pwc-archive/papers-with-abstracts huggingface dataset:
Look, Listen and Learn
Authors: Relja Arandjelović, Andrew Zisserman
Date: 2017-05-23 00:00:00
Proceeding: ICCV 2017 10
Tasks: Audio Classification, General Classification, Sound Classification

We consider the question: what can be learnt by looking at and listening to a
large number of unlabelled videos? There is a valuable, but so far untapped,
source of information contained in the video itself -- the correspondence
between the visual and the audio streams, and we introduce a novel
"Audio-Visual Correspondence" learning task that makes use of this. Training
visual and audio networks from scratch, without any additional supervision
other than the raw unconstrained videos themselves, is shown to successfully
solve this task, and, more interestingly, result in good visual and audio
representations. These features set the new state-of-the-art on two sound
classification benchmarks, and perform on par with the state-of-the-art
self-supervised approaches on ImageNet classification. We also demonstrate that
the network is able to localize objects in both modalities, as well as perform
fine-grained recognition tasks.
Spacetimes with Semantics (III) - The Structure of Functional Knowledge Representation and Artificial Reasoning
Authors: Mark Burgess
Date: 2016-08-07 00:00:00

Using the previously developed concepts of semantic spacetime, I explore the
interpretation of knowledge representations, and their structure, as a semantic
system, within the framework of promise theory. By assigning interpretations to
phenomena, from observers to observed, we may approach a simple description of
knowledge-based functional systems, with direct practical utility. The focus is
especially on the interpretation of concepts, associative knowledge, and
context awareness. The inference seems to be that most if not all of these
concepts emerge from purely semantic spacetime properties, which opens the
possibility for a more generalized understanding of what constitutes a
learning, or even `intelligent' system.
  Some key principles emerge for effective knowledge representation: 1)
separation of spacetime scales, 2) the recurrence of four irreducible types of
association, by which intent propagates: aggregation, causation, cooperation,
and similarity, 3) the need for discrimination of identities (discrete), which
is assisted by distinguishing timeline simultaneity from sequential events, and
4) the ability to learn (memory). It is at least plausible that emergent
knowledge abstraction capabilities have their origin in basic spacetime
structures.
  These notes present a unified view of mostly well-known results; they allow
us to see information models, knowledge representations, machine learning, and
semantic networking (transport and information base) in a common framework. The
notion of `smart spaces' thus encompasses artificial systems as well as living
systems, across many different scales, e.g. smart cities and organizations.
A Spacetime Approach to Generalized Cognitive Reasoning in Multi-scale Learning
Authors: Mark Burgess
Date: 2017-02-12 00:00:00

In modern machine learning, pattern recognition replaces realtime semantic
reasoning. The mapping from input to output is learned with fixed semantics by
training outcomes deliberately. This is an expensive and static approach which
depends heavily on the availability of a very particular kind of prior raining
data to make inferences in a single step. Conventional semantic network
approaches, on the other hand, base multi-step reasoning on modal logics and
handcrafted ontologies, which are ad hoc, expensive to construct, and fragile
to inconsistency. Both approaches may be enhanced by a hybrid approach, which
completely separates reasoning from pattern recognition. In this report, a
quasi-linguistic approach to knowledge representation is discussed, motivated
by spacetime structure. Tokenized patterns from diverse sources are integrated
to build a lightly constrained and approximately scale-free network. This is
then be parsed with very simple recursive algorithms to generate
`brainstorming' sets of reasoned knowledge.
Application of machine learning for hematological diagnosis
Authors: Gregor Gunčar, Matjaž Kukar, Mateja Notar, Miran Brvar, Peter Černelč, Manca Notar, Marko Notar
Date: 2017-08-01 00:00:00
Tasks: BIG-bench Machine Learning, Medical Diagnosis

