---
_id: '11736'
abstract:
- lang: eng
text: "This paper introduces a methodology for inverse-modeling of yarn-level mechanics
of cloth, based on the mechanical response of fabrics in the real world. We compiled
a database from physical tests of several different knitted fabrics used in the
textile industry. These data span different types of complex knit patterns, yarn
compositions, and fabric finishes, and the results demonstrate diverse physical
properties like stiffness, nonlinearity, and anisotropy.\r\n\r\nWe then develop
a system for approximating these mechanical responses with yarn-level cloth simulation.
To do so, we introduce an efficient pipeline for converting between fabric-level
data and yarn-level simulation, including a novel swatch-level approximation for
speeding up computation, and some small-but-necessary extensions to yarn-level
models used in computer graphics. The dataset used for this paper can be found
at http://mslab.es/projects/YarnLevelFabrics."
acknowledged_ssus:
- _id: ScienComp
acknowledgement: We wish to thank the anonymous reviewers for their helpful comments.
To develop this project, we were helped by many people both at Under Armour (Clay
Dean, Randall Harward, Kyle Blakely, Craig Simile, Michael Seiz, Brooke Malone,
Brittainy McFarland, Emilie Phan, Lindsey Kern, Courtney Oswald, Haley Barkley,
Bob Chin, Adam Bayer, Connie Kwok, Marielle Newman, Nick Pence, Allison Hicks, Allison
White, Candace Rubenstein, Jeremy Stangland, Fred Fagergren, Michael Mazzoleni,
Nathaniel Berry, Manuel Frank) and SEDDI (Gabriel Cirio, Alejandro Rodríguez, Sofía
Dominguez, Alicia Nicas, Elena Garcés, Daniel Rodríguez, David Pascual, Manuel Godoy,
Sergio Suja, Sergio Ruiz, Roberto Condori, Alberto Martín, Graham Sullivan). We
also thank the members of the Visual Computing Group at IST Austria and the Multimodal
Simulation Lab at URJC for their feedback. This research was supported by the Scientific
Service Units (SSU) of IST Austria through resources provided by Scientific Computing,
and it was funded in part by the European Research Council (ERC Consolidator Grant
772738 TouchDesign).
article_number: '65'
article_processing_charge: No
article_type: original
author:
- first_name: Georg
full_name: Sperl, Georg
id: 4DD40360-F248-11E8-B48F-1D18A9856A87
last_name: Sperl
- first_name: Rosa M.
full_name: Sánchez-Banderas, Rosa M.
last_name: Sánchez-Banderas
- first_name: Manwen
full_name: Li, Manwen
last_name: Li
- first_name: Christopher J
full_name: Wojtan, Christopher J
id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87
last_name: Wojtan
orcid: 0000-0001-6646-5546
- first_name: Miguel A.
full_name: Otaduy, Miguel A.
last_name: Otaduy
citation:
ama: Sperl G, Sánchez-Banderas RM, Li M, Wojtan C, Otaduy MA. Estimation of yarn-level
simulation models for production fabrics. ACM Transactions on Graphics.
2022;41(4). doi:10.1145/3528223.3530167
apa: Sperl, G., Sánchez-Banderas, R. M., Li, M., Wojtan, C., & Otaduy, M. A.
(2022). Estimation of yarn-level simulation models for production fabrics. ACM
Transactions on Graphics. Association for Computing Machinery. https://doi.org/10.1145/3528223.3530167
chicago: Sperl, Georg, Rosa M. Sánchez-Banderas, Manwen Li, Chris Wojtan, and Miguel
A. Otaduy. “Estimation of Yarn-Level Simulation Models for Production Fabrics.”
ACM Transactions on Graphics. Association for Computing Machinery, 2022.
https://doi.org/10.1145/3528223.3530167.
ieee: G. Sperl, R. M. Sánchez-Banderas, M. Li, C. Wojtan, and M. A. Otaduy, “Estimation
of yarn-level simulation models for production fabrics,” ACM Transactions on
Graphics, vol. 41, no. 4. Association for Computing Machinery, 2022.
ista: Sperl G, Sánchez-Banderas RM, Li M, Wojtan C, Otaduy MA. 2022. Estimation
of yarn-level simulation models for production fabrics. ACM Transactions on Graphics.
