Learning Deep Descriptors with Scale-Aware Triplet Networks
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Author / Producer
Date
2018
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
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Data
Rights / License
Abstract
Research on learning suitable feature descriptors for Computer Vision has recently shifted to deep learning where the biggest challenge lies with the formulation of appropriate loss functions, especially since the descriptors to be learned are not known at training time. While approaches such as Siamese and triplet losses have been applied with success, it is still not well understood what makes a good loss function. In this spirit, this work demonstrates that many commonly used losses suffer from a range of problems. Based on this analysis, we introduce mixed-context losses and scale-aware sampling, two methods that when combined enable networks to learn consistently scaled descriptors for the first time.
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Publication status
published
External links
Editor
Book title
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Journal / series
Volume
Pages / Article No.
2762 - 2770
Publisher
IEEE
Event
31st IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Feature Encoding; Triplet Network
Organisational unit
09559 - Chli, Margarita (ehemalig) / Chli, Margarita (former)
Notes
Funding
644128 - Collaborative Aerial Robotic Workers (SBFI)