pwc-archive/papers-with-abstracts huggingface dataset:
Diverse M-Best Solutions by Dynamic Programming
Authors: Carsten Haubold, Virginie Uhlmann, Michael Unser, Fred A. Hamprecht
Date: 2018-03-15 00:00:00
Tasks: object-detection, Object Detection

Many computer vision pipelines involve dynamic programming primitives such as
finding a shortest path or the minimum energy solution in a tree-shaped
probabilistic graphical model. In such cases, extracting not merely the best,
but the set of M-best solutions is useful to generate a rich collection of
candidate proposals that can be used in downstream processing. In this work, we
show how M-best solutions of tree-shaped graphical models can be obtained by
dynamic programming on a special graph with M layers. The proposed multi-layer
concept is optimal for searching M-best solutions, and so flexible that it can
also approximate M-best diverse solutions. We illustrate the usefulness with
applications to object detection, panorama stitching and centerline extraction.
  Note: We have observed that an assumption in section 4 of our paper is not
always fulfilled, see the attached corrigendum for details.
A Novel Motion Detection Method Resistant to Severe Illumination Changes
Authors: Sahar Yousefi, M. T. Manzuri Shalmani, Jeremy Lin, Marius Staring
Date: 2016-12-11 00:00:00
Tasks: Clustering, Motion Detection

Recently, there has been a considerable attention given to the motion
detection problem due to the explosive growth of its applications in video
analysis and surveillance systems. While the previous approaches can produce
good results, an accurate detection of motion remains a challenging task due to
the difficulties raised by illumination variations, occlusion, camouflage,
burst physical motion, dynamic texture, and environmental changes such as those
on climate changes, sunlight changes during a day, etc. In this paper, we
propose a novel per-pixel motion descriptor for both motion detection and
dynamic texture segmentation which outperforms the current methods in the
literature particularly in severe scenarios. The proposed descriptor is based
on two complementary three-dimensional-discrete wavelet transform (3D-DWT) and
three-dimensional wavelet leader. In this approach, a feature vector is
extracted for each pixel by applying a novel three dimensional wavelet-based
motion descriptor. Then, the extracted features are clustered by a clustering
method such as well-known k-means algorithm or Gaussian Mixture Model (GMM).
The experimental results demonstrate the effectiveness of our proposed method
compared to the other motion detection approaches from the literature. The
application of the proposed method and additional experimental results for the
different datasets are available at
(http://dspl.ce.sharif.edu/motiondetector.html).
Exploring Linear Relationship in Feature Map Subspace for ConvNets Compression
Authors: Dong Wang, Lei Zhou, Xueni Zhang, Xiao Bai, Jun Zhou
Date: 2018-03-15 00:00:00
Tasks: Clustering

While the research on convolutional neural networks (CNNs) is progressing
quickly, the real-world deployment of these models is often limited by
computing resources and memory constraints. In this paper, we address this
issue by proposing a novel filter pruning method to compress and accelerate
CNNs. Our work is based on the linear relationship identified in different
feature map subspaces via visualization of feature maps. Such linear
relationship implies that the information in CNNs is redundant. Our method
eliminates the redundancy in convolutional filters by applying subspace
clustering to feature maps. In this way, most of the representative information
in the network can be retained in each cluster. Therefore, our method provides
an effective solution to filter pruning for which most existing methods
directly remove filters based on simple heuristics. The proposed method is
independent of the network structure, thus it can be adopted by any
off-the-shelf deep learning libraries. Experiments on different networks and
tasks show that our method outperforms existing techniques before fine-tuning,
and achieves the state-of-the-art results after fine-tuning.
Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning
Authors: Morgan A. Schmitz, Matthieu Heitz, Nicolas Bonneel, Fred Maurice Ngolè Mboula, David Coeurjolly, Marco Cuturi, Gabriel Peyré, Jean-Luc Starck
Date: 2017-08-07 00:00:00
Tasks: Dictionary Learning