Quick and accurate medical diagnosis is crucial for the successful treatment
of a disease. Using machine learning algorithms, we have built two models to
predict a hematologic disease, based on laboratory blood test results. In one
predictive model, we used all available blood test parameters and in the other
a reduced set, which is usually measured upon patient admittance. Both models
produced good results, with a prediction accuracy of 0.88 and 0.86, when
considering the list of five most probable diseases, and 0.59 and 0.57, when
considering only the most probable disease. Models did not differ significantly
from each other, which indicates that a reduced set of parameters contains a
relevant fingerprint of a disease, expanding the utility of the model for
general practitioner's use and indicating that there is more information in the
blood test results than physicians recognize. In the clinical test we showed
that the accuracy of our predictive models was on a par with the ability of
hematology specialists. Our study is the first to show that a machine learning
predictive model based on blood tests alone, can be successfully applied to
predict hematologic diseases and could open up unprecedented possibilities in
medical diagnosis.
CNN Cascades for Segmenting Whole Slide Images of the Kidney
Authors: Michael Gadermayr, Ann-Kathrin Dombrowski, Barbara Mara Klinkhammer, Peter Boor, Dorit Merhof
Date: 2017-08-01 00:00:00
Tasks: Segmentation, whole slide images

Due to the increasing availability of whole slide scanners facilitating
digitization of histopathological tissue, there is a strong demand for the
development of computer based image analysis systems. In this work, the focus
is on the segmentation of the glomeruli constituting a highly relevant
structure in renal histopathology, which has not been investigated before in
combination with CNNs. We propose two different CNN cascades for segmentation
applications with sparse objects. These approaches are applied to the problem
of glomerulus segmentation and compared with conventional fully-convolutional
networks. Overall, with the best performing cascade approach, single CNNs are
outperformed and a pixel-level Dice similarity coefficient of 0.90 is obtained.
Combined with qualitative and further object-level analyses the obtained
results are assessed as excellent also compared to recent approaches. In
conclusion, we can state that especially one of the proposed cascade networks
proved to be a highly powerful tool for segmenting the renal glomeruli
providing best segmentation accuracies and also keeping the computing time at a
low level.
Improving Part-of-Speech Tagging for NLP Pipelines
Authors: Vishaal Jatav, Ravi Teja, Srini Bharadwaj, Venkat Srinivasan
Date: 2017-08-01 00:00:00
Tasks: Part-Of-Speech Tagging, Sentence

This paper outlines the results of sentence level linguistics based rules for
improving part-of-speech tagging. It is well known that the performance of
complex NLP systems is negatively affected if one of the preliminary stages is
less than perfect. Errors in the initial stages in the pipeline have a
snowballing effect on the pipeline's end performance. We have created a set of
linguistics based rules at the sentence level which adjust part-of-speech tags
from state-of-the-art taggers. Comparison with state-of-the-art taggers on
widely used benchmarks demonstrate significant improvements in tagging accuracy
and consequently in the quality and accuracy of NLP systems.
Camera Relocalization by Computing Pairwise Relative Poses Using Convolutional Neural Network
Authors: Zakaria Laskar, Iaroslav Melekhov, Surya Kalia, Juho Kannala
Date: 2017-07-31 00:00:00
Tasks: Camera Relocalization, Pose Estimation

We propose a new deep learning based approach for camera relocalization. Our
approach localizes a given query image by using a convolutional neural network
(CNN) for first retrieving similar database images and then predicting the
relative pose between the query and the database images, whose poses are known.
The camera location for the query image is obtained via triangulation from two
relative translation estimates using a RANSAC based approach. Each relative
pose estimate provides a hypothesis for the camera orientation and they are
fused in a second RANSAC scheme. The neural network is trained for relative
pose estimation in an end-to-end manner using training image pairs. In contrast
to previous work, our approach does not require scene-specific training of the
network, which improves scalability, and it can also be applied to scenes which
are not available during the training of the network. As another main
contribution, we release a challenging indoor localisation dataset covering 5
different scenes registered to a common coordinate frame. We evaluate our
approach using both our own dataset and the standard 7 Scenes benchmark. The
results show that the proposed approach generalizes well to previously unseen
scenes and compares favourably to other recent CNN-based methods.
HMM-based Indic Handwritten Word Recognition using Zone Segmentation
Authors: Partha Pratim Roy, Ayan Kumar Bhunia, Ayan Das, Prasenjit Dey, Umapada Pal
Date: 2017-08-01 00:00:00
Tasks: Segmentation

