pwc-archive/papers-with-abstracts huggingface dataset:
Source codes in human communication
Authors: Michael Ramscar
Date: 2019-03-08 00:00:00

Although information theoretic characterizations of human communication have
become increasingly popular in linguistics, to date they have largely involved
grafting probabilistic constructs onto older ideas about grammar. Similarities
between human and digital communication have been strongly emphasized, and
differences largely ignored. However, some of these differences matter:
communication systems are based on predefined codes shared by every
sender-receiver, whereas the distributions of words in natural languages
guarantee that no speaker-hearer ever has access to an entire linguistic code,
which seemingly undermines the idea that natural languages are probabilistic
systems in any meaningful sense. This paper describes how the distributional
properties of languages meet the various challenges arising from the
differences between information systems and natural languages, along with the
very different view of human communication these properties suggest.
Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
Authors: Nicolas Coudray, Paolo Santiago Ocampo, Theodore Sakellaropoulos, Navneet Narula, Matija Snuderl, David Fenyö, Andre L. Moreira, Narges Razavian, Aristotelis Tsirigos
Date: 2018-09-17 00:00:00
Proceeding: Nature Medicine 2018 9
Tasks: General Classification, Lung Cancer Diagnosis, whole slide images

Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them—STK11, EGFR, FAT1, SETBP1, KRAS and TP53—can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH.
pdbmine: A Node.js API for the RCSB Protein Data Bank (PDB)
Authors: Newlyn N. Joseph, Raktim N. Roy, Thomas A. Steitz
Date: 2019-04-03 00:00:00

Summary: The advent of Web-based tools that assist in the analysis and
visualization of macromolecules require application programming interfaces
(APIs) designed for modern web frameworks. To this end, we have developed a
Node.js module pdbmine that allows any user to generate faster data-request
queries to the RCSB Protein Data Bank (PDB). This JavaScript API acts as a
layer over the XML-based RCSB PDB RESTful API. The relatively simple nature of
the function calls within this module allows the user to easily implement and
integrate pdbmine into larger Node.js web applications.
  Availability: This module can be installed via the Node Package Manager (NPM)
at https://www.npmjs.com/package/pdbmine/, and is hosted on GitHub under the
open-source MIT license at https://github.com/nnj1/pdbmine/. Relevant
documentation is detailed at https://nnj1.github.io/pdbmine/
Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks
Authors: Yauhen Babakhin, Artsiom Sanakoyeu, Hirotoshi Kitamura
Date: 2019-04-09 00:00:00
Tasks: Geophysics, Seismic Imaging

Seismic image analysis plays a crucial role in a wide range of industrial applications and has been receiving significant attention. One of the essential challenges of seismic imaging is detecting subsurface salt structure which is indispensable for identification of hydrocarbon reservoirs and drill path planning. Unfortunately, exact identification of large salt deposits is notoriously difficult and professional seismic imaging often requires expert human interpretation of salt bodies. Convolutional neural networks (CNNs) have been successfully applied in many fields, and several attempts have been made in the field of seismic imaging. But the high cost of manual annotations by geophysics experts and scarce publicly available labeled datasets hinder the performance of the existing CNN-based methods. In this work, we propose a semi-supervised method for segmentation (delineation) of salt bodies in seismic images which utilizes unlabeled data for multi-round self-training. To reduce error amplification during self-training we propose a scheme which uses an ensemble of CNNs. We show that our approach outperforms state-of-the-art on the TGS Salt Identification Challenge dataset and is ranked the first among the 3234 competing methods.
Active Domain Randomization
Authors: Bhairav Mehta, Manfred Diaz, Florian Golemo, Christopher J. Pal, Liam Paull
Date: 2019-04-09 00:00:00
Tasks: Attribute

Domain randomization is a popular technique for improving domain transfer, often used in a zero-shot setting when the target domain is unknown or cannot easily be used for training. In this work, we empirically examine the effects of domain randomization on agent generalization. Our experiments show that domain randomization may lead to suboptimal, high-variance policies, which we attribute to the uniform sampling of environment parameters. We propose Active Domain Randomization, a novel algorithm that learns a parameter sampling strategy. Our method looks for the most informative environment variations within the given randomization ranges by leveraging the discrepancies of policy rollouts in randomized and reference environment instances. We find that training more frequently on these instances leads to better overall agent generalization. Our experiments across various physics-based simulated and real-robot tasks show that this enhancement leads to more robust, consistent policies.
Block Neural Autoregressive Flow
Authors: Nicola De Cao, Ivan Titov, Wilker Aziz
Date: 2019-04-09 00:00:00
Tasks: Density Estimation, Normalising Flows