This paper introduces a new nonlinear dictionary learning method for
histograms in the probability simplex. The method leverages optimal transport
theory, in the sense that our aim is to reconstruct histograms using so-called
displacement interpolations (a.k.a. Wasserstein barycenters) between dictionary
atoms; such atoms are themselves synthetic histograms in the probability
simplex. Our method simultaneously estimates such atoms, and, for each
datapoint, the vector of weights that can optimally reconstruct it as an
optimal transport barycenter of such atoms. Our method is computationally
tractable thanks to the addition of an entropic regularization to the usual
optimal transportation problem, leading to an approximation scheme that is
efficient, parallel and simple to differentiate. Both atoms and weights are
learned using a gradient-based descent method. Gradients are obtained by
automatic differentiation of the generalized Sinkhorn iterations that yield
barycenters with entropic smoothing. Because of its formulation relying on
Wasserstein barycenters instead of the usual matrix product between dictionary
and codes, our method allows for nonlinear relationships between atoms and the
reconstruction of input data. We illustrate its application in several
different image processing settings.
Algebraic Machine Learning
Authors: Fernando Martin-Maroto, Gonzalo G. de Polavieja
Date: 2018-03-14 00:00:00
Tasks: BIG-bench Machine Learning, Memorization

Machine learning algorithms use error function minimization to fit a large
set of parameters in a preexisting model. However, error minimization
eventually leads to a memorization of the training dataset, losing the ability
to generalize to other datasets. To achieve generalization something else is
needed, for example a regularization method or stopping the training when error
in a validation dataset is minimal. Here we propose a different approach to
learning and generalization that is parameter-free, fully discrete and that
does not use function minimization. We use the training data to find an
algebraic representation with minimal size and maximal freedom, explicitly
expressed as a product of irreducible components. This algebraic representation
is shown to directly generalize, giving high accuracy in test data, more so the
smaller the representation. We prove that the number of generalizing
representations can be very large and the algebra only needs to find one. We
also derive and test a relationship between compression and error rate. We give
results for a simple problem solved step by step, hand-written character
recognition, and the Queens Completion problem as an example of unsupervised
learning. As an alternative to statistical learning, algebraic learning may
offer advantages in combining bottom-up and top-down information, formal
concept derivation from data and large-scale parallelization.
How compatible are our discourse annotations? Insights from mapping RST-DT and PDTB annotations
Authors: Vera Demberg, Fatemeh Torabi Asr, Merel Scholman
Date: 2017-04-28 00:00:00
Tasks: Implicit Relations, Relation

Discourse-annotated corpora are an important resource for the community, but
they are often annotated according to different frameworks. This makes
comparison of the annotations difficult, thereby also preventing researchers
from searching the corpora in a unified way, or using all annotated data
jointly to train computational systems. Several theoretical proposals have
recently been made for mapping the relational labels of different frameworks to
each other, but these proposals have so far not been validated against existing
annotations. The two largest discourse relation annotated resources, the Penn
Discourse Treebank and the Rhetorical Structure Theory Discourse Treebank, have
however been annotated on the same text, allowing for a direct comparison of
the annotation layers. We propose a method for automatically aligning the
discourse segments, and then evaluate existing mapping proposals by comparing
the empirically observed against the proposed mappings. Our analysis highlights
the influence of segmentation on subsequent discourse relation labeling, and
shows that while agreement between frameworks is reasonable for explicit
relations, agreement on implicit relations is low. We identify several sources
of systematic discrepancies between the two annotation schemes and discuss
consequences of these discrepancies for future annotation and for the training
of automatic discourse relation labellers.
Dense Limit of the Dawid-Skene Model for Crowdsourcing and Regions of Sub-optimality of Message Passing Algorithms
Authors: Christian Schmidt, Lenka Zdeborová
Date: 2018-03-13 00:00:00

