pwc-archive/papers-with-abstracts huggingface dataset:
Weakly Supervised Semantic Segmentation of Satellite Images
Authors: Adrien Nivaggioli, Hicham Randrianarivo
Date: 2019-04-08 00:00:00
Tasks: Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

When one wants to train a neural network to perform semantic segmentation,
creating pixel-level annotations for each of the images in the database is a
tedious task. If he works with aerial or satellite images, which are usually
very large, it is even worse. With that in mind, we investigate how to use
image-level annotations in order to perform semantic segmentation. Image-level
annotations are much less expensive to acquire than pixel-level annotations,
but we lose a lot of information for the training of the model. From the
annotations of the images, the model must find by itself how to classify the
different regions of the image. In this work, we use the method proposed by Anh
and Kwak [1] to produce pixel-level annotation from image level annotation. We
compare the overall quality of our generated dataset with the original dataset.
In addition, we propose an adaptation of the AffinityNet that allows us to
directly perform a semantic segmentation. Our results show that the generated
labels lead to the same performances for the training of several segmentation
networks. Also, the quality of semantic segmentation performed directly by the
AffinityNet and the Random Walk is close to the one of the best
fully-supervised approaches.
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images
Authors: Lefei Zhang, Qian Zhang, Bo Du, Xin Huang, Yuan Yan Tang, DaCheng Tao
Date: 2019-04-08 00:00:00
Tasks: Dimensionality Reduction, feature selection, General Classification, image-classification, Image Classification

In hyperspectral remote sensing data mining, it is important to take into
account of both spectral and spatial information, such as the spectral
signature, texture feature and morphological property, to improve the
performances, e.g., the image classification accuracy. In a feature
representation point of view, a nature approach to handle this situation is to
concatenate the spectral and spatial features into a single but high
dimensional vector and then apply a certain dimension reduction technique
directly on that concatenated vector before feed it into the subsequent
classifier. However, multiple features from various domains definitely have
different physical meanings and statistical properties, and thus such
concatenation hasn't efficiently explore the complementary properties among
different features, which should benefit for boost the feature
discriminability. Furthermore, it is also difficult to interpret the
transformed results of the concatenated vector. Consequently, finding a
physically meaningful consensus low dimensional feature representation of
original multiple features is still a challenging task. In order to address the
these issues, we propose a novel feature learning framework, i.e., the
simultaneous spectral-spatial feature selection and extraction algorithm, for
hyperspectral images spectral-spatial feature representation and
classification. Specifically, the proposed method learns a latent low
dimensional subspace by projecting the spectral-spatial feature into a common
feature space, where the complementary information has been effectively
exploited, and simultaneously, only the most significant original features have
been transformed. Encouraging experimental results on three public available
hyperspectral remote sensing datasets confirm that our proposed method is
effective and efficient.
VayuAnukulani: Adaptive Memory Networks for Air Pollution Forecasting
Authors: Divyam Madaan, Radhika Dua, Prerana Mukherjee, Brejesh lall
Date: 2019-04-08 00:00:00

Air pollution is the leading environmental health hazard globally due to
various sources which include factory emissions, car exhaust and cooking
stoves. As a precautionary measure, air pollution forecast serves as the basis
for taking effective pollution control measures, and accurate air pollution
forecasting has become an important task. In this paper, we forecast
fine-grained ambient air quality information for 5 prominent locations in Delhi
based on the historical and real-time ambient air quality and meteorological
data reported by Central Pollution Control board. We present VayuAnukulani
system, a novel end-to-end solution to predict air quality for next 24 hours by
estimating the concentration and level of different air pollutants including
nitrogen dioxide ($NO_2$), particulate matter ($PM_{2.5}$ and $PM_{10}$) for
Delhi. Extensive experiments on data sources obtained in Delhi demonstrate that
the proposed adaptive attention based Bidirectional LSTM Network outperforms
several baselines for classification and regression models. The accuracy of the
proposed adaptive system is $\sim 15 - 20\%$ better than the same offline
trained model. We compare the proposed methodology on several competing
baselines, and show that the network outperforms conventional methods by $\sim
3 - 5 \%$.
GELP: GAN-Excited Linear Prediction for Speech Synthesis from Mel-spectrogram
Authors: Lauri Juvela, Bajibabu Bollepalli, Junichi Yamagishi, Paavo Alku
Date: 2019-04-08 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech

