pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Active Learning for Classification and Preference Learning
Authors: Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, Máté Lengyel
Date: 2011-12-24 00:00:00
Tasks: Active Learning, Classification, General Classification

Information theoretic active learning has been widely studied for
probabilistic models. For simple regression an optimal myopic policy is easily
tractable. However, for other tasks and with more complex models, such as
classification with nonparametric models, the optimal solution is harder to
compute. Current approaches make approximations to achieve tractability. We
propose an approach that expresses information gain in terms of predictive
entropies, and apply this method to the Gaussian Process Classifier (GPC). Our
approach makes minimal approximations to the full information theoretic
objective. Our experimental performance compares favourably to many popular
active learning algorithms, and has equal or lower computational complexity. We
compare well to decision theoretic approaches also, which are privy to more
information and require much more computational time. Secondly, by developing
further a reformulation of binary preference learning to a classification
problem, we extend our algorithm to Gaussian Process preference learning.
CornerNet-Lite: Efficient Keypoint Based Object Detection
Authors: Hei Law, Yun Teng, Olga Russakovsky, Jia Deng
Date: 2019-04-18 00:00:00
Tasks: Object, object-detection, Object Detection, Real-Time Object Detection

Keypoint-based methods are a relatively new paradigm in object detection, eliminating the need for anchor boxes and offering a simplified detection framework. Keypoint-based CornerNet achieves state of the art accuracy among single-stage detectors. However, this accuracy comes at high processing cost. In this work, we tackle the problem of efficient keypoint-based object detection and introduce CornerNet-Lite. CornerNet-Lite is a combination of two efficient variants of CornerNet: CornerNet-Saccade, which uses an attention mechanism to eliminate the need for exhaustively processing all pixels of the image, and CornerNet-Squeeze, which introduces a new compact backbone architecture. Together these two variants address the two critical use cases in efficient object detection: improving efficiency without sacrificing accuracy, and improving accuracy at real-time efficiency. CornerNet-Saccade is suitable for offline processing, improving the efficiency of CornerNet by 6.0x and the AP by 1.0% on COCO. CornerNet-Squeeze is suitable for real-time detection, improving both the efficiency and accuracy of the popular real-time detector YOLOv3 (34.4% AP at 30ms for CornerNet-Squeeze compared to 33.0% AP at 39ms for YOLOv3 on COCO). Together these contributions for the first time reveal the potential of keypoint-based detection to be useful for applications requiring processing efficiency.
An Efficient Approximate kNN Graph Method for Diffusion on Image Retrieval
Authors: Federico Magliani, Kevin McGuinness, Eva Mohedano, Andrea Prati
Date: 2019-04-18 00:00:00
Tasks: Image Retrieval, Retrieval

The application of the diffusion in many computer vision and artificial
intelligence projects has been shown to give excellent improvements in
performance. One of the main bottlenecks of this technique is the quadratic
growth of the kNN graph size due to the high-quantity of new connections
between nodes in the graph, resulting in long computation times. Several
strategies have been proposed to address this, but none are effective and
efficient. Our novel technique, based on LSH projections, obtains the same
performance as the exact kNN graph after diffusion, but in less time
(approximately 18 times faster on a dataset of a hundred thousand images). The
proposed method was validated and compared with other state-of-the-art on
several public image datasets, including Oxford5k, Paris6k, and Oxford105k.
Deep Parametric Shape Predictions using Distance Fields
Authors: Dmitriy Smirnov, Matthew Fisher, Vladimir G. Kim, Richard Zhang, Justin Solomon
Date: 2019-04-18 00:00:00
Proceeding: CVPR 2020 6
Tasks: Deep Learning

