pwc-archive/papers-with-abstracts huggingface dataset:
Algae Detection Using Computer Vision and Deep Learning
Authors: Arabinda Samantaray, Baijian Yang, J. Eric Dietz, Byung-Cheol Min
Date: 2018-11-27 00:00:00
Tasks: Deep Learning

A disconcerting ramification of water pollution caused by burgeoning
populations, rapid industrialization and modernization of agriculture, has been
the exponential increase in the incidence of algal growth across the globe.
Harmful algal blooms (HABs) have devastated fisheries, contaminated drinking
water and killed livestock, resulting in economic losses to the tune of
millions of dollars. Therefore, it is important to constantly monitor water
bodies and identify any algae build-up so that prompt action against its
accumulation can be taken and the harmful consequences can be avoided. In this
paper, we propose a computer vision system based on deep learning for algae
monitoring. The proposed system is fast, accurate and cheap, and it can be
installed on any robotic platforms such as USVs and UAVs for autonomous algae
monitoring. The experimental results demonstrate that the proposed system can
detect algae in distinct environments regardless of the underlying hardware
with high accuracy and in real time.
CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation
Authors: Junsong Fan, Zhao-Xiang Zhang, Tieniu Tan, Chunfeng Song, Jun Xiao
Date: 2018-11-27 00:00:00
Tasks: 2D Object Detection, Multispectral Object Detection, Segmentation, Semantic Segmentation, Weakly supervised segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Weakly supervised semantic segmentation with only image-level labels saves large human effort to annotate pixel-level labels. Cutting-edge approaches rely on various innovative constraints and heuristic rules to generate the masks for every single image. Although great progress has been achieved by these methods, they treat each image independently and do not take account of the relationships across different images. In this paper, however, we argue that the cross-image relationship is vital for weakly supervised segmentation. Because it connects related regions across images, where supplementary representations can be propagated to obtain more consistent and integral regions. To leverage this information, we propose an end-to-end cross-image affinity module, which exploits pixel-level cross-image relationships with only image-level labels. By means of this, our approach achieves 64.3% and 65.3% mIoU on Pascal VOC 2012 validation and test set respectively, which is a new state-of-the-art result by only using image-level labels for weakly supervised semantic segmentation, demonstrating the superiority of our approach.
Robust Artificial Intelligence and Robust Human Organizations
Authors: Thomas G. Dietterich
Date: 2018-11-27 00:00:00

Every AI system is deployed by a human organization. In high risk
applications, the combined human plus AI system must function as a
high-reliability organization in order to avoid catastrophic errors. This short
note reviews the properties of high-reliability organizations and draws
implications for the development of AI technology and the safe application of
that technology.
Part-level Car Parsing and Reconstruction from Single Street View
Authors: Qichuan Geng, Hong Zhang, Xinyu Huang, Sen Wang, Feixiang Lu, Xinjing Cheng, Zhong Zhou, Ruigang Yang
Date: 2018-11-27 00:00:00
Tasks: Car Pose Estimation, Domain Adaptation, Pose Estimation, Vehicle Re-Identification

Part information has been shown to be resistant to occlusions and viewpoint changes, which is beneficial for various vision-related tasks. However, we found very limited work in car pose estimation and reconstruction from street views leveraging the part information. There are two major contributions in this paper. Firstly, we make the first attempt to build a framework to simultaneously estimate shape, translation, orientation, and semantic parts of cars in 3D space from a single street view. As it is labor-intensive to annotate semantic parts on real street views, we propose a specific approach to implicitly transfer part features from synthesized images to real street views. For pose and shape estimation, we propose a novel network structure that utilizes both part features and 3D losses. Secondly, we are the first to construct a high-quality dataset that contains 348 different car models with physical dimensions and part-level annotations based on global and local deformations. Given these models, we further generate 60K synthesized images with randomization of orientation, illumination, occlusion, and texture. Our results demonstrate that our part segmentation performance is significantly improved after applying our implicit transfer approach. Our network for pose and shape estimation achieves the state-of-the-art performance on the ApolloCar3D dataset and outperforms 3D-RCNN and DeepMANTA by 12.57 and 8.91 percentage points in terms of mean A3DP-Abs.
From Recognition to Cognition: Visual Commonsense Reasoning
Authors: Rowan Zellers, Yonatan Bisk, Ali Farhadi, Yejin Choi
Date: 2018-11-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Multiple-choice, Multiple Choice Question Answering (MCQA), Visual Commonsense Reasoning