Recent advances in neural network -based text-to-speech have reached human level naturalness in synthetic speech. The present sequence-to-sequence models can directly map text to mel-spectrogram acoustic features, which are convenient for modeling, but present additional challenges for vocoding (i.e., waveform generation from the acoustic features). High-quality synthesis can be achieved with neural vocoders, such as WaveNet, but such autoregressive models suffer from slow sequential inference. Meanwhile, their existing parallel inference counterparts are difficult to train and require increasingly large model sizes. In this paper, we propose an alternative training strategy for a parallel neural vocoder utilizing generative adversarial networks, and integrate a linear predictive synthesis filter into the model. Results show that the proposed model achieves significant improvement in inference speed, while outperforming a WaveNet in copy-synthesis quality.
Adaptive Morphological Reconstruction for Seeded Image Segmentation
Authors: Tao Lei, Xiaohong Jia, Tongliang Liu, Shigang Liu, Hongy-ing Meng, Asoke K. Nandi
Date: 2019-04-08 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Morphological reconstruction (MR) is often employed by seeded image
segmentation algorithms such as watershed transform and power watershed as it
is able to filter seeds (regional minima) to reduce over-segmentation. However,
MR might mistakenly filter meaningful seeds that are required for generating
accurate segmentation and it is also sensitive to the scale because a
single-scale structuring element is employed. In this paper, a novel adaptive
morphological reconstruction (AMR) operation is proposed that has three
advantages. Firstly, AMR can adaptively filter useless seeds while preserving
meaningful ones. Secondly, AMR is insensitive to the scale of structuring
elements because multiscale structuring elements are employed. Finally, AMR has
two attractive properties: monotonic increasingness and convergence that help
seeded segmentation algorithms to achieve a hierarchical segmentation.
Experiments clearly demonstrate that AMR is useful for improving algorithms of
seeded image segmentation and seed-based spectral segmentation. Compared to
several state-of-the-art algorithms, the proposed algorithms provide better
segmentation results requiring less computing time. Source code is available at
https://github.com/SUST-reynole/AMR.
Jointly Measuring Diversity and Quality in Text Generation Models
Authors: Ehsan Montahaei, Danial Alihosseini, Mahdieh Soleymani Baghshah
Date: 2019-04-08 00:00:00
Proceeding: NAACL 2019 6
Tasks: Diversity, Sentence, Text Generation

Text generation is an important Natural Language Processing task with various applications. Although several metrics have already been introduced to evaluate the text generation methods, each of them has its own shortcomings. The most widely used metrics such as BLEU only consider the quality of generated sentences and neglect their diversity. For example, repeatedly generation of only one high quality sentence would result in a high BLEU score. On the other hand, the more recent metric introduced to evaluate the diversity of generated texts known as Self-BLEU ignores the quality of generated texts. In this paper, we propose metrics to evaluate both the quality and diversity simultaneously by approximating the distance of the learned generative model and the real data distribution. For this purpose, we first introduce a metric that approximates this distance using n-gram based measures. Then, a feature-based measure which is based on a recent highly deep model trained on a large text corpus called BERT is introduced. Finally, for oracle training mode in which the generator's density can also be calculated, we propose to use the distance measures between the corresponding explicit distributions. Eventually, the most popular and recent text generation models are evaluated using both the existing and the proposed metrics and the preferences of the proposed metrics are determined.
Issue Framing in Online Discussion Fora
Authors: Mareike Hartmann, Tallulah Jansen, Isabelle Augenstein, Anders Søgaard
Date: 2019-04-08 00:00:00
Proceeding: NAACL 2019 6

In online discussion fora, speakers often make arguments for or against
something, say birth control, by highlighting certain aspects of the topic. In
social science, this is referred to as issue framing. In this paper, we
introduce a new issue frame annotated corpus of online discussions. We explore
to what extent models trained to detect issue frames in newswire and social
media can be transferred to the domain of discussion fora, using a combination
of multi-task and adversarial training, assuming only unlabeled training data
in the target domain.
Bounded rational decision-making from elementary computations that reduce uncertainty
Authors: Sebastian Gottwald, Daniel A. Braun
Date: 2019-04-08 00:00:00
Tasks: Decision Making

In its most basic form, decision-making can be viewed as a computational
process that progressively eliminates alternatives, thereby reducing
uncertainty. Such processes are generally costly, meaning that the amount of
uncertainty that can be reduced is limited by the amount of available
computational resources. Here, we introduce the notion of elementary
computation based on a fundamental principle for probability transfers that
reduce uncertainty. Elementary computations can be considered as the inverse of
Pigou-Dalton transfers applied to probability distributions, closely related to
the concepts of majorization, T-transforms, and generalized entropies that
induce a preorder on the space of probability distributions. As a consequence
we can define resource cost functions that are order-preserving and therefore
monotonic with respect to the uncertainty reduction. This leads to a
comprehensive notion of decision-making processes with limited resources. Along
the way, we prove several new results on majorization theory, as well as on
entropy and divergence measures.
Filter Pruning by Switching to Neighboring CNNs with Good Attributes
Authors: Yang He, Ping Liu, Linchao Zhu, Yi Yang
Date: 2019-04-08 00:00:00
Tasks: Attribute, image-classification, Image Classification