Normalising flows (NFS) map two density functions via a differentiable
bijection whose Jacobian determinant can be computed efficiently. Recently, as
an alternative to hand-crafted bijections, Huang et al. (2018) proposed neural
autoregressive flow (NAF) which is a universal approximator for density
functions. Their flow is a neural network (NN) whose parameters are predicted
by another NN. The latter grows quadratically with the size of the former and
thus an efficient technique for parametrization is needed. We propose block
neural autoregressive flow (B-NAF), a much more compact universal approximator
of density functions, where we model a bijection directly using a single
feed-forward network. Invertibility is ensured by carefully designing each
affine transformation with block matrices that make the flow autoregressive and
(strictly) monotone. We compare B-NAF to NAF and other established flows on
density estimation and approximate inference for latent variable models. Our
proposed flow is competitive across datasets while using orders of magnitude
fewer parameters.
Functional Isolation Forest
Authors: Guillaume Staerman, Pavlo Mozharovskyi, Stephan Clémençon, Florence d'Alché-Buc
Date: 2019-04-09 00:00:00
Tasks: Anomaly Detection

For the purpose of monitoring the behavior of complex infrastructures (e.g. aircrafts, transport or energy networks), high-rate sensors are deployed to capture multivariate data, generally unlabeled, in quasi continuous-time to detect quickly the occurrence of anomalies that may jeopardize the smooth operation of the system of interest. The statistical analysis of such massive data of functional nature raises many challenging methodological questions. The primary goal of this paper is to extend the popular Isolation Forest (IF) approach to Anomaly Detection, originally dedicated to finite dimensional observations, to functional data. The major difficulty lies in the wide variety of topological structures that may equip a space of functions and the great variety of patterns that may characterize abnormal curves. We address the issue of (randomly) splitting the functional space in a flexible manner in order to isolate progressively any trajectory from the others, a key ingredient to the efficiency of the algorithm. Beyond a detailed description of the algorithm, computational complexity and stability issues are investigated at length. From the scoring function measuring the degree of abnormality of an observation provided by the proposed variant of the IF algorithm, a Functional Statistical Depth function is defined and discussed as well as a multivariate functional extension. Numerical experiments provide strong empirical evidence of the accuracy of the extension proposed.
Cross-Modal Self-Attention Network for Referring Image Segmentation
Authors: Linwei Ye, Mrigank Rochan, Zhi Liu, Yang Wang
Date: 2019-04-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Segmentation, Referring Expression, Referring Expression Segmentation, Referring Video Object Segmentation, Semantic Segmentation

We consider the problem of referring image segmentation. Given an input image
and a natural language expression, the goal is to segment the object referred
by the language expression in the image. Existing works in this area treat the
language expression and the input image separately in their representations.
They do not sufficiently capture long-range correlations between these two
modalities. In this paper, we propose a cross-modal self-attention (CMSA)
module that effectively captures the long-range dependencies between linguistic
and visual features. Our model can adaptively focus on informative words in the
referring expression and important regions in the input image. In addition, we
propose a gated multi-level fusion module to selectively integrate
self-attentive cross-modal features corresponding to different levels in the
image. This module controls the information flow of features at different
levels. We validate the proposed approach on four evaluation datasets. Our
proposed approach consistently outperforms existing state-of-the-art methods.
$\mathcal{G}$-softmax: Improving Intra-class Compactness and Inter-class Separability of Features
Authors: Yan Luo, Yongkang Wong, Mohan Kankanhalli, Qi Zhao
Date: 2019-04-08 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Intra-class compactness and inter-class separability are crucial indicators to measure the effectiveness of a model to produce discriminative features, where intra-class compactness indicates how close the features with the same label are to each other and inter-class separability indicates how far away the features with different labels are. In this work, we investigate intra-class compactness and inter-class separability of features learned by convolutional networks and propose a Gaussian-based softmax ($\mathcal{G}$-softmax) function that can effectively improve intra-class compactness and inter-class separability. The proposed function is simple to implement and can easily replace the softmax function. We evaluate the proposed $\mathcal{G}$-softmax function on classification datasets (i.e., CIFAR-10, CIFAR-100, and Tiny ImageNet) and on multi-label classification datasets (i.e., MS COCO and NUS-WIDE). The experimental results show that the proposed $\mathcal{G}$-softmax function improves the state-of-the-art models across all evaluated datasets. In addition, analysis of the intra-class compactness and inter-class separability demonstrates the advantages of the proposed function over the softmax function, which is consistent with the performance improvement. More importantly, we observe that high intra-class compactness and inter-class separability are linearly correlated to average precision on MS COCO and NUS-WIDE. This implies that improvement of intra-class compactness and inter-class separability would lead to improvement of average precision.
SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking
Authors: Guangting Wang, Chong Luo, Zhiwei Xiong, Wen-Jun Zeng
Date: 2019-04-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: GPU, Object Tracking, Visual Object Tracking