Many tasks in graphics and vision demand machinery for converting shapes into consistent representations with sparse sets of parameters; these representations facilitate rendering, editing, and storage. When the source data is noisy or ambiguous, however, artists and engineers often manually construct such representations, a tedious and potentially time-consuming process. While advances in deep learning have been successfully applied to noisy geometric data, the task of generating parametric shapes has so far been difficult for these methods. Hence, we propose a new framework for predicting parametric shape primitives using deep learning. We use distance fields to transition between shape parameters like control points and input data on a pixel grid. We demonstrate efficacy on 2D and 3D tasks, including font vectorization and surface abstraction.
Towards VQA Models That Can Read
Authors: Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, Marcus Rohrbach
Date: 2019-04-18 00:00:00
Proceeding: CVPR 2019 6
Tasks: TextVQA, Visual Question Answering (VQA)

Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today's VQA models can not read! Our paper takes a first step towards addressing this problem. First, we introduce a new "TextVQA" dataset to facilitate progress on this important problem. Existing datasets either have a small proportion of questions about text (e.g., the VQA dataset) or are too small (e.g., the VizWiz dataset). TextVQA contains 45,336 questions on 28,408 images that require reasoning about text to answer. Second, we introduce a novel model architecture that reads text in the image, reasons about it in the context of the image and the question, and predicts an answer which might be a deduction based on the text and the image or composed of the strings found in the image. Consequently, we call our approach Look, Read, Reason & Answer (LoRRA). We show that LoRRA outperforms existing state-of-the-art VQA models on our TextVQA dataset. We find that the gap between human performance and machine performance is significantly larger on TextVQA than on VQA 2.0, suggesting that TextVQA is well-suited to benchmark progress along directions complementary to VQA 2.0.
Attentive Single-Tasking of Multiple Tasks
Authors: Kevis-Kokitsi Maninis, Ilija Radosavovic, Iasonas Kokkinos
Date: 2019-04-18 00:00:00
Proceeding: CVPR 2019 6

In this work we address task interference in universal networks by
considering that a network is trained on multiple tasks, but performs one task
at a time, an approach we refer to as "single-tasking multiple tasks". The
network thus modifies its behaviour through task-dependent feature adaptation,
or task attention. This gives the network the ability to accentuate the
features that are adapted to a task, while shunning irrelevant ones. We further
reduce task interference by forcing the task gradients to be statistically
indistinguishable through adversarial training, ensuring that the common
backbone architecture serving all tasks is not dominated by any of the
task-specific gradients. Results in three multi-task dense labelling problems
consistently show: (i) a large reduction in the number of parameters while
preserving, or even improving performance and (ii) a smooth trade-off between
computation and multi-task accuracy. We provide our system's code and
pre-trained models at http://vision.ee.ethz.ch/~kmaninis/astmt/.
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
Authors: Daniel S. Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, Quoc V. Le
Date: 2019-04-18 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Language Modeling, Language Modelling, Speech Recognition

We present SpecAugment, a simple data augmentation method for speech recognition. SpecAugment is applied directly to the feature inputs of a neural network (i.e., filter bank coefficients). The augmentation policy consists of warping the features, masking blocks of frequency channels, and masking blocks of time steps. We apply SpecAugment on Listen, Attend and Spell networks for end-to-end speech recognition tasks. We achieve state-of-the-art performance on the LibriSpeech 960h and Swichboard 300h tasks, outperforming all prior work. On LibriSpeech, we achieve 6.8% WER on test-other without the use of a language model, and 5.8% WER with shallow fusion with a language model. This compares to the previous state-of-the-art hybrid system of 7.5% WER. For Switchboard, we achieve 7.2%/14.6% on the Switchboard/CallHome portion of the Hub5'00 test set without the use of a language model, and 6.8%/14.1% with shallow fusion, which compares to the previous state-of-the-art hybrid system at 8.3%/17.3% WER.
Early Detection of Injuries in MLB Pitchers from Video
Authors: AJ Piergiovanni, Michael S. Ryoo
Date: 2019-04-18 00:00:00
Tasks: Video Recognition

