pwc-archive/papers-with-abstracts huggingface dataset:
3D Trajectory Reconstruction of Dynamic Objects Using Planarity Constraints
Authors: Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, Rainer Stiefelhagen
Date: 2017-11-16 00:00:00
Tasks: Object, Optical Flow Estimation, Semantic Segmentation

We present a method to reconstruct the three-dimensional trajectory of a
moving instance of a known object category in monocular video data. We track
the two-dimensional shape of objects on pixel level exploiting instance-aware
semantic segmentation techniques and optical flow cues. We apply Structure from
Motion techniques to object and background images to determine for each frame
camera poses relative to object instances and background structures. By
combining object and background camera pose information, we restrict the object
trajectory to a one-parameter family of possible solutions. We compute a ground
representation by fusing background structures and corresponding semantic
segmentations. This allows us to determine an object trajectory consistent to
image observations and reconstructed environment model. Our method is robust to
occlusion and handles temporarily stationary objects. We show qualitative
results using drone imagery. Due to the lack of suitable benchmark datasets we
present a new dataset to evaluate the quality of reconstructed
three-dimensional object trajectories. The video sequences contain vehicles in
urban areas and are rendered using the path-tracing render engine Cycles to
achieve realistic results. We perform a quantitative evaluation of the
presented approach using this dataset. Our algorithm achieves an average
reconstruction-to-ground-truth distance of 0.31 meter.
Effective Tensor Sketching via Sparsification
Authors: Dong Xia, Ming Yuan
Date: 2017-10-31 00:00:00

In this paper, we investigate effective sketching schemes via sparsification
for high dimensional multilinear arrays or tensors. More specifically, we
propose a novel tensor sparsification algorithm that retains a subset of the
entries of a tensor in a judicious way, and prove that it can attain a given
level of approximation accuracy in terms of tensor spectral norm with a much
smaller sample complexity when compared with existing approaches. In
particular, we show that for a $k$th order $d\times\cdots\times d$ cubic tensor
of {\it stable rank} $r_s$, the sample size requirement for achieving a
relative error $\varepsilon$ is, up to a logarithmic factor, of the order
$r_s^{1/2} d^{k/2} /\varepsilon$ when $\varepsilon$ is relatively large, and
$r_s d /\varepsilon^2$ and essentially optimal when $\varepsilon$ is
sufficiently small. It is especially noteworthy that the sample size
requirement for achieving a high accuracy is of an order independent of $k$. To
further demonstrate the utility of our techniques, we also study how higher
order singular value decomposition (HOSVD) of large tensors can be efficiently
approximated via sparsification.
The implementation of a Deep Recurrent Neural Network Language Model on a Xilinx FPGA
Authors: Yufeng Hao, Steven Quigley
Date: 2017-10-26 00:00:00
Tasks: CPU, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Recently, FPGA has been increasingly applied to problems such as speech
recognition, machine learning, and cloud computation such as the Bing search
engine used by Microsoft. This is due to FPGAs great parallel computation
capacity as well as low power consumption compared to general purpose
processors. However, these applications mainly focus on large scale FPGA
clusters which have an extreme processing power for executing massive matrix or
convolution operations but are unsuitable for portable or mobile applications.
This paper describes research on single-FPGA platform to explore the
applications of FPGAs in these fields. In this project, we design a Deep
Recurrent Neural Network (DRNN) Language Model (LM) and implement a hardware
accelerator with AXI Stream interface on a PYNQ board which is equipped with a
XILINX ZYNQ SOC XC7Z020 1CLG400C. The PYNQ has not only abundant programmable
logic resources but also a flexible embedded operation system, which makes it
suitable to be applied in the natural language processing field. We design the
DRNN language model with Python and Theano, train the model on a CPU platform,
and deploy the model on a PYNQ board to validate the model with Jupyter
notebook. Meanwhile, we design the hardware accelerator with Overlay, which is
a kind of hardware library on PYNQ, and verify the acceleration effect on the
PYNQ board. Finally, we have found that the DRNN language model can be deployed
on the embedded system smoothly and the Overlay accelerator with AXI Stream
interface performs at 20 GOPS processing throughput, which constitutes a 70.5X
and 2.75X speed up compared to the work in Ref.30 and Ref.31 respectively.
Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
Authors: Florian Kluger, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn
Date: 2017-07-08 00:00:00
Tasks: Camera Calibration, Horizon Line Estimation