Visual understanding goes well beyond object recognition. With one glance at
an image, we can effortlessly imagine the world beyond the pixels: for
instance, we can infer people's actions, goals, and mental states. While this
task is easy for humans, it is tremendously difficult for today's vision
systems, requiring higher-order cognition and commonsense reasoning about the
world. We formalize this task as Visual Commonsense Reasoning. Given a
challenging question about an image, a machine must answer correctly and then
provide a rationale justifying its answer.
  Next, we introduce a new dataset, VCR, consisting of 290k multiple choice QA
problems derived from 110k movie scenes. The key recipe for generating
non-trivial and high-quality problems at scale is Adversarial Matching, a new
approach to transform rich annotations into multiple choice questions with
minimal bias. Experimental results show that while humans find VCR easy (over
90% accuracy), state-of-the-art vision models struggle (~45%).
  To move towards cognition-level understanding, we present a new reasoning
engine, Recognition to Cognition Networks (R2C), that models the necessary
layered inferences for grounding, contextualization, and reasoning. R2C helps
narrow the gap between humans and machines (~65%); still, the challenge is far
from solved, and we provide analysis that suggests avenues for future work.
Optimal Learning for Dynamic Coding in Deadline-Constrained Multi-Channel Networks
Authors: Semih Cayci, Atilla Eryilmaz
Date: 2018-11-27 00:00:00
Tasks: Thompson Sampling

We study the problem of serving randomly arriving and delay-sensitive traffic
over a multi-channel communication system with time-varying channel states and
unknown statistics. This problem deviates from the classical
exploration-exploitation setting in that the design and analysis must
accommodate the dynamics of packet availability and urgency as well as the cost
of each channel use at the time of decision. To that end, we have developed and
investigated an index-based policy UCB-Deadline, which performs dynamic channel
allocation decisions that incorporate these traffic requirements and costs.
Under symmetric channel conditions, we have proved that the UCB-Deadline policy
can achieve bounded regret in the likely case where the cost of using a channel
is not too high to prevent all transmissions, and logarithmic regret otherwise.
In this case, we show that UCB-Deadline is order-optimal. We also perform
numerical investigations to validate the theoretical findings, and also compare
the performance of the UCB-Deadline to another learning algorithm that we
propose based on Thompson Sampling.
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks
Authors: Jinghui Chen, Dongruo Zhou, Jin-Feng Yi, Quanquan Gu
Date: 2018-11-27 00:00:00
Proceeding: ICLR 2019 5
Tasks: Adversarial Attack

Depending on how much information an adversary can access to, adversarial attacks can be classified as white-box attack and black-box attack. For white-box attack, optimization-based attack algorithms such as projected gradient descent (PGD) can achieve relatively high attack success rates within moderate iterates. However, they tend to generate adversarial examples near or upon the boundary of the perturbation set, resulting in large distortion. Furthermore, their corresponding black-box attack algorithms also suffer from high query complexities, thereby limiting their practical usefulness. In this paper, we focus on the problem of developing efficient and effective optimization-based adversarial attack algorithms. In particular, we propose a novel adversarial attack framework for both white-box and black-box settings based on a variant of Frank-Wolfe algorithm. We show in theory that the proposed attack algorithms are efficient with an $O(1/\sqrt{T})$ convergence rate. The empirical results of attacking the ImageNet and MNIST datasets also verify the efficiency and effectiveness of the proposed algorithms. More specifically, our proposed algorithms attain the best attack performances in both white-box and black-box attacks among all baselines, and are more time and query efficient than the state-of-the-art.
Noise-tolerant Audio-visual Online Person Verification using an Attention-based Neural Network Fusion
Authors: Suwon Shon, Tae-Hyun Oh, James Glass
Date: 2018-11-27 00:00:00