The greatest challenge facing visual object tracking is the simultaneous
requirements on robustness and discrimination power. In this paper, we propose
a SiamFC-based tracker, named SPM-Tracker, to tackle this challenge. The basic
idea is to address the two requirements in two separate matching stages.
Robustness is strengthened in the coarse matching (CM) stage through
generalized training while discrimination power is enhanced in the fine
matching (FM) stage through a distance learning network. The two stages are
connected in series as the input proposals of the FM stage are generated by the
CM stage. They are also connected in parallel as the matching scores and box
location refinements are fused to generate the final results. This innovative
series-parallel structure takes advantage of both stages and results in
superior performance. The proposed SPM-Tracker, running at 120fps on GPU,
achieves an AUC of 0.687 on OTB-100 and an EAO of 0.434 on VOT-16, exceeding
other real-time trackers by a notable margin.
BoLTVOS: Box-Level Tracking for Video Object Segmentation
Authors: Paul Voigtlaender, Jonathon Luiten, Bastian Leibe
Date: 2019-04-09 00:00:00
Tasks: Object, One-shot visual object segmentation, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

We approach video object segmentation (VOS) by splitting the task into two sub-tasks: bounding box level tracking, followed by bounding box segmentation. Following this paradigm, we present BoLTVOS (Box-Level Tracking for VOS), which consists of an R-CNN detector conditioned on the first-frame bounding box to detect the object of interest, a temporal consistency rescoring algorithm, and a Box2Seg network that converts bounding boxes to segmentation masks. BoLTVOS performs VOS using only the firstframe bounding box without the mask. We evaluate our approach on DAVIS 2017 and YouTube-VOS, and show that it outperforms all methods that do not perform first-frame fine-tuning. We further present BoLTVOS-ft, which learns to segment the object in question using the first-frame mask while it is being tracked, without increasing the runtime. BoLTVOS-ft outperforms PReMVOS, the previously best performing VOS method on DAVIS 2016 and YouTube-VOS, while running up to 45 times faster. Our bounding box tracker also outperforms all previous short-term and longterm trackers on the bounding box level tracking datasets OTB 2015 and LTB35. A newer version of this work can be found at arXiv:1911.12836.
SoDeep: a Sorting Deep net to learn ranking loss surrogates
Authors: Martin Engilberge, Louis Chevallier, Patrick Pérez, Matthieu Cord
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: image-classification, Image Classification, Image Retrieval, Multi-Label Image Classification, Retrieval

Several tasks in machine learning are evaluated using non-differentiable
metrics such as mean average precision or Spearman correlation. However, their
non-differentiability prevents from using them as objective functions in a
learning framework. Surrogate and relaxation methods exist but tend to be
specific to a given metric.
  In the present work, we introduce a new method to learn approximations of
such non-differentiable objective functions. Our approach is based on a deep
architecture that approximates the sorting of arbitrary sets of scores. It is
trained virtually for free using synthetic data. This sorting deep (SoDeep) net
can then be combined in a plug-and-play manner with existing deep
architectures. We demonstrate the interest of our approach in three different
tasks that require ranking: Cross-modal text-image retrieval, multi-label image
classification and visual memorability ranking. Our approach yields very
competitive results on these three tasks, which validates the merit and the
flexibility of SoDeep as a proxy for sorting operation in ranking-based losses.
3D Local Features for Direct Pairwise Registration
Authors: Haowen Deng, Tolga Birdal, Slobodan Ilic
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: Pose Estimation

We present a novel, data driven approach for solving the problem of
registration of two point cloud scans. Our approach is direct in the sense that
a single pair of corresponding local patches already provides the necessary
transformation cue for the global registration. To achieve that, we first endow
the state of the art PPF-FoldNet auto-encoder (AE) with a pose-variant sibling,
where the discrepancy between the two leads to pose-specific descriptors. Based
upon this, we introduce RelativeNet, a relative pose estimation network to
assign correspondence-specific orientations to the keypoints, eliminating any
local reference frame computations. Finally, we devise a simple yet effective
hypothesize-and-verify algorithm to quickly use the predictions and align two
point sets. Our extensive quantitative and qualitative experiments suggests
that our approach outperforms the state of the art in challenging real datasets
of pairwise registration and that augmenting the keypoints with local pose
information leads to better generalization and a dramatic speed-up.
Neural Rerendering in the Wild
Authors: Moustafa Meshry, Dan B. Goldman, Sameh Khamis, Hugues Hoppe, Rohit Pandey, Noah Snavely, Ricardo Martin-Brualla
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Reconstruction

