pwc-archive/papers-with-abstracts huggingface dataset:
Watermark Retrieval from 3D Printed Objects via Convolutional Neural Networks
Authors: Xin Zhang, Qian Wang, Toby Breckon, Ioannis Ivrissimtzis
Date: 2018-11-19 00:00:00
Tasks: Retrieval

We present a method for reading digital data embedded in planar 3D printed
surfaces. The data are organised in binary arrays and embedded as surface
textures in a way inspired by QR codes. At the core of the retrieval method
lies a Convolutional Neural Network, outputting a confidence map of the
location of the surface textures encoding value 1 bits. Subsequently, the bit
array is retrieved through a series of simple image processing and statistical
operations applied on the confidence map. Extensive experimentation with images
captured from various camera views, under various illumination conditions and
from objects printed with various material colours, shows that the proposed
method generalizes well and achieves the level of accuracy required in
practical applications.
Collaborative Dense SLAM
Authors: Louis Gallagher, John B. McDonald
Date: 2018-11-19 00:00:00
Tasks: Camera Pose Estimation, Pose Estimation, Surface Reconstruction, Visual Place Recognition

In this paper, we present a new system for live collaborative dense surface
reconstruction. Cooperative robotics, multi participant augmented reality and
human-robot interaction are all examples of situations where collaborative
mapping can be leveraged for greater agent autonomy. Our system builds on
ElasticFusion to allow a number of cameras starting with unknown initial
relative positions to maintain local maps utilising the original algorithm.
Carrying out visual place recognition across these local maps the system can
identify when two maps overlap in space, providing an inter-map constraint from
which the system can derive the relative poses of the two maps. Using these
resulting pose constraints, our system performs map merging, allowing multiple
cameras to fuse their measurements into a single shared reconstruction. The
advantage of this approach is that it avoids replication of structures
subsequent to loop closures, where multiple cameras traverse the same regions
of the environment. Furthermore, it allows cameras to directly exploit and
update regions of the environment previously mapped by other cameras within the
system. We provide both quantitative and qualitative analyses using the
synthetic ICL-NUIM dataset and the real-world Freiburg dataset including the
impact of multi-camera mapping on surface reconstruction accuracy, camera pose
estimation accuracy and overall processing time. We also include qualitative
results in the form of sample reconstructions of room sized environments with
up to 3 cameras undergoing intersecting and loopy trajectories.
Chat More If You Like: Dynamic Cue Words Planning to Flow Longer Conversations
Authors: Lili Yao, Ruijian Xu, Chao Li, Dongyan Zhao, Rui Yan
Date: 2018-11-19 00:00:00
Tasks: Diversity, Reinforcement Learning

To build an open-domain multi-turn conversation system is one of the most
interesting and challenging tasks in Artificial Intelligence. Many research
efforts have been dedicated to building such dialogue systems, yet few shed
light on modeling the conversation flow in an ongoing dialogue. Besides, it is
common for people to talk about highly relevant aspects during a conversation.
And the topics are coherent and drift naturally, which demonstrates the
necessity of dialogue flow modeling. To this end, we present the multi-turn
cue-words driven conversation system with reinforcement learning method (RLCw),
which strives to select an adaptive cue word with the greatest future credit,
and therefore improve the quality of generated responses. We introduce a new
reward to measure the quality of cue words in terms of effectiveness and
relevance. To further optimize the model for long-term conversations, a
reinforcement approach is adopted in this paper. Experiments on real-life
dataset demonstrate that our model consistently outperforms a set of
competitive baselines in terms of simulated turns, diversity and human
evaluation.
SEIGAN: Towards Compositional Image Generation by Simultaneously Learning to Segment, Enhance, and Inpaint
Authors: Pavel Ostyakov, Roman Suvorov, Elizaveta Logacheva, Oleg Khomenko, Sergey I. Nikolenko
Date: 2018-11-19 00:00:00
Tasks: Generative Adversarial Network, Image Generation, Image Manipulation