41(4), 65.
mla: Sperl, Georg, et al. “Estimation of Yarn-Level Simulation Models for Production
Fabrics.” ACM Transactions on Graphics, vol. 41, no. 4, 65, Association
for Computing Machinery, 2022, doi:10.1145/3528223.3530167.
short: G. Sperl, R.M. Sánchez-Banderas, M. Li, C. Wojtan, M.A. Otaduy, ACM Transactions
on Graphics 41 (2022).
date_created: 2022-08-07T22:01:58Z
date_published: 2022-07-22T00:00:00Z
date_updated: 2023-08-03T12:38:30Z
day: '22'
department:
- _id: ChWo
doi: 10.1145/3528223.3530167
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issue: '4'
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month: '07'
oa: 1
oa_version: Published Version
publication: ACM Transactions on Graphics
publication_identifier:
eissn:
- 1557-7368
issn:
- 0730-0301
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
related_material:
link:
- description: News on the ISTA website
relation: press_release
url: https://ista.ac.at/en/news/digital-yarn-real-socks/
record:
- id: '12358'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Estimation of yarn-level simulation models for production fabrics
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 41
year: '2022'
...
---
_id: '12358'
abstract:
- lang: eng
text: "The complex yarn structure of knitted and woven fabrics gives rise to both
a mechanical and\r\nvisual complexity. The small-scale interactions of yarns colliding
with and pulling on each\r\nother result in drastically different large-scale
stretching and bending behavior, introducing\r\nanisotropy, curling, and more.
While simulating cloth as individual yarns can reproduce this\r\ncomplexity and
match the quality of real fabric, it may be too computationally expensive for\r\nlarge
fabrics. On the other hand, continuum-based approaches do not need to discretize
the\r\ncloth at a stitch-level, but it is non-trivial to find a material model
that would replicate the\r\nlarge-scale behavior of yarn fabrics, and they discard
the intricate visual detail. In this thesis,\r\nwe discuss three methods to try
and bridge the gap between small-scale and large-scale yarn\r\nmechanics using
numerical homogenization: fitting a continuum model to periodic yarn simulations,
adding mechanics-aware yarn detail onto thin-shell simulations, and quantitatively\r\nfitting
yarn parameters to physical measurements of real fabric.\r\nTo start, we present
a method for animating yarn-level cloth effects using a thin-shell solver.\r\nWe
first use a large number of periodic yarn-level simulations to build a model of
the potential\r\nenergy density of the cloth, and then use it to compute forces
in a thin-shell simulator. The\r\nresulting simulations faithfully reproduce expected
effects like the stiffening of woven fabrics\r\nand the highly deformable nature
and anisotropy of knitted fabrics at a fraction of the cost of\r\nfull yarn-level
simulation.\r\nWhile our thin-shell simulations are able to capture large-scale
yarn mechanics, they lack\r\nthe rich visual detail of yarn-level simulations.
Therefore, we propose a method to animate\r\nyarn-level cloth geometry on top
of an underlying deforming mesh in a mechanics-aware\r\nfashion in real time.
Using triangle strains to interpolate precomputed yarn geometry, we are\r\nable
to reproduce effects such as knit loops tightening under stretching at negligible
cost.\r\nFinally, we introduce a methodology for inverse-modeling of yarn-level
mechanics of cloth,\r\nbased on the mechanical response of fabrics in the real
world. We compile a database from\r\nphysical tests of several knitted fabrics
used in the textile industry spanning diverse physical\r\nproperties like stiffness,
nonlinearity, and anisotropy. We then develop a system for approximating these
mechanical responses with yarn-level cloth simulation, using homogenized\r\nshell
models to speed up computation and adding some small-but-necessary extensions
to\r\nyarn-level models used in computer graphics.\r\n"
acknowledged_ssus:
- _id: SSU
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Georg
full_name: Sperl, Georg
id: 4DD40360-F248-11E8-B48F-1D18A9856A87
last_name: Sperl
citation:
ama: 'Sperl G. Homogenizing yarn simulations: Large-scale mechanics, small-scale
detail, and quantitative fitting. 2022. doi:10.15479/at:ista:12103'
apa: 'Sperl, G. (2022). Homogenizing yarn simulations: Large-scale mechanics,
small-scale detail, and quantitative fitting. Institute of Science and Technology
Austria. https://doi.org/10.15479/at:ista:12103'
chicago: 'Sperl, Georg. “Homogenizing Yarn Simulations: Large-Scale Mechanics, Small-Scale
Detail, and Quantitative Fitting.” Institute of Science and Technology Austria,
2022. https://doi.org/10.15479/at:ista:12103.'
ieee: 'G. Sperl, “Homogenizing yarn simulations: Large-scale mechanics, small-scale
detail, and quantitative fitting,” Institute of Science and Technology Austria,
2022.'
ista: 'Sperl G. 2022. Homogenizing yarn simulations: Large-scale mechanics, small-scale
detail, and quantitative fitting. Institute of Science and Technology Austria.'