Injuries are a major cost in sports. Teams spend millions of dollars every
year on players who are hurt and unable to play, resulting in lost games,
decreased fan interest and additional wages for replacement players. Modern
convolutional neural networks have been successfully applied to many video
recognition tasks. In this paper, we introduce the problem of injury
detection/prediction in MLB pitchers and experimentally evaluate the ability of
such convolutional models to detect and predict injuries in pitches only from
video data. We conduct experiments on a large dataset of TV broadcast MLB
videos of 20 different pitchers who were injured during the 2017 season. We
experimentally evaluate the model's performance on each individual pitcher, how
well it generalizes to new pitchers, how it performs for various injuries, and
how early it can predict or detect an injury.
Decoding Molecular Graph Embeddings with Reinforcement Learning
Authors: Steven Kearnes, Li Li, Patrick Riley
Date: 2019-04-18 00:00:00
Tasks: Graph Matching, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present RL-VAE, a graph-to-graph variational autoencoder that uses reinforcement learning to decode molecular graphs from latent embeddings. Methods have been described previously for graph-to-graph autoencoding, but these approaches require sophisticated decoders that increase the complexity of training and evaluation (such as requiring parallel encoders and decoders or non-trivial graph matching). Here, we repurpose a simple graph generator to enable efficient decoding and generation of molecular graphs.
Deep Rigid Instance Scene Flow
Authors: Wei-Chiu Ma, Shenlong Wang, Rui Hu, Yuwen Xiong, Raquel Urtasun
Date: 2019-04-18 00:00:00
Proceeding: CVPR 2019 6
Tasks: GPU, Rolling Shutter Correction, Scene Flow Estimation

In this paper we tackle the problem of scene flow estimation in the context
of self-driving. We leverage deep learning techniques as well as strong priors
as in our application domain the motion of the scene can be composed by the
motion of the robot and the 3D motion of the actors in the scene. We formulate
the problem as energy minimization in a deep structured model, which can be
solved efficiently in the GPU by unrolling a Gaussian-Newton solver. Our
experiments in the challenging KITTI scene flow dataset show that we outperform
the state-of-the-art by a very large margin, while being 800 times faster.
Combating the Elsagate phenomenon: Deep learning architectures for disturbing cartoons
Authors: Akari Ishikawa, Edson Bollis, Sandra Avila
Date: 2019-04-18 00:00:00
Tasks: Deep Learning, Pornography Detection

Watching cartoons can be useful for children's intellectual, social and
emotional development. However, the most popular video sharing platform today
provides many videos with Elsagate content. Elsagate is a phenomenon that
depicts childhood characters in disturbing circumstances (e.g., gore, toilet
humor, drinking urine, stealing). Even with this threat easily available for
children, there is no work in the literature addressing the problem. As the
first to explore disturbing content in cartoons, we proceed from the most
recent pornography detection literature applying deep convolutional neural
networks combined with static and motion information of the video. Our solution
is compatible with mobile platforms and achieved 92.6% of accuracy. Our goal is
not only to introduce the first solution but also to bring up the discussion
around Elsagate.
KPConv: Flexible and Deformable Convolution for Point Clouds
Authors: Hugues Thomas, Charles R. Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, Leonidas J. Guibas
Date: 2019-04-18 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Part Segmentation, 3D Point Cloud Classification, 3D Semantic Segmentation, Descriptive, LIDAR Semantic Segmentation, Robust 3D Semantic Segmentation, Scene Segmentation, Semantic Segmentation

We present Kernel Point Convolution (KPConv), a new design of point convolution, i.e. that operates on point clouds without any intermediate representation. The convolution weights of KPConv are located in Euclidean space by kernel points, and applied to the input points close to them. Its capacity to use any number of kernel points gives KPConv more flexibility than fixed grid convolutions. Furthermore, these locations are continuous in space and can be learned by the network. Therefore, KPConv can be extended to deformable convolutions that learn to adapt kernel points to local geometry. Thanks to a regular subsampling strategy, KPConv is also efficient and robust to varying densities. Whether they use deformable KPConv for complex tasks, or rigid KPconv for simpler tasks, our networks outperform state-of-the-art classification and segmentation approaches on several datasets. We also offer ablation studies and visualizations to provide understanding of what has been learned by KPConv and to validate the descriptive power of deformable KPConv.
No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement
Authors: Jia Yan, Jie Li, Xin Fu
Date: 2019-04-18 00:00:00
Tasks: Image Quality Assessment, No-Reference Image Quality Assessment