We present a novel approach to image manipulation and understanding by
simultaneously learning to segment object masks, paste objects to another
background image, and remove them from original images. For this purpose, we
develop a novel generative model for compositional image generation, SEIGAN
(Segment-Enhance-Inpaint Generative Adversarial Network), which learns these
three operations together in an adversarial architecture with additional cycle
consistency losses. To train, SEIGAN needs only bounding box supervision and
does not require pairing or ground truth masks. SEIGAN produces better
generated images (evaluated by human assessors) than other approaches and
produces high-quality segmentation masks, improving over other adversarially
trained approaches and getting closer to the results of fully supervised
training.
ATOM: Accurate Tracking by Overlap Maximization
Authors: Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg
Date: 2018-11-19 00:00:00
Proceeding: CVPR 2019 6
Tasks: General Classification, Object Tracking, State Estimation, Visual Object Tracking, Visual Tracking

While recent years have witnessed astonishing improvements in visual tracking
robustness, the advancements in tracking accuracy have been limited. As the
focus has been directed towards the development of powerful classifiers, the
problem of accurate target state estimation has been largely overlooked. In
fact, most trackers resort to a simple multi-scale search in order to estimate
the target bounding box. We argue that this approach is fundamentally limited
since target estimation is a complex task, requiring high-level knowledge about
the object.
  We address this problem by proposing a novel tracking architecture,
consisting of dedicated target estimation and classification components. High
level knowledge is incorporated into the target estimation through extensive
offline learning. Our target estimation component is trained to predict the
overlap between the target object and an estimated bounding box. By carefully
integrating target-specific information, our approach achieves previously
unseen bounding box accuracy. We further introduce a classification component
that is trained online to guarantee high discriminative power in the presence
of distractors. Our final tracking framework sets a new state-of-the-art on
five challenging benchmarks. On the new large-scale TrackingNet dataset, our
tracker ATOM achieves a relative gain of 15% over the previous best approach,
while running at over 30 FPS. Code and models are available at
https://github.com/visionml/pytracking.
Mixed Likelihood Gaussian Process Latent Variable Model
Authors: Samuel Murray, Hedvig Kjellström
Date: 2018-11-19 00:00:00
Tasks: model, Variational Inference

We present the Mixed Likelihood Gaussian process latent variable model
(GP-LVM), capable of modeling data with attributes of different types. The
standard formulation of GP-LVM assumes that each observation is drawn from a
Gaussian distribution, which makes the model unsuited for data with e.g.
categorical or nominal attributes. Our model, for which we use a sampling based
variational inference, instead assumes a separate likelihood for each observed
dimension. This formulation results in more meaningful latent representations,
and give better predictive performance for real world data with dimensions of
different types.
Beyond Attributes: Adversarial Erasing Embedding Network for Zero-shot Learning
Authors: Xiao-Bo Jin, Kai-Zhu Huang, Jianyu Miao
Date: 2018-11-19 00:00:00
Tasks: Attribute, Zero-Shot Learning

In this paper, an adversarial erasing embedding network with the guidance of
high-order attributes (AEEN-HOA) is proposed for going further to solve the
challenging ZSL/GZSL task. AEEN-HOA consists of two branches, i.e., the upper
stream is capable of erasing some initially discovered regions, then the
high-order attribute supervision is incorporated to characterize the
relationship between the class attributes. Meanwhile, the bottom stream is
trained by taking the current background regions to train the same attribute.
As far as we know, it is the first time of introducing the erasing operations
into the ZSL task. In addition, we first propose a class attribute activation
map for the visualization of ZSL output, which shows the relationship between
class attribute feature and attention map. Experiments on four standard
benchmark datasets demonstrate the superiority of AEEN-HOA framework.
Approximate Eigenvalue Decompositions of Linear Transformations with a Few Householder Reflectors
Authors: Cristian Rusu
Date: 2018-11-19 00:00:00

