-propagates through a
few of these temporal skip connections, realized by a learned attention
mechanism that associates current states with relevant past states. We
demonstrate in experiments that our method matches or outperforms regular BPTT
and truncated BPTT in tasks involving particularly long-term dependencies, but
without requiring the biologically implausible backward replay through the
whole history of states. Additionally, we demonstrate that the proposed method
transfers to longer sequences significantly better than LSTMs trained with BPTT
and LSTMs trained with full self-attention.
Evaluating Multimodal Representations on Sentence Similarity: vSTS, Visual Semantic Textual Similarity Dataset
Authors: Oier Lopez de Lacalle, Aitor Soroa, Eneko Agirre
Date: 2018-09-11 00:00:00
Tasks: Semantic Textual Similarity, Sentence, Sentence Similarity, valid

In this paper we introduce vSTS, a new dataset for measuring textual
similarity of sentences using multimodal information. The dataset is comprised
by images along with its respectively textual captions. We describe the dataset
both quantitatively and qualitatively, and claim that it is a valid gold
standard for measuring automatic multimodal textual similarity systems. We also
describe the initial experiments combining the multimodal information.
A Joint Model of Conversational Discourse and Latent Topics on Microblogs
Authors: Jing Li, Yan Song, Zhongyu Wei, Kam-Fai Wong
Date: 2018-09-11 00:00:00
Tasks: Topic Models

Conventional topic models are ineffective for topic extraction from microblog
messages, because the data sparseness exhibited in short messages lacking
structure and contexts results in poor message-level word co-occurrence
patterns. To address this issue, we organize microblog messages as conversation
trees based on their reposting and replying relations, and propose an
unsupervised model that jointly learns word distributions to represent: 1)
different roles of conversational discourse, 2) various latent topics in
reflecting content information. By explicitly distinguishing the probabilities
of messages with varying discourse roles in containing topical words, our model
is able to discover clusters of discourse words that are indicative of topical
content. In an automatic evaluation on large-scale microblog corpora, our joint
model yields topics with better coherence scores than competitive topic models
from previous studies. Qualitative analysis on model outputs indicates that our
model induces meaningful representations for both discourse and topics. We
further present an empirical study on microblog summarization based on the
outputs of our joint model. The results show that the jointly modeled discourse
and topic representations can effectively indicate summary-worthy content in
microblog conversations.
Learning Scripts as Hidden Markov Models
Authors: J. Walker Orr, Prasad Tadepalli, Janardhan Rao Doppa, Xiaoli Fern, Thomas G. Dietterich
Date: 2018-09-11 00:00:00
Tasks: Clustering

Scripts have been proposed to model the stereotypical event sequences found
in narratives. They can be applied to make a variety of inferences including
filling gaps in the narratives and resolving ambiguous references. This paper
proposes the first formal framework for scripts based on Hidden Markov Models
(HMMs). Our framework supports robust inference and learning algorithms, which
are lacking in previous clustering models. We develop an algorithm for
structure and parameter learning based on Expectation Maximization and evaluate
it on a number of natural datasets. The results show that our algorithm is
superior to several informed baselines for predicting missing events in partial
observation sequences.
Unbiasing Semantic Segmentation For Robot Perception using Synthetic Data Feature Transfer
Authors: Jonathan C Balloch, Varun Agrawal, Irfan Essa, Sonia Chernova
Date: 2018-09-11 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Robot perception systems need to perform reliable image segmentation in
real-time on noisy, raw perception data. State-of-the-art segmentation
approaches use large CNN models and carefully constructed datasets; however,
these models focus on accuracy at the cost of real-time inference. Furthermore,
the standard semantic segmentation datasets are not large enough for training
CNNs without augmentation and are not representative of noisy, uncurated robot
perception data. We propose improving the performance of real-time segmentation
frameworks on robot perception data by transferring features learned from
synthetic segmentation data. We show that pretraining real-time segmentation
architectures with synthetic segmentation data instead of ImageNet improves
fine-tuning performance by reducing the bias learned in pretraining and closing
the \textit{transfer gap} as a result. Our experiments show that our real-time
robot perception models pretrained on synthetic data outperform those
pretrained on ImageNet for every scale of fine-tuning data examined. Moreover,
the degree to which synthetic pretraining outperforms ImageNet pretraining
increases as the availability of robot data decreases, making our approach
attractive for robotics domains where dataset collection is hard and/or
expensive.
Deep Interest Evolution Network for Click-Through Rate Prediction
Authors: Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, Kun Gai
Date: 2018-09-11 00:00:00
Tasks: Click-Through Rate Prediction, Deep Learning, Prediction