Crowdsourcing is a strategy to categorize data through the contribution of
many individuals. A wide range of theoretical and algorithmic contributions are
based on the model of Dawid and Skene [1]. Recently it was shown in [2,3] that,
in certain regimes, belief propagation is asymptotically optimal for data
generated from the Dawid-Skene model. This paper is motivated by this recent
progress. We analyze the dense limit of the Dawid-Skene model. It is shown that
it belongs to a larger class of low-rank matrix estimation problems for which
it is possible to express the asymptotic, Bayes-optimal, performance in a
simple closed form. In the dense limit the mapping to a low-rank matrix
estimation problem provides an approximate message passing algorithm that
solves the problem algorithmically. We identify the regions where the algorithm
efficiently computes the Bayes-optimal estimates. Our analysis refines the
results of [2,3] about optimality of message passing algorithms by
characterizing regions of parameters where these algorithms do not match the
Bayes-optimal performance. We further study numerically the performance of
approximate message passing, derived in the dense limit, on sparse instances
and carry out experiments on a real world dataset.
Concise Radiometric Calibration Using The Power of Ranking
Authors: Han Gong, Graham D. Finlayson, Maryam M. Darrodi
Date: 2017-07-27 00:00:00

Compared with raw images, the more common JPEG images are less useful for
machine vision algorithms and professional photographers because JPEG-sRGB does
not preserve a linear relation between pixel values and the light measured from
the scene. A camera is said to be radiometrically calibrated if there is a
computational model which can predict how the raw linear sensor image is mapped
to the corresponding rendered image (e.g. JPEGs) and vice versa. This paper
begins with the observation that the rank order of pixel values are mostly
preserved post colour correction. We show that this observation is the key to
solving for the whole camera pipeline (colour correction, tone and gamut
mapping). Our rank-based calibration method is simpler than the prior art and
so is parametrised by fewer variables which, concomitantly, can be solved for
using less calibration data. Another advantage is that we can derive the camera
pipeline from a single pair of raw-JPEG images. Experiments demonstrate that
our method delivers state-of-the-art results (especially for the most
interesting case of JPEG to raw).
Tracking all members of a honey bee colony over their lifetime
Authors: Franziska Boenisch, Benjamin Rosemann, Benjamin Wild, Fernando Wario, David Dormagen, Tim Landgraf
Date: 2018-02-09 00:00:00
Tasks: All

Computational approaches to the analysis of collective behavior in social
insects increasingly rely on motion paths as an intermediate data layer from
which one can infer individual behaviors or social interactions. Honey bees are
a popular model for learning and memory. Previous experience has been shown to
affect and modulate future social interactions. So far, no lifetime history
observations have been reported for all bees of a colony. In a previous work we
introduced a tracking system customized to track up to $4000$ bees over several
weeks. In this contribution we present an in-depth description of the
underlying multi-step algorithm which both produces the motion paths, and also
improves the marker decoding accuracy significantly. We automatically tracked
${\sim}2000$ marked honey bees over 10 weeks with inexpensive recording
hardware using markers without any error correction bits. We found that the
proposed two-step tracking reduced incorrect ID decodings from initially
${\sim}13\%$ to around $2\%$ post-tracking. Alongside this paper, we publish
the first trajectory dataset for all bees in a colony, extracted from ${\sim}
4$ million images. We invite researchers to join the collective scientific
effort to investigate this intriguing animal system. All components of our
system are open-source.
Blind Image Deconvolution using Deep Generative Priors
Authors: Muhammad Asim, Fahad Shamshad, Ali Ahmed
Date: 2018-02-12 00:00:00
Tasks: Deblurring, Image Deblurring, Image Deconvolution

This paper proposes a novel approach to regularize the \textit{ill-posed} and
\textit{non-linear} blind image deconvolution (blind deblurring) using deep
generative networks as priors. We employ two separate generative models --- one
trained to produce sharp images while the other trained to generate blur
kernels from lower-dimensional parameters. To deblur, we propose an alternating
gradient descent scheme operating in the latent lower-dimensional space of each
of the pretrained generative models. Our experiments show promising deblurring
results on images even under large blurs, and heavy noise. To address the
shortcomings of generative models such as mode collapse, we augment our
generative priors with classical image priors and report improved performance
on complex image datasets. The deblurring performance depends on how well the
range of the generator spans the image class. Interestingly, our experiments
show that even an untrained structured (convolutional) generative networks acts
as an image prior in the image deblurring context allowing us to extend our
results to more diverse natural image datasets.
Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation
Authors: Panagiotis Meletis, Gijs Dubbelman
Date: 2018-03-15 00:00:00
Tasks: GPU, Semantic Segmentation