The ability to decompose a signal in an orthonormal basis (a set of orthogonal components, each normalized to have unit length) using a fast numerical procedure rests at the heart of many signal processing methods and applications. The classic examples are the Fourier and wavelet transforms that enjoy numerically efficient implementations (FFT and FWT, respectively). Unfortunately, orthonormal transformations are in general unstructured, and therefore they do not enjoy low computational complexity properties. In this paper, based on Householder reflectors, we introduce a class of orthonormal matrices that are numerically efficient to manipulate: we control the complexity of matrix-vector multiplications with these matrices using a given parameter. We provide numerical algorithms that approximate any orthonormal or symmetric transform with a new orthonormal or symmetric structure made up of products of a given number of Householder reflectors. We show analyses and numerical evidence to highlight the accuracy of the proposed approximations and provide an application to the case of learning fast Mahanalobis distance metric transformations.
Adversarial Soft-detection-based Aggregation Network for Image Retrieval
Authors: Jian Xu, Chunheng Wang, Cunzhao Shi, Baihua Xiao
Date: 2018-11-19 00:00:00
Tasks: Image Retrieval, Region Proposal, Retrieval

In recent year, the compact representations based on activations of
Convolutional Neural Network (CNN) achieve remarkable performance in image
retrieval. However, retrieval of some interested object that only takes up a
small part of the whole image is still a challenging problem. Therefore, it is
significant to extract the discriminative representations that contain regional
information of the pivotal small object. In this paper, we propose a novel
adversarial soft-detection-based aggregation (ASDA) method free from bounding
box annotations for image retrieval, based on adversarial detector and soft
region proposal layer. Our trainable adversarial detector generates semantic
maps based on adversarial erasing strategy to preserve more discriminative and
detailed information. Computed based on semantic maps corresponding to various
discriminative patterns and semantic contents, our soft region proposal is
arbitrary shape rather than only rectangle and it reflects the significance of
objects. The aggregation based on trainable soft region proposal highlights
discriminative semantic contents and suppresses the noise of background.
  We conduct comprehensive experiments on standard image retrieval datasets.
Our weakly supervised ASDA method achieves state-of-the-art performance on most
datasets. The results demonstrate that the proposed ASDA method is effective
for image retrieval.
An efficient density-based clustering algorithm using reverse nearest neighbour
Authors: Stiphen Chowdhury, Renato Cordeiro de Amorim
Date: 2018-11-19 00:00:00
Tasks: Clustering

Density-based clustering is the task of discovering high-density regions of
entities (clusters) that are separated from each other by contiguous regions of
low-density. DBSCAN is, arguably, the most popular density-based clustering
algorithm. However, its cluster recovery capabilities depend on the combination
of the two parameters. In this paper we present a new density-based clustering
algorithm which uses reverse nearest neighbour (RNN) and has a single
parameter. We also show that it is possible to estimate a good value for this
parameter using a clustering validity index. The RNN queries enable our
algorithm to estimate densities taking more than a single entity into account,
and to recover clusters that are not well-separated or have different
densities. Our experiments on synthetic and real-world data sets show our
proposed algorithm outperforms DBSCAN and its recent variant ISDBSCAN.
Outlier Aware Network Embedding for Attributed Networks
Authors: Sambaran Bandyopadhyay, Lokesh N, M. N. Murty
Date: 2018-11-19 00:00:00
Tasks: Network Embedding

Attributed network embedding has received much interest from the research
community as most of the networks come with some content in each node, which is
also known as node attributes. Existing attributed network approaches work well
when the network is consistent in structure and attributes, and nodes behave as
expected. But real world networks often have anomalous nodes. Typically these
outliers, being relatively unexplainable, affect the embeddings of other nodes
in the network. Thus all the downstream network mining tasks fail miserably in
the presence of such outliers. Hence an integrated approach to detect anomalies
and reduce their overall effect on the network embedding is required.
  Towards this end, we propose an unsupervised outlier aware network embedding
algorithm (ONE) for attributed networks, which minimizes the effect of the
outlier nodes, and hence generates robust network embeddings. We align and
jointly optimize the loss functions coming from structure and attributes of the
network. To the best of our knowledge, this is the first generic network
embedding approach which incorporates the effect of outliers for an attributed
network without any supervision. We experimented on publicly available real
networks and manually planted different types of outliers to check the
performance of the proposed algorithm. Results demonstrate the superiority of
our approach to detect the network outliers compared to the state-of-the-art
approaches. We also consider different downstream machine learning applications
on networks to show the efficiency of ONE as a generic network embedding
technique. The source code is made available at
https://github.com/sambaranban/ONE.
Adversarial Autoencoders for Compact Representations of 3D Point Clouds
Authors: Maciej Zamorski, Maciej Zięba, Piotr Klukowski, Rafał Nowak, Karol Kurach, Wojciech Stokowiec, Tomasz Trzciński
Date: 2018-11-19 00:00:00
Tasks: 3D Object Retrieval, Clustering, Generating 3D Point Clouds, Point Cloud Generation, Representation Learning, Retrieval