This paper presents a novel approach towards Indic handwritten word
recognition using zone-wise information. Because of complex nature due to
compound characters, modifiers, overlapping and touching, etc., character
segmentation and recognition is a tedious job in Indic scripts (e.g.
Devanagari, Bangla, Gurumukhi, and other similar scripts). To avoid character
segmentation in such scripts, HMM-based sequence modeling has been used earlier
in holistic way. This paper proposes an efficient word recognition framework by
segmenting the handwritten word images horizontally into three zones (upper,
middle and lower) and recognize the corresponding zones. The main aim of this
zone segmentation approach is to reduce the number of distinct component
classes compared to the total number of classes in Indic scripts. As a result,
use of this zone segmentation approach enhances the recognition performance of
the system. The components in middle zone where characters are mostly touching
are recognized using HMM. After the recognition of middle zone, HMM based
Viterbi forced alignment is applied to mark the left and right boundaries of
the characters. Next, the residue components, if any, in upper and lower zones
in their respective boundary are combined to achieve the final word level
recognition. Water reservoir feature has been integrated in this framework to
improve the zone segmentation and character alignment defects while
segmentation. A novel sliding window-based feature, called Pyramid Histogram of
Oriented Gradient (PHOG) is proposed for middle zone recognition. An exhaustive
experiment is performed on two Indic scripts namely, Bangla and Devanagari for
the performance evaluation. From the experiment, it has been noted that
proposed zone-wise recognition improves accuracy with respect to the
traditional way of Indic word recognition.
CREST: Convolutional Residual Learning for Visual Tracking
Authors: Yibing Song, Chao Ma, Lijun Gong, Jiawei Zhang, Rynson Lau, Ming-Hsuan Yang
Date: 2017-08-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Visual Tracking

Discriminative correlation filters (DCFs) have been shown to perform
superiorly in visual tracking. They only need a small set of training samples
from the initial frame to generate an appearance model. However, existing DCFs
learn the filters separately from feature extraction, and update these filters
using a moving average operation with an empirical weight. These DCF trackers
hardly benefit from the end-to-end training. In this paper, we propose the
CREST algorithm to reformulate DCFs as a one-layer convolutional neural
network. Our method integrates feature extraction, response map generation as
well as model update into the neural networks for an end-to-end training. To
reduce model degradation during online update, we apply residual learning to
take appearance changes into account. Extensive experiments on the benchmark
datasets demonstrate that our CREST tracker performs favorably against
state-of-the-art trackers.
Fast Preprocessing for Robust Face Sketch Synthesis
Authors: Yibing Song, Jiawei Zhang, Linchao Bao, Qingxiong Yang
Date: 2017-08-01 00:00:00
Tasks: Face Sketch Synthesis

Exemplar-based face sketch synthesis methods usually meet the challenging
problem that input photos are captured in different lighting conditions from
training photos. The critical step causing the failure is the search of similar
patch candidates for an input photo patch. Conventional illumination invariant
patch distances are adopted rather than directly relying on pixel intensity
difference, but they will fail when local contrast within a patch changes. In
this paper, we propose a fast preprocessing method named Bidirectional
Luminance Remapping (BLR), which interactively adjust the lighting of training
and input photos. Our method can be directly integrated into state-of-the-art
exemplar-based methods to improve their robustness with ignorable computational
cost.
Learning to Hallucinate Face Images via Component Generation and Enhancement
Authors: Yibing Song, Jiawei Zhang, Shengfeng He, Linchao Bao, Qingxiong Yang
Date: 2017-08-01 00:00:00
Tasks: Face Hallucination, Hallucination

We propose a two-stage method for face hallucination. First, we generate
facial components of the input image using CNNs. These components represent the
basic facial structures. Second, we synthesize fine-grained facial structures
from high resolution training images. The details of these structures are
transferred into facial components for enhancement. Therefore, we generate
facial components to approximate ground truth global appearance in the first
stage and enhance them through recovering details in the second stage. The
experiments demonstrate that our method performs favorably against
state-of-the-art methods
Natural Language Processing with Small Feed-Forward Networks
Authors: Jan A. Botha, Emily Pitler, Ji Ma, Anton Bakalov, Alex Salcianu, David Weiss, Ryan Mcdonald, Slav Petrov
Date: 2017-08-01 00:00:00
Proceeding: EMNLP 2017 9