We propose a convolutional network with hierarchical classifiers for
per-pixel semantic segmentation, which is able to be trained on multiple,
heterogeneous datasets and exploit their semantic hierarchy. Our network is the
first to be simultaneously trained on three different datasets from the
intelligent vehicles domain, i.e. Cityscapes, GTSDB and Mapillary Vistas, and
is able to handle different semantic level-of-detail, class imbalances, and
different annotation types, i.e. dense per-pixel and sparse bounding-box
labels. We assess our hierarchical approach, by comparing against flat,
non-hierarchical classifiers and we show improvements in mean pixel accuracy of
13.0% for Cityscapes classes and 2.4% for Vistas classes and 32.3% for GTSDB
classes. Our implementation achieves inference rates of 17 fps at a resolution
of 520x706 for 108 classes running on a GPU.
End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks
Authors: Szu-Wei Fu, Tao-Wei Wang, Yu Tsao, Xugang Lu, Hisashi Kawai
Date: 2017-09-12 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Model Optimization, Speech Enhancement, speech-recognition, Speech Recognition

Speech enhancement model is used to map a noisy speech to a clean speech. In
the training stage, an objective function is often adopted to optimize the
model parameters. However, in most studies, there is an inconsistency between
the model optimization criterion and the evaluation criterion on the enhanced
speech. For example, in measuring speech intelligibility, most of the
evaluation metric is based on a short-time objective intelligibility (STOI)
measure, while the frame based minimum mean square error (MMSE) between
estimated and clean speech is widely used in optimizing the model. Due to the
inconsistency, there is no guarantee that the trained model can provide optimal
performance in applications. In this study, we propose an end-to-end
utterance-based speech enhancement framework using fully convolutional neural
networks (FCN) to reduce the gap between the model optimization and evaluation
criterion. Because of the utterance-based optimization, temporal correlation
information of long speech segments, or even at the entire utterance level, can
be considered when perception-based objective functions are used for the direct
optimization. As an example, we implement the proposed FCN enhancement
framework to optimize the STOI measure. Experimental results show that the STOI
of test speech is better than conventional MMSE-optimized speech due to the
consistency between the training and evaluation target. Moreover, by
integrating the STOI in model optimization, the intelligibility of human
subjects and automatic speech recognition (ASR) system on the enhanced speech
is also substantially improved compared to those generated by the MMSE
criterion.
ShuffleSeg: Real-time Semantic Segmentation Network
Authors: Mostafa Gamal, Mennatullah Siam, Moemen Abdel-Razek
Date: 2018-03-10 00:00:00
Tasks: Real-Time Semantic Segmentation, Segmentation, Semantic Segmentation

Real-time semantic segmentation is of significant importance for mobile and
robotics related applications. We propose a computationally efficient
segmentation network which we term as ShuffleSeg. The proposed architecture is
based on grouped convolution and channel shuffling in its encoder for improving
the performance. An ablation study of different decoding methods is compared
including Skip architecture, UNet, and Dilation Frontend. Interesting insights
on the speed and accuracy tradeoff is discussed. It is shown that skip
architecture in the decoding method provides the best compromise for the goal
of real-time performance, while it provides adequate accuracy by utilizing
higher resolution feature maps for a more accurate segmentation. ShuffleSeg is
evaluated on CityScapes and compared against the state of the art real-time
segmentation networks. It achieves 2x GFLOPs reduction, while it provides on
par mean intersection over union of 58.3% on CityScapes test set. ShuffleSeg
runs at 15.7 frames per second on NVIDIA Jetson TX2, which makes it of great
potential for real-time applications.
A Study of Recent Contributions on Information Extraction
Authors: Parisa Naderi Golshan, HosseinAli Rahmani Dashti, Shahrzad Azizi, Leila Safari
Date: 2018-03-15 00:00:00
Tasks: BIG-bench Machine Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Relation, Relation Extraction