Easy-to-use,Modular and Extendible package of deep-learning based CTR models.DeepFM,DeepInterestNetwork(DIN),DeepInterestEvolutionNetwork(DIEN),DeepCrossNetwork(DCN),AttentionalFactorizationMachine(AFM),Neural Factorization Machine(NFM),AutoInt
Temporal-Spatial Mapping for Action Recognition
Authors: Xiaolin Song, Cuiling Lan, Wen-Jun Zeng, Junliang Xing, Jingyu Yang, Xiaoyan Sun
Date: 2018-09-11 00:00:00
Tasks: Action Recognition, image-classification, Image Classification, object-detection, Object Detection, Temporal Action Localization

Deep learning models have enjoyed great success for image related computer
vision tasks like image classification and object detection. For video related
tasks like human action recognition, however, the advancements are not as
significant yet. The main challenge is the lack of effective and efficient
models in modeling the rich temporal spatial information in a video. We
introduce a simple yet effective operation, termed Temporal-Spatial Mapping
(TSM), for capturing the temporal evolution of the frames by jointly analyzing
all the frames of a video. We propose a video level 2D feature representation
by transforming the convolutional features of all frames to a 2D feature map,
referred to as VideoMap. With each row being the vectorized feature
representation of a frame, the temporal-spatial features are compactly
represented, while the temporal dynamic evolution is also well embedded. Based
on the VideoMap representation, we further propose a temporal attention model
within a shallow convolutional neural network to efficiently exploit the
temporal-spatial dynamics. The experiment results show that the proposed scheme
achieves the state-of-the-art performance, with 4.2% accuracy gain over
Temporal Segment Network (TSN), a competing baseline method, on the challenging
human action benchmark dataset HMDB51.
Comparing Computing Platforms for Deep Learning on a Humanoid Robot
Authors: Alexander Biddulph, Trent Houlistion, Alexandre Mendes, Stephan K. Chalup
Date: 2018-09-11 00:00:00
Tasks: Benchmarking, CPU, Deep Learning, GPU, Pedestrian Detection

The goal of this study is to test two different computing platforms with
respect to their suitability for running deep networks as part of a humanoid
robot software system. One of the platforms is the CPU-centered Intel NUC7i7BNH
and the other is a NVIDIA Jetson TX2 system that puts more emphasis on GPU
processing. The experiments addressed a number of benchmarking tasks including
pedestrian detection using deep neural networks. Some of the results were
unexpected but demonstrate that platforms exhibit both advantages and
disadvantages when taking computational performance and electrical power
requirements of such a system into account.
Topic Memory Networks for Short Text Classification
Authors: Jichuan Zeng, Jing Li, Yan Song, Cuiyun Gao, Michael R. Lyu, Irwin King
Date: 2018-09-11 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Classification, General Classification, text-classification, Text Classification

Many classification models work poorly on short texts due to data sparsity.
To address this issue, we propose topic memory networks for short text
classification with a novel topic memory mechanism to encode latent topic
representations indicative of class labels. Different from most prior work that
focuses on extending features with external knowledge or pre-trained topics,
our model jointly explores topic inference and text classification with memory
networks in an end-to-end manner. Experimental results on four benchmark
datasets show that our model outperforms state-of-the-art models on short text
classification, meanwhile generates coherent topics.
New models for symbolic data analysis
Authors: Boris Beranger, Huan Lin, Scott A. Sisson
Date: 2018-09-11 00:00:00