In this paper, we present a multi-modal online person verification system
using both speech and visual signals. Inspired by neuroscientific findings on
the association of voice and face, we propose an attention-based end-to-end
neural network that learns multi-sensory associations for the task of person
verification. The attention mechanism in our proposed network learns to
conditionally select a salient modality between speech and facial
representations that provides a balance between complementary inputs. By virtue
of this capability, the network is robust to missing or corrupted data from
either modality. In the VoxCeleb2 dataset, we show that our method performs
favorably against competing multi-modal methods. Even for extreme cases of
large corruption or an entirely missing modality, our method demonstrates
robustness over other unimodal methods.
Uncertainty aware audiovisual activity recognition using deep Bayesian variational inference
Authors: Mahesh Subedar, Ranganath Krishnan, Paulo Lopez Meyer, Omesh Tickoo, Jonathan Huang
Date: 2018-11-27 00:00:00
Tasks: Activity Recognition, Bayesian Inference, Multimodal Activity Recognition, Variational Inference

Deep neural networks (DNNs) provide state-of-the-art results for a multitude of applications, but the approaches using DNNs for multimodal audiovisual applications do not consider predictive uncertainty associated with individual modalities. Bayesian deep learning methods provide principled confidence and quantify predictive uncertainty. Our contribution in this work is to propose an uncertainty aware multimodal Bayesian fusion framework for activity recognition. We demonstrate a novel approach that combines deterministic and variational layers to scale Bayesian DNNs to deeper architectures. Our experiments using in- and out-of-distribution samples selected from a subset of Moments-in-Time (MiT) dataset show a more reliable confidence measure as compared to the non-Bayesian baseline and the Monte Carlo dropout (MC dropout) approximate Bayesian inference. We also demonstrate the uncertainty estimates obtained from the proposed framework can identify out-of-distribution data on the UCF101 and MiT datasets. In the multimodal setting, the proposed framework improved precision-recall AUC by 10.2% on the subset of MiT dataset as compared to non-Bayesian baseline.
Perceptual Conditional Generative Adversarial Networks for End-to-End Image Colourization
Authors: Shirsendu Sukanta Halder, Kanjar De, Partha Pratim Roy
Date: 2018-11-27 00:00:00
Tasks: Decoder

Colours are everywhere. They embody a significant part of human visual
perception. In this paper, we explore the paradigm of hallucinating colours
from a given gray-scale image. The problem of colourization has been dealt in
previous literature but mostly in a supervised manner involving
user-interference. With the emergence of Deep Learning methods numerous tasks
related to computer vision and pattern recognition have been automatized and
carried in an end-to-end fashion due to the availability of large data-sets and
high-power computing systems. We investigate and build upon the recent success
of Conditional Generative Adversarial Networks (cGANs) for Image-to-Image
translations. In addition to using the training scheme in the basic cGAN, we
propose an encoder-decoder generator network which utilizes the class-specific
cross-entropy loss as well as the perceptual loss in addition to the original
objective function of cGAN. We train our model on a large-scale dataset and
present illustrative qualitative and quantitative analysis of our results. Our
results vividly display the versatility and proficiency of our methods through
life-like colourization outcomes.
Probabilistic Object Detection: Definition and Evaluation
Authors: David Hall, Feras Dayoub, John Skinner, Haoyang Zhang, Dimity Miller, Peter Corke, Gustavo Carneiro, Anelia Angelova, Niko Sünderhauf
Date: 2018-11-27 00:00:00
Tasks: Object, object-detection, Object Detection