No-reference image quality assessment (NR-IQA) aims to measure the image
quality without reference image. However, contrast distortion has been
overlooked in the current research of NR-IQA. In this paper, we propose a very
simple but effective metric for predicting quality of contrast-altered images
based on the fact that a high-contrast image is often more similar to its
contrast enhanced image. Specifically, we first generate an enhanced image
through histogram equalization. We then calculate the similarity of the
original image and the enhanced one by using structural-similarity index (SSIM)
as the first feature. Further, we calculate the histogram based entropy and
cross entropy between the original image and the enhanced one respectively, to
gain a sum of 4 features. Finally, we learn a regression module to fuse the
aforementioned 5 features for inferring the quality score. Experiments on four
publicly available databases validate the superiority and efficiency of the
proposed technique.
DScribe: Library of Descriptors for Machine Learning in Materials Science
Authors: Lauri Himanen, Marc O. J. Jäger, Eiaki V. Morooka, Filippo Federici Canova, Yashasvi S. Ranawat, David Z. Gao, Patrick Rinke, Adam S. Foster
Date: 2019-04-18 00:00:00
Tasks: BIG-bench Machine Learning, Formation Energy, Prediction, Property Prediction

DScribe is a software package for machine learning that provides popular
feature transformations ("descriptors") for atomistic materials simulations.
DScribe accelerates the application of machine learning for atomistic property
prediction by providing user-friendly, off-the-shelf descriptor
implementations. The package currently contains implementations for Coulomb
matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR),
Atom-centered Symmetry Function (ACSF) and Smooth Overlap of Atomic Positions
(SOAP). Usage of the package is illustrated for two different applications:
formation energy prediction for solids and ionic charge prediction for atoms in
organic molecules. The package is freely available under the open-source Apache
License 2.0.
Codes, Functions, and Causes: A Critique of Brette's Conceptual Analysis of Coding
Authors: David Barack, Andrew Jaegle
Date: 2019-04-18 00:00:00

In a recent article, Brette argues that coding as a concept is inappropriate
for explanations of neurocognitive phenomena. Here, we argue that Brette's
conceptual analysis mischaracterizes the structure of causal claims in coding
and other forms of analysis-by-decomposition. We argue that analyses of this
form are permissible, conceptually coherent, and offer essential tools for
building and developing models of neurocognitive systems like the brain.
Salient Object Detection: A Distinctive Feature Integration Model
Authors: Abdullah J. Alzahrani, Hina Afridi
Date: 2019-04-18 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

We propose a novel method for salient object detection in different images.
Our method integrates spatial features for efficient and robust representation
to capture meaningful information about the salient objects. We then train a
conditional random field (CRF) using the integrated features. The trained CRF
model is then used to detect salient objects during the online testing stage.
We perform experiments on two standard datasets and compare the performance of
our method with different reference methods. Our experiments show that our
method outperforms the compared methods in terms of precision, recall, and
F-Measure.
Enhanced Center Coding for Cell Detection with Convolutional Neural Networks
Authors: Haoyi Liang, Aijaz Naik, Cedric L. Williams, Jaideep Kapur, Daniel S. Weller
Date: 2019-04-18 00:00:00
Tasks: Cell Detection