This paper reports on modern approaches in Information Extraction (IE) and
its two main sub-tasks of Named Entity Recognition (NER) and Relation
Extraction (RE). Basic concepts and the most recent approaches in this area are
reviewed, which mainly include Machine Learning (ML) based approaches and the
more recent trend to Deep Learning (DL) based methods.
Language Design as Information Renormalization
Authors: Angel J. Gallego, Roman Orus
Date: 2017-08-04 00:00:00
Tasks: Sentence, Tensor Networks

Here we consider some well-known facts in syntax from a physics perspective, allowing us to establish equivalences between both fields with many consequences. Mainly, we observe that the operation MERGE, put forward by N. Chomsky in 1995, can be interpreted as a physical information coarse-graining. Thus, MERGE in linguistics entails information renormalization in physics, according to different time scales. We make this point mathematically formal in terms of language models. In this setting, MERGE amounts to a probability tensor implementing a coarse-graining, akin to a probabilistic context-free grammar. The probability vectors of meaningful sentences are given by stochastic tensor networks (TN) built from diagonal tensors and which are mostly loop-free, such as Tree Tensor Networks and Matrix Product States, thus being computationally very efficient to manipulate. We show that this implies the polynomially-decaying (long-range) correlations experimentally observed in language, and also provides arguments in favour of certain types of neural networks for language processing. Moreover, we show how to obtain such language models from quantum states that can be efficiently prepared on a quantum computer, and use this to find bounds on the perplexity of the probability distribution of words in a sentence. Implications of our results are discussed across several ambits.
Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text
Authors: Ji Wen, Xu sun, Xuancheng Ren, Qi Su
Date: 2018-03-15 00:00:00
Proceeding: NAACL 2018 6
Tasks: General Classification, Relation, Relation Classification

Relation classification is an important semantic processing task in the field
of natural language processing. In this paper, we propose the task of relation
classification for Chinese literature text. A new dataset of Chinese literature
text is constructed to facilitate the study in this task. We present a novel
model, named Structure Regularized Bidirectional Recurrent Convolutional Neural
Network (SR-BRCNN), to identify the relation between entities. The proposed
model learns relation representations along the shortest dependency path (SDP)
extracted from the structure regularized dependency tree, which has the
benefits of reducing the complexity of the whole model. Experimental results
show that the proposed method significantly improves the F1 score by 10.3, and
outperforms the state-of-the-art approaches on Chinese literature text.
Fast Subspace Clustering Based on the Kronecker Product
Authors: Lei Zhou, Xiao Bai, Xianglong Liu, Jun Zhou, Hancock Edwin
Date: 2018-03-15 00:00:00
Tasks: Clustering

Subspace clustering is a useful technique for many computer vision
applications in which the intrinsic dimension of high-dimensional data is often
smaller than the ambient dimension. Spectral clustering, as one of the main
approaches to subspace clustering, often takes on a sparse representation or a
low-rank representation to learn a block diagonal self-representation matrix
for subspace generation. However, existing methods require solving a large
scale convex optimization problem with a large set of data, with computational
complexity reaches O(N^3) for N data points. Therefore, the efficiency and
scalability of traditional spectral clustering methods can not be guaranteed
for large scale datasets. In this paper, we propose a subspace clustering model
based on the Kronecker product. Due to the property that the Kronecker product
of a block diagonal matrix with any other matrix is still a block diagonal
matrix, we can efficiently learn the representation matrix which is formed by
the Kronecker product of k smaller matrices. By doing so, our model
significantly reduces the computational complexity to O(kN^{3/k}). Furthermore,
our model is general in nature, and can be adapted to different regularization
based subspace clustering methods. Experimental results on two public datasets
show that our model significantly improves the efficiency compared with several
state-of-the-art methods. Moreover, we have conducted experiments on synthetic
data to verify the scalability of our model for large scale datasets.