Symbolic data analysis (SDA) is an emerging area of statistics concerned with understanding and modelling data that takes distributional form (i.e. symbols), such as random lists, intervals and histograms. It was developed under the premise that the statistical unit of interest is the symbol, and that inference is required at this level. Here we consider a different perspective, which opens a new research direction in the field of SDA. We assume that, as with a standard statistical analysis, inference is required at the level of individual-level data. However, the individual-level data are aggregated into symbols - group-based distributional-valued summaries - prior to the analysis. In this way, large and complex datasets can be reduced to a smaller number of distributional summaries, that may be analysed more efficiently than the original dataset. As such, we develop SDA techniques as a new approach for the analysis of big data. In particular we introduce a new general method for constructing likelihood functions for symbolic data based on a desired probability model for the underlying measurement-level data, while only observing the distributional summaries. This approach opens the door for new classes of symbol design and construction, in addition to developing SDA as a viable tool to enable and improve upon classical data analyses, particularly for very large and complex datasets. We illustrate this new direction for SDA research through several real and simulated data analyses.
Neural Rendering and Reenactment of Human Actor Videos
Authors: Lingjie Liu, Weipeng Xu, Michael Zollhoefer, Hyeongwoo Kim, Florian Bernard, Marc Habermann, Wenping Wang, Christian Theobalt
Date: 2018-09-11 00:00:00
Tasks: Generative Adversarial Network, Image Generation, Neural Rendering

We propose a method for generating video-realistic animations of real humans under user control. In contrast to conventional human character rendering, we do not require the availability of a production-quality photo-realistic 3D model of the human, but instead rely on a video sequence in conjunction with a (medium-quality) controllable 3D template model of the person. With that, our approach significantly reduces production cost compared to conventional rendering approaches based on production-quality 3D models, and can also be used to realistically edit existing videos. Technically, this is achieved by training a neural network that translates simple synthetic images of a human character into realistic imagery. For training our networks, we first track the 3D motion of the person in the video using the template model, and subsequently generate a synthetically rendered version of the video. These images are then used to train a conditional generative adversarial network that translates synthetic images of the 3D model into realistic imagery of the human. We evaluate our method for the reenactment of another person that is tracked in order to obtain the motion data, and show video results generated from artist-designed skeleton motion. Our results outperform the state-of-the-art in learning-based human image synthesis. Project page: http://gvv.mpi-inf.mpg.de/projects/wxu/HumanReenactment/
Resource-driven Substructural Defeasible Logic
Authors: Francesco Olivieri, Guido Governatori, Matteo Cristani, Nick van Beest, Silvano Colombo-Tosatto
Date: 2018-09-11 00:00:00

Linear Logic and Defeasible Logic have been adopted to formalise different
features relevant to agents: consumption of resources, and reasoning with
exceptions. We propose a framework to combine sub-structural features,
corresponding to the consumption of resources, with defeasibility aspects, and
we discuss the design choices for the framework.
An Efficient ADMM-Based Algorithm to Nonconvex Penalized Support Vector Machines
Authors: Lei Guan, Linbo Qiao, Dongsheng Li, Tao Sun, Keshi Ge, Xicheng Lu
Date: 2018-09-11 00:00:00
Tasks: General Classification, Variable Selection

Support vector machines (SVMs) with sparsity-inducing nonconvex penalties
have received considerable attentions for the characteristics of automatic
classification and variable selection. However, it is quite challenging to
solve the nonconvex penalized SVMs due to their nondifferentiability,
nonsmoothness and nonconvexity. In this paper, we propose an efficient
ADMM-based algorithm to the nonconvex penalized SVMs. The proposed algorithm
covers a large class of commonly used nonconvex regularization terms including
the smooth clipped absolute deviation (SCAD) penalty, minimax concave penalty
(MCP), log-sum penalty (LSP) and capped-$\ell_1$ penalty. The computational
complexity analysis shows that the proposed algorithm enjoys low computational
cost. Moreover, the convergence of the proposed algorithm is guaranteed.
Extensive experimental evaluations on five benchmark datasets demonstrate the
superior performance of the proposed algorithm to other three state-of-the-art
approaches.