We show that small and shallow feed-forward neural networks can achieve near
state-of-the-art results on a range of unstructured and structured language
processing tasks while being considerably cheaper in memory and computational
requirements than deep recurrent models. Motivated by resource-constrained
environments like mobile phones, we showcase simple techniques for obtaining
such small neural network models, and investigate different tradeoffs when
deciding how to allocate a small memory budget.
Video Object Segmentation with Re-identification
Authors: Xiaoxiao Li, Yuankai Qi, Zhe Wang, Kai Chen, Ziwei Liu, Jianping Shi, Ping Luo, Xiaoou Tang, Chen Change Loy
Date: 2017-08-01 00:00:00
Tasks: Object, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Segmentation, Video Semantic Segmentation

Conventional video segmentation methods often rely on temporal continuity to
propagate masks. Such an assumption suffers from issues like drifting and
inability to handle large displacement. To overcome these issues, we formulate
an effective mechanism to prevent the target from being lost via adaptive
object re-identification. Specifically, our Video Object Segmentation with
Re-identification (VS-ReID) model includes a mask propagation module and a ReID
module. The former module produces an initial probability map by flow warping
while the latter module retrieves missing instances by adaptive matching. With
these two modules iteratively applied, our VS-ReID records a global mean
(Region Jaccard and Boundary F measure) of 0.699, the best performance in 2017
DAVIS Challenge.
Real-time Deep Video Deinterlacing
Authors: Haichao Zhu, Xueting Liu, Xiangyu Mao, Tien-Tsin Wong
Date: 2017-08-01 00:00:00
Tasks: GPU, Super-Resolution, Translation, Video Deinterlacing

Interlacing is a widely used technique, for television broadcast and video
recording, to double the perceived frame rate without increasing the bandwidth.
But it presents annoying visual artifacts, such as flickering and silhouette
"serration," during the playback. Existing state-of-the-art deinterlacing
methods either ignore the temporal information to provide real-time performance
but lower visual quality, or estimate the motion for better deinterlacing but
with a trade-off of higher computational cost. In this paper, we present the
first and novel deep convolutional neural networks (DCNNs) based method to
deinterlace with high visual quality and real-time performance. Unlike existing
models for super-resolution problems which relies on the translation-invariant
assumption, our proposed DCNN model utilizes the temporal information from both
the odd and even half frames to reconstruct only the missing scanlines, and
retains the given odd and even scanlines for producing the full deinterlaced
frames. By further introducing a layer-sharable architecture, our system can
achieve real-time performance on a single GPU. Experiments shows that our
method outperforms all existing methods, in terms of reconstruction accuracy
and computational performance.
Hard-Aware Deeply Cascaded Embedding
Authors: Yuhui Yuan, Kuiyuan Yang, Chao Zhang
Date: 2016-11-17 00:00:00
Proceeding: ICCV 2017 10
Tasks: Metric Learning, Triplet

Riding on the waves of deep neural networks, deep metric learning has also
achieved promising results in various tasks using triplet network or Siamese
network. Though the basic goal of making images from the same category closer
than the ones from different categories is intuitive, it is hard to directly
optimize due to the quadratic or cubic sample size. To solve the problem, hard
example mining which only focuses on a subset of samples that are considered
hard is widely used. However, hard is defined relative to a model, where
complex models treat most samples as easy ones and vice versa for simple
models, and both are not good for training. Samples are also with different
hard levels, it is hard to define a model with the just right complexity and
choose hard examples adequately. This motivates us to ensemble a set of models
with different complexities in cascaded manner and mine hard examples
adaptively, a sample is judged by a series of models with increasing
complexities and only updates models that consider the sample as a hard case.
We evaluate our method on CARS196, CUB-200-2011, Stanford Online Products,
VehicleID and DeepFashion datasets. Our method outperforms state-of-the-art
methods by a large margin.
Tensorial Recurrent Neural Networks for Longitudinal Data Analysis
Authors: Mingyuan Bai, Boyan Zhang, Junbin Gao
Date: 2017-08-01 00:00:00
Tasks: Time Series, Time Series Analysis

