Incorporating Metadata Into the Active Learning Cycle for 2D Object Detection

Incorporating Metadata Into the Active Learning Cycle for 2D Object Detection

In the past years, Deep Convolutional Neural Networks have proven to be very useful for 2D Object Detection in many applications. These types of networks require large amounts of labeled data, which can be increasingly costly for companies deploying these detectors in practice if the data quality is...

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Bibliographic Details
Main Author: Stadler, Karsten
Format: Others
Language:English
Published: KTH, Skolan för elektroteknik och datavetenskap (EECS) 2021
Subjects:
Active learning
Deep Learning
Object detection
Metadata
Nuscenes Nuimages
Gaussian mixture model
Rejection sampling
Monte-Carlo methods
Aktiv Inlärning
Djupinlärning
Objektdetektering
metadata
Gaussisk blandingsmodell
Monte-Carlo metoder
Computer and Information Sciences
Data- och informationsvetenskap
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-305578

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http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-305578

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