We present a novel approach for vanishing point detection from uncalibrated
monocular images. In contrast to state-of-the-art, we make no a priori
assumptions about the observed scene. Our method is based on a convolutional
neural network (CNN) which does not use natural images, but a Gaussian sphere
representation arising from an inverse gnomonic projection of lines detected in
an image. This allows us to rely on synthetic data for training, eliminating
the need for labelled images. Our method achieves competitive performance on
three horizon estimation benchmark datasets. We further highlight some
additional use cases for which our vanishing point detection algorithm can be
used.
DR2-Net: Deep Residual Reconstruction Network for Image Compressive Sensing
Authors: Hantao Yao, Feng Dai, Dongming Zhang, Yike Ma, Shiliang Zhang, Yongdong Zhang, Qi Tian
Date: 2017-02-19 00:00:00
Tasks: Compressive Sensing, Image Reconstruction

Most traditional algorithms for compressive sensing image reconstruction
suffer from the intensive computation. Recently, deep learning-based
reconstruction algorithms have been reported, which dramatically reduce the
time complexity than iterative reconstruction algorithms. In this paper, we
propose a novel \textbf{D}eep \textbf{R}esidual \textbf{R}econstruction Network
(DR$^{2}$-Net) to reconstruct the image from its Compressively Sensed (CS)
measurement. The DR$^{2}$-Net is proposed based on two observations: 1) linear
mapping could reconstruct a high-quality preliminary image, and 2) residual
learning could further improve the reconstruction quality. Accordingly,
DR$^{2}$-Net consists of two components, \emph{i.e.,} linear mapping network
and residual network, respectively. Specifically, the fully-connected layer in
neural network implements the linear mapping network. We then expand the linear
mapping network to DR$^{2}$-Net by adding several residual learning blocks to
enhance the preliminary image. Extensive experiments demonstrate that the
DR$^{2}$-Net outperforms traditional iterative methods and recent deep
learning-based methods by large margins at measurement rates 0.01, 0.04, 0.1,
and 0.25, respectively. The code of DR$^{2}$-Net has been released on:
https://github.com/coldrainyht/caffe\_dr2
Deep Representation Learning with Part Loss for Person Re-Identification
Authors: Hantao Yao, Shiliang Zhang, Yongdong Zhang, Jintao Li, Qi Tian
Date: 2017-07-04 00:00:00
Tasks: Classification, General Classification, Person Re-Identification, Representation Learning

Learning discriminative representations for unseen person images is critical
for person Re-Identification (ReID). Most of current approaches learn deep
representations in classification tasks, which essentially minimize the
empirical classification risk on the training set. As shown in our experiments,
such representations commonly focus on several body parts discriminative to the
training set, rather than the entire human body. Inspired by the structural
risk minimization principle in SVM, we revise the traditional deep
representation learning procedure to minimize both the empirical classification
risk and the representation learning risk. The representation learning risk is
evaluated by the proposed part loss, which automatically generates several
parts for an image, and computes the person classification loss on each part
separately. Compared with traditional global classification loss,
simultaneously considering multiple part loss enforces the deep network to
focus on the entire human body and learn discriminative representations for
different parts. Experimental results on three datasets, i.e., Market1501,
CUHK03, VIPeR, show that our representation outperforms the existing deep
representations.
The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks
Authors: Joos Behncke, Robin Tibor Schirrmeister, Wolfram Burgard, Tonio Ball
Date: 2017-11-16 00:00:00
Tasks: EEG, Eeg Decoding, Electroencephalogram (EEG), Management

