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Joint Analysis and Prediction of Human Actions and Paths in Video
JunweiLiang....
Published date-11/20/2020
ActionDetection, AutonomousDriving, TrajectoryPrediction
With the advancement in computer vision deep learning, systems now are able to analyze an unprecedented amount of rich visual information from videos to enable applications such as autonomous driving, …
Crowdsourcing Airway Annotations in Chest Computed Tomography Images
VeronikaCheplygina, AdriaPerez-Rovira, WieyingKuo....
Published date-11/20/2020
ComputedTomography(CT)
Measuring airways in chest computed tomography (CT) scans is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually. Machine learning algorithms offer an alternative, but …
AirConcierge: Generating Task-Oriented Dialogue via Efficient Large-Scale Knowledge Retrieval
Chieh-YangChen, Pei-HsinWang, Shih-ChiehChang....
Published date-11/20/2020
Task-OrientedDialogueSystems, Text-To-Sql
Despite recent success in neural task-oriented dialogue systems, developing such a real-world system involves accessing large-scale knowledge bases (KBs), which cannot be simply encoded by neural approaches, such as memory …
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 …
Node Similarity Preserving Graph Convolutional Networks
WeiJin, TylerDerr, YiqiWang....
Published date-11/19/2020
GraphRepresentationLearning, RepresentationLearning, Self-SupervisedLearning
Graph Neural Networks (GNNs) have achieved tremendous success in various real-world applications due to their strong ability in graph representation learning. GNNs explore the graph structure and node features by …