We explore total scene capture -- recording, modeling, and rerendering a
scene under varying appearance such as season and time of day. Starting from
internet photos of a tourist landmark, we apply traditional 3D reconstruction
to register the photos and approximate the scene as a point cloud. For each
photo, we render the scene points into a deep framebuffer, and train a neural
network to learn the mapping of these initial renderings to the actual photos.
This rerendering network also takes as input a latent appearance vector and a
semantic mask indicating the location of transient objects like pedestrians.
The model is evaluated on several datasets of publicly available images
spanning a broad range of illumination conditions. We create short videos
demonstrating realistic manipulation of the image viewpoint, appearance, and
semantic labeling. We also compare results with prior work on scene
reconstruction from internet photos.
End-to-end Projector Photometric Compensation
Authors: Bingyao Huang, Haibin Ling
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6

Projector photometric compensation aims to modify a projector input image
such that it can compensate for disturbance from the appearance of projection
surface. In this paper, for the first time, we formulate the compensation
problem as an end-to-end learning problem and propose a convolutional neural
network, named CompenNet, to implicitly learn the complex compensation
function. CompenNet consists of a UNet-like backbone network and an autoencoder
subnet. Such architecture encourages rich multi-level interactions between the
camera-captured projection surface image and the input image, and thus captures
both photometric and environment information of the projection surface. In
addition, the visual details and interaction information are carried to deeper
layers along the multi-level skip convolution layers. The architecture is of
particular importance for the projector compensation task, for which only a
small training dataset is allowed in practice. Another contribution we make is
a novel evaluation benchmark, which is independent of system setup and thus
quantitatively verifiable. Such benchmark is not previously available, to our
best knowledge, due to the fact that conventional evaluation requests the
hardware system to actually project the final results. Our key idea, motivated
from our end-to-end problem formulation, is to use a reasonable surrogate to
avoid such projection process so as to be setup-independent. Our method is
evaluated carefully on the benchmark, and the results show that our end-to-end
learning solution outperforms state-of-the-arts both qualitatively and
quantitatively by a significant margin.
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
Authors: Paritosh Parmar, Brendan Tran Morris
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Classification, Action Quality Assessment, Action Recognition, Avg, Fine-grained Action Recognition, Multi-Task Learning, Skills Assessment, Temporal Action Localization, Video Captioning

Can performance on the task of action quality assessment (AQA) be improved by exploiting a description of the action and its quality? Current AQA and skills assessment approaches propose to learn features that serve only one task - estimating the final score. In this paper, we propose to learn spatio-temporal features that explain three related tasks - fine-grained action recognition, commentary generation, and estimating the AQA score. A new multitask-AQA dataset, the largest to date, comprising of 1412 diving samples was collected to evaluate our approach (https://github.com/ParitoshParmar/MTL-AQA). We show that our MTL approach outperforms STL approach using two different kinds of architectures: C3D-AVG and MSCADC. The C3D-AVG-MTL approach achieves the new state-of-the-art performance with a rank correlation of 90.44%. Detailed experiments were performed to show that MTL offers better generalization than STL, and representations from action recognition models are not sufficient for the AQA task and instead should be learned.
Towards Universal Object Detection by Domain Attention
Authors: Xudong Wang, Zhaowei Cai, Dashan Gao, Nuno Vasconcelos
Date: 2019-04-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object, object-detection, Object Detection

Despite increasing efforts on universal representations for visual recognition, few have addressed object detection. In this paper, we develop an effective and efficient universal object detection system that is capable of working on various image domains, from human faces and traffic signs to medical CT images. Unlike multi-domain models, this universal model does not require prior knowledge of the domain of interest. This is achieved by the introduction of a new family of adaptation layers, based on the principles of squeeze and excitation, and a new domain-attention mechanism. In the proposed universal detector, all parameters and computations are shared across domains, and a single network processes all domains all the time. Experiments, on a newly established universal object detection benchmark of 11 diverse datasets, show that the proposed detector outperforms a bank of individual detectors, a multi-domain detector, and a baseline universal detector, with a 1.3x parameter increase over a single-domain baseline detector. The code and benchmark will be released at http://www.svcl.ucsd.edu/projects/universal-detection/.