The importance of robotic assistive devices grows in our work and everyday
life. Cooperative scenarios involving both robots and humans require safe
human-robot interaction. One important aspect here is the management of robot
errors, including fast and accurate online robot-error detection and
correction. Analysis of brain signals from a human interacting with a robot may
help identifying robot errors, but accuracies of such analyses have still
substantial space for improvement. In this paper we evaluate whether a novel
framework based on deep convolutional neural networks (deep ConvNets) could
improve the accuracy of decoding robot errors from the EEG of a human observer,
both during an object grasping and a pouring task. We show that deep ConvNets
reached significantly higher accuracies than both regularized Linear
Discriminant Analysis (rLDA) and filter bank common spatial patterns (FB-CSP)
combined with rLDA, both widely used EEG classifiers. Deep ConvNets reached
mean accuracies of 75% +/- 9 %, rLDA 65% +/- 10% and FB-CSP + rLDA 63% +/- 6%
for decoding of erroneous vs. correct trials. Visualization of the time-domain
EEG features learned by the ConvNets to decode errors revealed spatiotemporal
patterns that reflected differences between the two experimental paradigms.
Across subjects, ConvNet decoding accuracies were significantly correlated with
those obtained with rLDA, but not CSP, indicating that in the present context
ConvNets behaved more 'rLDA-like' (but consistently better), while in a
previous decoding study with another task but the same ConvNet architecture, it
was found to behave more 'CSP-like'. Our findings thus provide further support
for the assumption that deep ConvNets are a versatile addition to the existing
toolbox of EEG decoding techniques, and we discuss steps how ConvNet EEG
decoding performance could be further optimized.
Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception
Authors: Ruofei Ouyang, Kian Hsiang Low
Date: 2017-11-16 00:00:00
Tasks: Transfer Learning

This paper presents novel Gaussian process decentralized data fusion
algorithms exploiting the notion of agent-centric support sets for distributed
cooperative perception of large-scale environmental phenomena. To overcome the
limitations of scale in existing works, our proposed algorithms allow every
mobile sensing agent to choose a different support set and dynamically switch
to another during execution for encapsulating its own data into a local summary
that, perhaps surprisingly, can still be assimilated with the other agents'
local summaries (i.e., based on their current choices of support sets) into a
globally consistent summary to be used for predicting the phenomenon. To
achieve this, we propose a novel transfer learning mechanism for a team of
agents capable of sharing and transferring information encapsulated in a
summary based on a support set to that utilizing a different support set with
some loss that can be theoretically bounded and analyzed. To alleviate the
issue of information loss accumulating over multiple instances of transfer
learning, we propose a new information sharing mechanism to be incorporated
into our algorithms in order to achieve memory-efficient lazy transfer
learning. Empirical evaluation on real-world datasets show that our algorithms
outperform the state-of-the-art methods.
Integrated Face Analytics Networks through Cross-Dataset Hybrid Training
Authors: Jianshu Li, Shengtao Xiao, Fang Zhao, Jian Zhao, Jianan Li, Jiashi Feng, Shuicheng Yan, Terence Sim
Date: 2017-11-16 00:00:00
Tasks: Emotion Recognition, Face Alignment, Face Parsing, Facial Emotion Recognition

Face analytics benefits many multimedia applications. It consists of a number
of tasks, such as facial emotion recognition and face parsing, and most
existing approaches generally treat these tasks independently, which limits
their deployment in real scenarios. In this paper we propose an integrated Face
Analytics Network (iFAN), which is able to perform multiple tasks jointly for
face analytics with a novel carefully designed network architecture to fully
facilitate the informative interaction among different tasks. The proposed
integrated network explicitly models the interactions between tasks so that the
correlations between tasks can be fully exploited for performance boost. In
addition, to solve the bottleneck of the absence of datasets with comprehensive
training data for various tasks, we propose a novel cross-dataset hybrid
training strategy. It allows "plug-in and play" of multiple datasets annotated
for different tasks without the requirement of a fully labeled common dataset
for all the tasks. We experimentally show that the proposed iFAN achieves
state-of-the-art performance on multiple face analytics tasks using a single
integrated model. Specifically, iFAN achieves an overall F-score of 91.15% on
the Helen dataset for face parsing, a normalized mean error of 5.81% on the
MTFL dataset for facial landmark localization and an accuracy of 45.73% on the
BNU dataset for emotion recognition with a single model.
Deep Matching Autoencoders
Authors: Tanmoy Mukherjee, Makoto Yamada, Timothy M. Hospedales
Date: 2017-11-16 00:00:00
Tasks: Image Captioning, Representation Learning