Traditional Recurrent Neural Networks assume vectorized data as inputs.
However many data from modern science and technology come in certain structures
such as tensorial time series data. To apply the recurrent neural networks for
this type of data, a vectorisation process is necessary, while such a
vectorisation leads to the loss of the precise information of the spatial or
longitudinal dimensions. In addition, such a vectorized data is not an optimum
solution for learning the representation of the longitudinal data. In this
paper, we propose a new variant of tensorial neural networks which directly
take tensorial time series data as inputs. We call this new variant as
Tensorial Recurrent Neural Network (TRNN). The proposed TRNN is based on tensor
Tucker decomposition.
Model-based learning of local image features for unsupervised texture segmentation
Authors: Martin Kiechle, Martin Storath, Andreas Weinmann, Martin Kleinsteuber
Date: 2017-08-01 00:00:00
Tasks: Segmentation

Features that capture well the textural patterns of a certain class of images
are crucial for the performance of texture segmentation methods. The manual
selection of features or designing new ones can be a tedious task. Therefore,
it is desirable to automatically adapt the features to a certain image or class
of images. Typically, this requires a large set of training images with similar
textures and ground truth segmentation. In this work, we propose a framework to
learn features for texture segmentation when no such training data is
available. The cost function for our learning process is constructed to match a
commonly used segmentation model, the piecewise constant Mumford-Shah model.
This means that the features are learned such that they provide an
approximately piecewise constant feature image with a small jump set. Based on
this idea, we develop a two-stage algorithm which first learns suitable
convolutional features and then performs a segmentation. We note that the
features can be learned from a small set of images, from a single image, or
even from image patches. The proposed method achieves a competitive rank in the
Prague texture segmentation benchmark, and it is effective for segmenting
histological images.
An Investigation into the Pedagogical Features of Documents
Authors: Emily Sheng, Prem Natarajan, Jonathan Gordon, Gully Burns
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 9

Characterizing the content of a technical document in terms of its learning
utility can be useful for applications related to education, such as generating
reading lists from large collections of documents. We refer to this learning
utility as the "pedagogical value" of the document to the learner. While
pedagogical value is an important concept that has been studied extensively
within the education domain, there has been little work exploring it from a
computational, i.e., natural language processing (NLP), perspective. To allow a
computational exploration of this concept, we introduce the notion of
"pedagogical roles" of documents (e.g., Tutorial and Survey) as an intermediary
component for the study of pedagogical value. Given the lack of available
corpora for our exploration, we create the first annotated corpus of
pedagogical roles and use it to test baseline techniques for automatic
prediction of such roles.
A Locally Weighted Fixation Density-Based Metric for Assessing the Quality of Visual Saliency Predictions
Authors: Milind S. Gide, Lina J. Karam
Date: 2017-08-01 00:00:00
Tasks: Saliency Prediction

With the increased focus on visual attention (VA) in the last decade, a large
number of computational visual saliency methods have been developed over the
past few years. These models are traditionally evaluated by using performance
evaluation metrics that quantify the match between predicted saliency and
fixation data obtained from eye-tracking experiments on human observers. Though
a considerable number of such metrics have been proposed in the literature,
there are notable problems in them. In this work, we discuss shortcomings in
existing metrics through illustrative examples and propose a new metric that
uses local weights based on fixation density which overcomes these flaws. To
compare the performance of our proposed metric at assessing the quality of
saliency prediction with other existing metrics, we construct a ground-truth
subjective database in which saliency maps obtained from 17 different VA models
are evaluated by 16 human observers on a 5-point categorical scale in terms of
their visual resemblance with corresponding ground-truth fixation density maps
obtained from eye-tracking data. The metrics are evaluated by correlating
metric scores with the human subjective ratings. The correlation results show
that the proposed evaluation metric outperforms all other popular existing
metrics. Additionally, the constructed database and corresponding subjective
ratings provide an insight into which of the existing metrics and future
metrics are better at estimating the quality of saliency prediction and can be
used as a benchmark.