Filter pruning is effective to reduce the computational costs of neural networks. Existing methods show that updating the previous pruned filter would enable large model capacity and achieve better performance. However, during the iterative pruning process, even if the network weights are updated to new values, the pruning criterion remains the same. In addition, when evaluating the filter importance, only the magnitude information of the filters is considered. However, in neural networks, filters do not work individually, but they would affect other filters. As a result, the magnitude information of each filter, which merely reflects the information of an individual filter itself, is not enough to judge the filter importance. To solve the above problems, we propose Meta-attribute-based Filter Pruning (MFP). First, to expand the existing magnitude information based pruning criteria, we introduce a new set of criteria to consider the geometric distance of filters. Additionally, to explicitly assess the current state of the network, we adaptively select the most suitable criteria for pruning via a meta-attribute, a property of the neural network at the current state. Experiments on two image classification benchmarks validate our method. For ResNet-50 on ILSVRC-2012, we could reduce more than 50% FLOPs with only 0.44% top-5 accuracy loss.
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations
Authors: Christian A. Scholbeck, Christoph Molnar, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio
Date: 2019-04-08 00:00:00
Tasks: Feature Importance

Model-agnostic interpretation techniques allow us to explain the behavior of any predictive model. Due to different notations and terminology, it is difficult to see how they are related. A unified view on these methods has been missing. We present the generalized SIPA (sampling, intervention, prediction, aggregation) framework of work stages for model-agnostic interpretations and demonstrate how several prominent methods for feature effects can be embedded into the proposed framework. Furthermore, we extend the framework to feature importance computations by pointing out how variance-based and performance-based importance measures are based on the same work stages. The SIPA framework reduces the diverse set of model-agnostic techniques to a single methodology and establishes a common terminology to discuss them in future work.
Kervolutional Neural Networks
Authors: Chen Wang, Jianfei Yang, Lihua Xie, Junsong Yuan
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6

Convolutional neural networks (CNNs) have enabled the state-of-the-art performance in many computer vision tasks. However, little effort has been devoted to establishing convolution in non-linear space. Existing works mainly leverage on the activation layers, which can only provide point-wise non-linearity. To solve this problem, a new operation, kervolution (kernel convolution), is introduced to approximate complex behaviors of human perception systems leveraging on the kernel trick. It generalizes convolution, enhances the model capacity, and captures higher order interactions of features, via patch-wise kernel functions, but without introducing additional parameters. Extensive experiments show that kervolutional neural networks (KNN) achieve higher accuracy and faster convergence than baseline CNN.
Feature Learning Viewpoint of AdaBoost and a New Algorithm
Authors: Fei Wang, Zhongheng Li, Fang He, Rong Wang, Weizhong Yu, Feiping Nie
Date: 2019-04-08 00:00:00

The AdaBoost algorithm has the superiority of resisting overfitting.
Understanding the mysteries of this phenomena is a very fascinating fundamental
theoretical problem. Many studies are devoted to explaining it from statistical
view and margin theory. In this paper, we illustrate it from feature learning
viewpoint, and propose the AdaBoost+SVM algorithm, which can explain the
resistant to overfitting of AdaBoost directly and easily to understand.
Firstly, we adopt the AdaBoost algorithm to learn the base classifiers. Then,
instead of directly weighted combination the base classifiers, we regard them
as features and input them to SVM classifier. With this, the new coefficient
and bias can be obtained, which can be used to construct the final classifier.
We explain the rationality of this and illustrate the theorem that when the
dimension of these features increases, the performance of SVM would not be
worse, which can explain the resistant to overfitting of AdaBoost.
Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning
Authors: Alessandro Bianchi, Moreno Raimondo Vendra, Pavlos Protopapas, Marco Brambilla
Date: 2019-04-08 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Transfer Learning