Increasingly many real world tasks involve data in multiple modalities or
views. This has motivated the development of many effective algorithms for
learning a common latent space to relate multiple domains. However, most
existing cross-view learning algorithms assume access to paired data for
training. Their applicability is thus limited as the paired data assumption is
often violated in practice: many tasks have only a small subset of data
available with pairing annotation, or even no paired data at all. In this paper
we introduce Deep Matching Autoencoders (DMAE), which learn a common latent
space and pairing from unpaired multi-modal data. Specifically we formulate
this as a cross-domain representation learning and object matching problem. We
simultaneously optimise parameters of representation learning auto-encoders and
the pairing of unpaired multi-modal data. This framework elegantly spans the
full regime from fully supervised, semi-supervised, and unsupervised (no paired
data) multi-modal learning. We show promising results in image captioning, and
on a new task that is uniquely enabled by our methodology: unsupervised
classifier learning.
Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks
Authors: Joost van Amersfoort, Wenzhe Shi, Alejandro Acosta, Francisco Massa, Johannes Totz, Zehan Wang, Jose Caballero
Date: 2017-11-16 00:00:00
Tasks: Generative Adversarial Network

Frame interpolation attempts to synthesise frames given one or more
consecutive video frames. In recent years, deep learning approaches, and
notably convolutional neural networks, have succeeded at tackling low- and
high-level computer vision problems including frame interpolation. These
techniques often tackle two problems, namely algorithm efficiency and
reconstruction quality. In this paper, we present a multi-scale generative
adversarial network for frame interpolation (\mbox{FIGAN}). To maximise the
efficiency of our network, we propose a novel multi-scale residual estimation
module where the predicted flow and synthesised frame are constructed in a
coarse-to-fine fashion. To improve the quality of synthesised intermediate
video frames, our network is jointly supervised at different levels with a
perceptual loss function that consists of an adversarial and two content
losses. We evaluate the proposed approach using a collection of 60fps videos
from YouTube-8m. Our results improve the state-of-the-art accuracy and provide
subjective visual quality comparable to the best performing interpolation
method at x47 faster runtime.
From Algorithmic Black Boxes to Adaptive White Boxes: Declarative Decision-Theoretic Ethical Programs as Codes of Ethics
Authors: Martijn van Otterlo
Date: 2017-11-16 00:00:00
Tasks: Decision Making, Ethics, Philosophy

Ethics of algorithms is an emerging topic in various disciplines such as
social science, law, and philosophy, but also artificial intelligence (AI). The
value alignment problem expresses the challenge of (machine) learning values
that are, in some way, aligned with human requirements or values. In this paper
I argue for looking at how humans have formalized and communicated values, in
professional codes of ethics, and for exploring declarative decision-theoretic
ethical programs (DDTEP) to formalize codes of ethics. This renders machine
ethical reasoning and decision-making, as well as learning, more transparent
and hopefully more accountable. The paper includes proof-of-concept examples of
known toy dilemmas and gatekeeping domains such as archives and libraries.
Sub-committee Approval Voting and Generalised Justified Representation Axioms
Authors: Haris Aziz, Barton E. Lee
Date: 2017-11-16 00:00:00
Tasks: Decision Making

Social choice is replete with various settings including single-winner
voting, multi-winner voting, probabilistic voting, multiple referenda, and
public decision making. We study a general model of social choice called
Sub-Committee Voting (SCV) that simultaneously generalizes these settings. We
then focus on sub-committee voting with approvals and propose extensions of the
justified representation axioms that have been considered for proportional
representation in approval-based committee voting. We study the properties and
relations of these axioms. For each of the axioms, we analyse whether a
representative committee exists and also examine the complexity of computing
and verifying such a committee.
On Support Relations and Semantic Scene Graphs
Authors: Michael Ying Yang, Wentong Liao, Hanno Ackermann, Bodo Rosenhahn
Date: 2016-09-19 00:00:00
Tasks: Scene Understanding

