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Demo Data from VLDB 2026 / reviewers in the wild / expert

Debleena Sengupta

dblp:224/1368 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Segmentation and scene understanding · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Segmentation and scene understanding › image segmentation
active contour model		 0.412020
End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery · ECCV (12) 2020
Computer vision › Segmentation and scene understanding
image segmentation		 0.412020
End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery · ECCV (12) 2020
Computer vision › Segmentation and scene understanding › semantic segmentation › remote sensing image segmentation
aerial image segmentation		 0.112020
End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery · ECCV (12) 2020

Methods — techniques the papers use, named apart from their topics

deep learning · 0.4
YearPublicationVenuePosition
2020 End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery
Ali Hatamizadeh, Debleena Sengupta, Demetri Terzopoulos
 ECCV (12)2