Image quality plays a big role in CNN-based image classification performance.
Fine-tuning the network with distorted samples may be too costly for large
networks. To solve this issue, we propose a transfer learning approach
optimized to keep into account that in each layer of a CNN some filters are
more susceptible to image distortion than others. Our method identifies the
most susceptible filters and applies retraining only to the filters that show
the highest activation maps distance between clean and distorted images.
Filters are ranked using the Borda count election method and then only the most
affected filters are fine-tuned. This significantly reduces the number of
parameters to retrain. We evaluate this approach on the CIFAR-10 and CIFAR-100
datasets, testing it on two different models and two different types of
distortion. Results show that the proposed transfer learning technique recovers
most of the lost performance due to input data distortion, at a considerably
faster pace with respect to existing methods, thanks to the reduced number of
parameters to fine-tune. When few noisy samples are provided for training, our
filter-level fine tuning performs particularly well, also outperforming state
of the art layer-level transfer learning approaches.
Component-Wise Boosting of Targets for Multi-Output Prediction
Authors: Quay Au, Daniel Schalk, Giuseppe Casalicchio, Ramona Schoedel, Clemens Stachl, Bernd Bischl
Date: 2019-04-08 00:00:00
Tasks: Multi-Label Learning, Prediction, regression

Multi-output prediction deals with the prediction of several targets of
possibly diverse types. One way to address this problem is the so called
problem transformation method. This method is often used in multi-label
learning, but can also be used for multi-output prediction due to its
generality and simplicity. In this paper, we introduce an algorithm that uses
the problem transformation method for multi-output prediction, while
simultaneously learning the dependencies between target variables in a sparse
and interpretable manner. In a first step, predictions are obtained for each
target individually. Target dependencies are then learned via a component-wise
boosting approach. We compare our new method with similar approaches in a
benchmark using multi-label, multivariate regression and mixed-type datasets.
Variational Uncalibrated Photometric Stereo under General Lighting
Authors: Bjoern Haefner, Zhenzhang Ye, Maolin Gao, Tao Wu, Yvain Quéau, Daniel Cremers
Date: 2019-04-08 00:00:00
Proceeding: ICCV 2019 10

Photometric stereo (PS) techniques nowadays remain constrained to an ideal laboratory setup where modeling and calibration of lighting is amenable. To eliminate such restrictions, we propose an efficient principled variational approach to uncalibrated PS under general illumination. To this end, the Lambertian reflectance model is approximated through a spherical harmonic expansion, which preserves the spatial invariance of the lighting. The joint recovery of shape, reflectance and illumination is then formulated as a single variational problem. There the shape estimation is carried out directly in terms of the underlying perspective depth map, thus implicitly ensuring integrability and bypassing the need for a subsequent normal integration. To tackle the resulting nonconvex problem numerically, we undertake a two-phase procedure to initialize a balloon-like perspective depth map, followed by a "lagged" block coordinate descent scheme. The experiments validate efficiency and robustness of this approach. Across a variety of evaluations, we are able to reduce the mean angular error consistently by a factor of 2-3 compared to the state-of-the-art.
Minimal Solvers for Mini-Loop Closures in 3D Multi-Scan Alignment
Authors: Pedro Miraldo, Surojit Saha, Srikumar Ramalingam
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6

3D scan registration is a classical, yet a highly useful problem in the
context of 3D sensors such as Kinect and Velodyne. While there are several
existing methods, the techniques are usually incremental where adjacent scans
are registered first to obtain the initial poses, followed by motion averaging
and bundle-adjustment refinement. In this paper, we take a different approach
and develop minimal solvers for jointly computing the initial poses of cameras
in small loops such as 3-, 4-, and 5-cycles. Note that the classical
registration of 2 scans can be done using a minimum of 3 point matches to
compute 6 degrees of relative motion. On the other hand, to jointly compute the
3D registrations in n-cycles, we take 2 point matches between the first n-1
consecutive pairs (i.e., Scan 1 & Scan 2, ... , and Scan n-1 & Scan n) and 1 or
2 point matches between Scan 1 and Scan n. Overall, we use 5, 7, and 10 point
matches for 3-, 4-, and 5-cycles, and recover 12, 18, and 24 degrees of
transformation variables, respectively. Using simulations and real-data we show
that the 3D registration using mini n-cycles are computationally efficient, and
can provide alternate and better initial poses compared to standard pairwise
methods.
Wasserstein Adversarial Regularization (WAR) on label noise
Authors: Kilian Fatras, Bharath Bhushan Damodaran, Sylvain Lobry, Rémi Flamary, Devis Tuia, Nicolas Courty
Date: 2019-04-08 00:00:00
Tasks: Semantic Segmentation

Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning robust classifiers in presence of noisy data. To achieve this goal, we propose a new adversarial regularization scheme based on the Wasserstein distance. Using this distance allows taking into account specific relations between classes by leveraging the geometric properties of the labels space. Our Wasserstein Adversarial Regularization (WAR) encodes a selective regularization, which promotes smoothness of the classifier between some classes, while preserving sufficient complexity of the decision boundary between others. We first discuss how and why adversarial regularization can be used in the context of label noise and then show the effectiveness of our method on five datasets corrupted with noisy labels: in both benchmarks and real datasets, WAR outperforms the state-of-the-art competitors.