We introduce Probabilistic Object Detection, the task of detecting objects in images and accurately quantifying the spatial and semantic uncertainties of the detections. Given the lack of methods capable of assessing such probabilistic object detections, we present the new Probability-based Detection Quality measure (PDQ).Unlike AP-based measures, PDQ has no arbitrary thresholds and rewards spatial and label quality, and foreground/background separation quality while explicitly penalising false positive and false negative detections. We contrast PDQ with existing mAP and moLRP measures by evaluating state-of-the-art detectors and a Bayesian object detector based on Monte Carlo Dropout. Our experiments indicate that conventional object detectors tend to be spatially overconfident and thus perform poorly on the task of probabilistic object detection. Our paper aims to encourage the development of new object detection approaches that provide detections with accurately estimated spatial and label uncertainties and are of critical importance for deployment on robots and embodied AI systems in the real world.
What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems
Authors: Owen Lahav, Nicholas Mastronarde, Mihaela van der Schaar
Date: 2018-11-27 00:00:00
Tasks: BIG-bench Machine Learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent efforts in Machine Learning (ML) interpretability have focused on creating methods for explaining black-box ML models. However, these methods rely on the assumption that simple approximations, such as linear models or decision-trees, are inherently human-interpretable, which has not been empirically tested. Additionally, past efforts have focused exclusively on comprehension, neglecting to explore the trust component necessary to convince non-technical experts, such as clinicians, to utilize ML models in practice. In this paper, we posit that reinforcement learning (RL) can be used to learn what is interpretable to different users and, consequently, build their trust in ML models. To validate this idea, we first train a neural network to provide risk assessments for heart failure patients. We then design a RL-based clinical decision-support system (DSS) around the neural network model, which can learn from its interactions with users. We conduct an experiment involving a diverse set of clinicians from multiple institutions in three different countries. Our results demonstrate that ML experts cannot accurately predict which system outputs will maximize clinicians' confidence in the underlying neural network model, and suggest additional findings that have broad implications to the future of research into ML interpretability and the use of ML in medicine.
Sequentially Aggregated Convolutional Networks
Authors: Yiwen Huang, Rihui Wu, Pinglai Ou, Ziyong Feng
Date: 2018-11-27 00:00:00
Tasks: General Classification, image-classification, Image Classification

Modern deep networks generally implement a certain form of shortcut connections to alleviate optimization difficulties. However, we observe that such network topology alters the nature of deep networks. In many ways, these networks behave similarly to aggregated wide networks. We thus exploit the aggregation nature of shortcut connections at a finer architectural level and place them within wide convolutional layers. We end up with a sequentially aggregated convolutional (SeqConv) layer that combines the benefits of both wide and deep representations by aggregating features of various depths in sequence. The proposed SeqConv serves as a drop-in replacement of regular wide convolutional layers and thus could be handily integrated into any backbone network. We apply SeqConv to widely adopted backbones including ResNet and ResNeXt, and conduct experiments for image classification on public benchmark datasets. Our ResNet based network with a model size of ResNet-50 easily surpasses the performance of the 2.35$\times$ larger ResNet-152, while our ResNeXt based model sets a new state-of-the-art accuracy on ImageNet classification for networks with similar model complexity. The code and pre-trained models of our work are publicly available at https://github.com/GroupOfAlchemists/SeqConv.
Node Embedding with Adaptive Similarities for Scalable Learning over Graphs
Authors: Dimitris Berberidis, Georgios B. Giannakis
Date: 2018-11-27 00:00:00
Tasks: Clustering, Community Detection, Descriptive, General Classification, Link Prediction, Node Classification