Deep generative architectures provide a way to model not only images but also
complex, 3-dimensional objects, such as point clouds. In this work, we present
a novel method to obtain meaningful representations of 3D shapes that can be
used for challenging tasks including 3D points generation, reconstruction,
compression, and clustering. Contrary to existing methods for 3D point cloud
generation that train separate decoupled models for representation learning and
generation, our approach is the first end-to-end solution that allows to
simultaneously learn a latent space of representation and generate 3D shape out
of it. Moreover, our model is capable of learning meaningful compact binary
descriptors with adversarial training conducted on a latent space. To achieve
this goal, we extend a deep Adversarial Autoencoder model (AAE) to accept 3D
input and create 3D output. Thanks to our end-to-end training regime, the
resulting method called 3D Adversarial Autoencoder (3dAAE) obtains either
binary or continuous latent space that covers a much wider portion of training
data distribution. Finally, our quantitative evaluation shows that 3dAAE
provides state-of-the-art results for 3D points clustering and 3D object
retrieval.
Quantum Inspired High Dimensional Conceptual Space as KID Model for Elderly Assistance
Authors: Ishwarya M S, Aswani Kumar Ch
Date: 2018-11-19 00:00:00

In this paper, we propose a cognitive system that acquires knowledge on
elderly daily activities to ensure their wellness in a smart home using a
Knowledge-Information-Data (KID) model. The novel cognitive framework called
high dimensional conceptual space is proposed and used as KID model. This KID
model is built using geometrical framework of conceptual spaces and formal
concept analysis (FCA) to overcome imprecise concept notation of conceptual
space with the help of topology based FCA. By doing so, conceptual space can be
represented using Hilbert space. This high dimensional conceptual space is
quantum inspired in terms of its concept representation. The knowledge learnt
by the KID model recognizes the daily activities of the elderly. Consequently,
the model identifies the scenario on which the wellness of the elderly has to
be ensured.
A Trustworthy, Responsible and Interpretable System to Handle Chit Chat in Conversational Bots
Authors: Parag Agrawal, Anshuman Suri, Tulasi Menon
Date: 2018-11-19 00:00:00
Tasks: Response Generation

Most often, chat-bots are built to solve the purpose of a search engine or a
human assistant: Their primary goal is to provide information to the user or
help them complete a task. However, these chat-bots are incapable of responding
to unscripted queries like "Hi, what's up", "What's your favourite food". Human
evaluation judgments show that 4 humans come to a consensus on the intent of a
given query which is from chat domain only 77% of the time, thus making it
evident how non-trivial this task is. In our work, we show why it is difficult
to break the chitchat space into clearly defined intents. We propose a system
to handle this task in chat-bots, keeping in mind scalability,
interpretability, appropriateness, trustworthiness, relevance and coverage. Our
work introduces a pipeline for query understanding in chitchat using
hierarchical intents as well as a way to use seq-seq auto-generation models in
professional bots. We explore an interpretable model for chat domain detection
and also show how various components such as adult/offensive classification,
grammars/regex patterns, curated personality based responses, generic guided
evasive responses and response generation models can be combined in a scalable
way to solve this problem.
Self-Referenced Deep Learning
Authors: Xu Lan, Xiatian Zhu, Shaogang Gong
Date: 2018-11-19 00:00:00
Tasks: Deep Learning, Knowledge Distillation

