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Deep Multi-view Depth Estimation with Predicted Uncertainty
TongKe, TienDo, KhiemVuong....
Published date-11/19/2020
DepthEstimation, OpticalFlowEstimation
In this paper, we address the problem of estimating dense depth from a sequence of images using deep neural networks. Specifically, we employ a dense-optical-flow network to compute correspondences and …
Exploring Constraint Handling Techniques in Real-world Problems on MOEA/D with Limited Budget of Evaluations
FelipeVaz, YuriLavinas, ClausAranha....
Published date-11/19/2020
Finding good solutions for Multi-objective Optimization (MOPs) Problems is considered a hard problem, especially when considering MOPs with constraints. Thus, most of the works in the context of MOPs do …
Dense Label Encoding for Boundary Discontinuity Free Rotation Detection
XueYang, LipingHou, YueZhou....
Published date-11/19/2020
SceneText
Rotation detection serves as a fundamental building block in many visual applications involving aerial image, scene text, and face etc. Differing from the dominant regression-based approaches for orientation estimation, this …
Exploring Text Specific and Blackbox Fairness Algorithms in Multimodal Clinical NLP
JohnChen, IanBerlot-Atwell, SafwanHossain....
Published date-11/19/2020
fairness, WordEmbeddings
Clinical machine learning is increasingly multimodal, collected in both structured tabular formats and unstructured forms such as freetext. We propose a novel task of exploring fairness on a multimodal clinical …
Improving Bayesian Network Structure Learning in the Presence of Measurement Error
YangLiu, AnthonyC.Constantinou, ZhigaoGuo....
Published date-11/19/2020
Structure learning algorithms that learn the graph of a Bayesian network from observational data often do so by assuming the data correctly reflect the true distribution of the variables. However, …
Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning
ZhendaXie, YutongLin, ZhengZhang....
Published date-11/19/2020
ContrastiveLearning, ObjectDetection, RepresentationLearning, SemanticSegmentation
Contrastive learning methods for unsupervised visual representation learning have reached remarkable levels of transfer performance. We argue that the power of contrastive learning has yet to be fully unleashed, as …