Scene understanding is a popular and challenging topic in both computer
vision and photogrammetry. Scene graph provides rich information for such scene
understanding. This paper presents a novel approach to infer such relations and
then to construct the scene graph. Support relations are estimated by
considering important, previously ignored information: the physical stability
and the prior support knowledge between object classes. In contrast to previous
methods for extracting support relations, the proposed approach generates more
accurate results, and does not require a pixel-wise semantic labeling of the
scene. The semantic scene graph which describes all the contextual relations
within the scene is constructed using this information. To evaluate the
accuracy of these graphs, multiple different measures are formulated. The
proposed algorithms are evaluated using the NYUv2 database. The results
demonstrate that the inferred support relations are more precise than
state-of-the-art. The scene graphs are compared against ground truth graphs.
A Revisit on Deep Hashings for Large-scale Content Based Image Retrieval
Authors: Deng Cai, Xiuye Gu, Chaoqi Wang
Date: 2017-11-16 00:00:00
Tasks: Content-Based Image Retrieval, Deep Hashing, Image Retrieval, Retrieval

There is a growing trend in studying deep hashing methods for content-based
image retrieval (CBIR), where hash functions and binary codes are learnt using
deep convolutional neural networks and then the binary codes can be used to do
approximate nearest neighbor (ANN) search. All the existing deep hashing papers
report their methods' superior performance over the traditional hashing methods
according to their experimental results. However, there are serious flaws in
the evaluations of existing deep hashing papers: (1) The datasets they used are
too small and simple to simulate the real CBIR situation. (2) They did not
correctly include the search time in their evaluation criteria, while the
search time is crucial in real CBIR systems. (3) The performance of some
unsupervised hashing algorithms (e.g., LSH) can easily be boosted if one uses
multiple hash tables, which is an important factor should be considered in the
evaluation while most of the deep hashing papers failed to do so.
  We re-evaluate several state-of-the-art deep hashing methods with a carefully
designed experimental setting. Empirical results reveal that the performance of
these deep hashing methods are inferior to multi-table IsoH, a very simple
unsupervised hashing method. Thus, the conclusions in all the deep hashing
papers should be carefully re-examined.
Reply With: Proactive Recommendation of Email Attachments
Authors: Christophe Van Gysel, Bhaskar Mitra, Matteo Venanzi, Roy Rosemarin, Grzegorz Kukla, Piotr Grudzien, Nicola Cancedda
Date: 2017-10-17 00:00:00
Tasks: Weakly-supervised Learning

Email responses often contain items-such as a file or a hyperlink to an
external document-that are attached to or included inline in the body of the
message. Analysis of an enterprise email corpus reveals that 35% of the time
when users include these items as part of their response, the attachable item
is already present in their inbox or sent folder. A modern email client can
proactively retrieve relevant attachable items from the user's past emails
based on the context of the current conversation, and recommend them for
inclusion, to reduce the time and effort involved in composing the response. In
this paper, we propose a weakly supervised learning framework for recommending
attachable items to the user. As email search systems are commonly available,
we constrain the recommendation task to formulating effective search queries
from the context of the conversations. The query is submitted to an existing IR
system to retrieve relevant items for attachment. We also present a novel
strategy for generating labels from an email corpus---without the need for
manual annotations---that can be used to train and evaluate the query
formulation model. In addition, we describe a deep convolutional neural network
that demonstrates satisfactory performance on this query formulation task when
evaluated on the publicly available Avocado dataset and a proprietary dataset
of internal emails obtained through an employee participation program.
DIMAL: Deep Isometric Manifold Learning Using Sparse Geodesic Sampling
Authors: Gautam Pai, Ronen Talmon, Alex Bronstein, Ron Kimmel
Date: 2017-11-16 00:00:00
Tasks: Deep Learning

This paper explores a fully unsupervised deep learning approach for computing
distance-preserving maps that generate low-dimensional embeddings for a certain
class of manifolds. We use the Siamese configuration to train a neural network
to solve the problem of least squares multidimensional scaling for generating
maps that approximately preserve geodesic distances. By training with only a
few landmarks, we show a significantly improved local and nonlocal
generalization of the isometric mapping as compared to analogous non-parametric
counterparts. Importantly, the combination of a deep-learning framework with a
multidimensional scaling objective enables a numerical analysis of network
architectures to aid in understanding their representation power. This provides
a geometric perspective to the generalizability of deep learning.