Knowledge distillation is an effective approach to transferring knowledge
from a teacher neural network to a student target network for satisfying the
low-memory and fast running requirements in practice use. Whilst being able to
create stronger target networks compared to the vanilla non-teacher based
learning strategy, this scheme needs to train additionally a large teacher
model with expensive computational cost. In this work, we present a
Self-Referenced Deep Learning (SRDL) strategy. Unlike both vanilla optimisation
and existing knowledge distillation, SRDL distils the knowledge discovered by
the in-training target model back to itself to regularise the subsequent
learning procedure therefore eliminating the need for training a large teacher
model. SRDL improves the model generalisation performance compared to vanilla
learning and conventional knowledge distillation approaches with negligible
extra computational cost. Extensive evaluations show that a variety of deep
networks benefit from SRDL resulting in enhanced deployment performance on both
coarse-grained object categorisation tasks (CIFAR10, CIFAR100, Tiny ImageNet,
and ImageNet) and fine-grained person instance identification tasks
(Market-1501).
Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer
Authors: J. Olivares-Sánchez, J. Casanova, E. Solano, L. Lamata
Date: 2018-11-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present an experimental realization of a measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti cloud quantum computer. The experiment in this few-qubit superconducting chip faithfully reproduces the theoretical proposal, setting the first steps towards a semiautonomous quantum agent. This experiment paves the way towards quantum reinforcement learning with superconducting circuits.
Deep Frank-Wolfe For Neural Network Optimization
Authors: Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
Date: 2018-11-19 00:00:00
Proceeding: ICLR 2019 5

Learning a deep neural network requires solving a challenging optimization problem: it is a high-dimensional, non-convex and non-smooth minimization problem with a large number of terms. The current practice in neural network optimization is to rely on the stochastic gradient descent (SGD) algorithm or its adaptive variants. However, SGD requires a hand-designed schedule for the learning rate. In addition, its adaptive variants tend to produce solutions that generalize less well on unseen data than SGD with a hand-designed schedule. We present an optimization method that offers empirically the best of both worlds: our algorithm yields good generalization performance while requiring only one hyper-parameter. Our approach is based on a composite proximal framework, which exploits the compositional nature of deep neural networks and can leverage powerful convex optimization algorithms by design. Specifically, we employ the Frank-Wolfe (FW) algorithm for SVM, which computes an optimal step-size in closed-form at each time-step. We further show that the descent direction is given by a simple backward pass in the network, yielding the same computational cost per iteration as SGD. We present experiments on the CIFAR and SNLI data sets, where we demonstrate the significant superiority of our method over Adam, Adagrad, as well as the recently proposed BPGrad and AMSGrad. Furthermore, we compare our algorithm to SGD with a hand-designed learning rate schedule, and show that it provides similar generalization while converging faster. The code is publicly available at https://github.com/oval-group/dfw.
Localisation via Deep Imagination: learn the features not the map
Authors: Jaime Spencer, Oscar Mendez, Richard Bowden, Simon Hadfield
Date: 2018-11-19 00:00:00

How many times does a human have to drive through the same area to become
familiar with it? To begin with, we might first build a mental model of our
surroundings. Upon revisiting this area, we can use this model to extrapolate
to new unseen locations and imagine their appearance. Based on this, we propose
an approach where an agent is capable of modelling new environments after a
single visitation. To this end, we introduce "Deep Imagination", a combination
of classical Visual-based Monte Carlo Localisation and deep learning. By making
use of a feature embedded 3D map, the system can "imagine" the view from any
novel location. These "imagined" views are contrasted with the current
observation in order to estimate the agent's current location. In order to
build the embedded map, we train a deep Siamese Fully Convolutional U-Net to
perform dense feature extraction. By training these features to be generic, no
additional training or fine tuning is required to adapt to new environments.
Our results demonstrate the generality and transfer capability of our learnt
dense features by training and evaluating on multiple datasets. Additionally,
we include several visualizations of the feature representations and resulting
3D maps, as well as their application to localisation.