Node embedding is the task of extracting informative and descriptive features over the nodes of a graph. The importance of node embeddings for graph analytics, as well as learning tasks such as node classification, link prediction and community detection, has led to increased interest on the problem leading to a number of recent advances. Much like PCA in the feature domain, node embedding is an inherently \emph{unsupervised} task; in lack of metadata used for validation, practical methods may require standardization and limiting the use of tunable hyperparameters. Finally, node embedding methods are faced with maintaining scalability in the face of large-scale real-world graphs of ever-increasing sizes. In the present work, we propose an adaptive node embedding framework that adjusts the embedding process to a given underlying graph, in a fully unsupervised manner. To achieve this, we adopt the notion of a tunable node similarity matrix that assigns weights on paths of different length. The design of the multilength similarities ensures that the resulting embeddings also inherit interpretable spectral properties. The proposed model is carefully studied, interpreted, and numerically evaluated using stochastic block models. Moreover, an algorithmic scheme is proposed for training the model parameters effieciently and in an unsupervised manner. We perform extensive node classification, link prediction, and clustering experiments on many real world graphs from various domains, and compare with state-of-the-art scalable and unsupervised node embedding alternatives. The proposed method enjoys superior performance in many cases, while also yielding interpretable information on the underlying structure of the graph.
Stochastic Gradient Push for Distributed Deep Learning
Authors: Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Michael Rabbat
Date: 2018-11-27 00:00:00
Proceeding: ICLR 2019 5
Tasks: Deep Learning, General Classification, image-classification, Image Classification, Machine Translation, Translation

Distributed data-parallel algorithms aim to accelerate the training of deep neural networks by parallelizing the computation of large mini-batch gradient updates across multiple nodes. Approaches that synchronize nodes using exact distributed averaging (e.g., via AllReduce) are sensitive to stragglers and communication delays. The PushSum gossip algorithm is robust to these issues, but only performs approximate distributed averaging. This paper studies Stochastic Gradient Push (SGP), which combines PushSum with stochastic gradient updates. We prove that SGP converges to a stationary point of smooth, non-convex objectives at the same sub-linear rate as SGD, and that all nodes achieve consensus. We empirically validate the performance of SGP on image classification (ResNet-50, ImageNet) and machine translation (Transformer, WMT'16 En-De) workloads. Our code will be made publicly available.
Accurate, Data-Efficient Learning from Noisy, Choice-Based Labels for Inherent Risk Scoring
Authors: W. Ronny Huang, Miguel A. Perez
Date: 2018-11-27 00:00:00
Tasks: Experimental Design

Inherent risk scoring is an important function in anti-money laundering, used
for determining the riskiness of an individual during onboarding
$\textit{before}$ fraudulent transactions occur. It is, however, often fraught
with two challenges: (1) inconsistent notions of what constitutes as high or
low risk by experts and (2) the lack of labeled data. This paper explores a new
paradigm of data labeling and data collection to tackle these issues. The data
labeling is choice-based; the expert does not provide an absolute risk score
but merely chooses the most/least risky example out of a small choice set,
which reduces inconsistency because experts make only relative judgments of
risk. The data collection is synthetic; examples are crafted using optimal
experimental design methods, obviating the need for real data which is often
difficult to obtain due to regulatory concerns. We present the methodology of
an end-to-end inherent risk scoring algorithm that we built for a large
financial institution. The system was trained on a small set of synthetic data
(188 examples, 24 features) whose labels are obtained via the choice-based
paradigm using an efficient number of expert labelers. The system achieves 89%
accuracy on a test set of 52 examples, with an area under the ROC curve of 93%.
High-dimensional Index Volatility Models via Stein's Identity
Authors: Sen Na, Mladen Kolar
Date: 2018-11-27 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We study the estimation of the parametric components of single and multiple index volatility models. Using the first- and second-order Stein's identities, we develop methods that are applicable for the estimation of the variance index in the high-dimensional setting requiring finite moment condition, which allows for heavy-tailed data. Our approach complements the existing literature in the low-dimensional setting, while relaxing the conditions on estimation, and provides a novel approach in the high-dimensional setting. We prove that the statistical rate of convergence of our variance index estimators consists of a parametric rate and a nonparametric rate, where the latter appears from the estimation of the mean link function. However, under standard assumptions, the parametric rate dominates the rate of convergence and our results match the minimax optimal rate for the mean index estimation. Simulation results illustrate finite sample properties of our methodology and back our theoretical conclusions.