Cell imaging and analysis are fundamental to biomedical research because
cells are the basic functional units of life. Among different cell-related
analysis, cell counting and detection are widely used. In this paper, we focus
on one common step of learning-based cell counting approaches: coding the raw
dot labels into more suitable maps for learning. Two criteria of coding raw dot
labels are discussed, and a new coding scheme is proposed in this paper. The
two criteria measure how easy it is to train the model with a coding scheme,
and how robust the recovered raw dot labels are when predicting. The most
compelling advantage of the proposed coding scheme is the ability to
distinguish neighboring cells in crowded regions. Cell counting and detection
experiments are conducted for five coding schemes on four types of cells and
two network architectures. The proposed coding scheme improves the counting
accuracy versus the widely-used Gaussian and rectangle kernels up to 12%, and
also improves the detection accuracy versus the common proximity coding up to
14%.
The Simplest Thing That Can Possibly Work: Pseudo-Relevance Feedback Using Text Classification
Authors: Jimmy Lin
Date: 2019-04-18 00:00:00
Tasks: General Classification, text-classification, Text Classification

Motivated by recent commentary that has questioned today's pursuit of
ever-more complex models and mathematical formalisms in applied machine
learning and whether meaningful empirical progress is actually being made, this
paper tries to tackle the decades-old problem of pseudo-relevance feedback with
"the simplest thing that can possibly work". I present a technique based on
training a document relevance classifier for each information need using
pseudo-labels from an initial ranked list and then applying the classifier to
rerank the retrieved documents. Experiments demonstrate significant
improvements across a number of newswire collections, with initial rankings
supplied by "bag of words" BM25 as well as from a well-tuned query expansion
model. While this simple technique draws elements from several well-known
threads in the literature, to my knowledge this exact combination has not
previously been proposed and evaluated.
Inspecting and Interacting with Meaningful Music Representations using VAE
Authors: Ruihan Yang, Tianyao Chen, Yiyi Zhang, Gus Xia
Date: 2019-04-18 00:00:00
Tasks: Disentanglement, Image Generation, Music Generation, Rhythm

Variational Autoencoders(VAEs) have already achieved great results on image
generation and recently made promising progress on music generation. However,
the generation process is still quite difficult to control in the sense that
the learned latent representations lack meaningful music semantics. It would be
much more useful if people can modify certain music features, such as rhythm
and pitch contour, via latent representations to test different composition
ideas. In this paper, we propose a new method to inspect the pitch and rhythm
interpretations of the latent representations and we name it disentanglement by
augmentation. Based on the interpretable representations, an intuitive
graphical user interface is designed for users to better direct the music
creation process by manipulating the pitch contours and rhythmic complexity.
On the validity of memristor modeling in the neural network literature
Authors: Y. V. Pershin, M. Di Ventra
Date: 2019-04-18 00:00:00

An analysis of the literature shows that there are two types of
non-memristive models that have been widely used in the modeling of so-called
"memristive" neural networks. Here, we demonstrate that such models have
nothing in common with the concept of memristive elements: they describe either
non-linear resistors or certain bi-state systems, which all are devices without
memory. Therefore, the results presented in a significant number of
publications are at least questionable, if not completely irrelevant to the
actual field of memristive neural networks.
Clause-Wise and Recursive Decoding for Complex and Cross-Domain Text-to-SQL Generation
Authors: Dongjun Lee
Date: 2019-04-18 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Text to SQL, Text-To-SQL

Most deep learning approaches for text-to-SQL generation are limited to the WikiSQL dataset, which only supports very simple queries over a single table. We focus on the Spider dataset, a complex and cross-domain text-to-SQL task, which includes complex queries over multiple tables. In this paper, we propose a SQL clause-wise decoding neural architecture with a self-attention based database schema encoder to address the Spider task. Each of the clause-specific decoders consists of a set of sub-modules, which is defined by the syntax of each clause. Additionally, our model works recursively to support nested queries. When evaluated on the Spider dataset, our approach achieves 4.6\% and 9.8\% accuracy gain in the test and dev sets, respectively. In addition, we show that our model is significantly more effective at predicting complex and nested queries than previous work.