mla: 'Sperl, Georg. Homogenizing Yarn Simulations: Large-Scale Mechanics, Small-Scale
Detail, and Quantitative Fitting. Institute of Science and Technology Austria,
2022, doi:10.15479/at:ista:12103.'
short: 'G. Sperl, Homogenizing Yarn Simulations: Large-Scale Mechanics, Small-Scale
Detail, and Quantitative Fitting, Institute of Science and Technology Austria,
2022.'
date_created: 2023-01-24T10:49:46Z
date_published: 2022-09-22T00:00:00Z
date_updated: 2024-02-28T12:57:46Z
day: '22'
ddc:
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- '620'
degree_awarded: PhD
department:
- _id: GradSch
- _id: ChWo
doi: 10.15479/at:ista:12103
ec_funded: 1
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page: '138'
project:
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call_identifier: H2020
grant_number: '638176'
name: Efficient Simulation of Natural Phenomena at Extremely Large Scales
publication_identifier:
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issn:
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publisher: Institute of Science and Technology Austria
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full_name: Wojtan, Christopher J
id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87
last_name: Wojtan
orcid: 0000-0001-6646-5546
title: 'Homogenizing yarn simulations: Large-scale mechanics, small-scale detail,
and quantitative fitting'
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
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...
---
_id: '9327'
abstract:
- lang: eng
text: "This archive contains the missing sweater mesh animations and displacement
models for the code of \"Mechanics-Aware Deformation of Yarn Pattern Geometry\"\r\n\r\nCode
Repository: https://git.ist.ac.at/gsperl/MADYPG"
author:
- first_name: Georg
full_name: Sperl, Georg
id: 4DD40360-F248-11E8-B48F-1D18A9856A87
last_name: Sperl
- first_name: Rahul
full_name: Narain, Rahul
last_name: Narain
- first_name: Christopher J
full_name: Wojtan, Christopher J
id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87
last_name: Wojtan
orcid: 0000-0001-6646-5546
citation:
ama: Sperl G, Narain R, Wojtan C. Mechanics-Aware Deformation of Yarn Pattern Geometry
(Additional Animation/Model Data). 2021. doi:10.15479/AT:ISTA:9327
apa: Sperl, G., Narain, R., & Wojtan, C. (2021). Mechanics-Aware Deformation
of Yarn Pattern Geometry (Additional Animation/Model Data). IST Austria. https://doi.org/10.15479/AT:ISTA:9327
chicago: Sperl, Georg, Rahul Narain, and Chris Wojtan. “Mechanics-Aware Deformation
of Yarn Pattern Geometry (Additional Animation/Model Data).” IST Austria, 2021.
https://doi.org/10.15479/AT:ISTA:9327.
ieee: G. Sperl, R. Narain, and C. Wojtan, “Mechanics-Aware Deformation of Yarn Pattern
Geometry (Additional Animation/Model Data).” IST Austria, 2021.
ista: Sperl G, Narain R, Wojtan C. 2021. Mechanics-Aware Deformation of Yarn Pattern
Geometry (Additional Animation/Model Data), IST Austria, 10.15479/AT:ISTA:9327.
mla: Sperl, Georg, et al. Mechanics-Aware Deformation of Yarn Pattern Geometry
(Additional Animation/Model Data). IST Austria, 2021, doi:10.15479/AT:ISTA:9327.
short: G. Sperl, R. Narain, C. Wojtan, (2021).
date_created: 2021-04-16T14:26:19Z
date_published: 2021-05-01T00:00:00Z
date_updated: 2023-08-10T14:24:36Z
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title: Mechanics-Aware Deformation of Yarn Pattern Geometry (Additional Animation/Model
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tmp:
legal_code_url: https://opensource.org/licenses/MIT
name: The MIT License
short: MIT
type: software
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2021'
...
---
_id: '9818'
abstract:
- lang: eng
text: Triangle mesh-based simulations are able to produce satisfying animations
of knitted and woven cloth; however, they lack the rich geometric detail of yarn-level
simulations. Naive texturing approaches do not consider yarn-level physics, while
full yarn-level simulations may become prohibitively expensive for large garments.
We propose a method to animate yarn-level cloth geometry on top of an underlying
deforming mesh in a mechanics-aware fashion. Using triangle strains to interpolate
precomputed yarn geometry, we are able to reproduce effects such as knit loops
tightening under stretching. In combination with precomputed mesh animation or
real-time mesh simulation, our method is able to animate yarn-level cloth in real-time
at large scales.
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "We wish to thank the anonymous reviewers and the members of the
Visual Computing Group at IST Austria for their valuable feedback. We also thank
Seddi Labs for providing the garment model with fold-over seams.\r\nThis research
was supported by the Scientific Service Units (SSU) of IST Austria through resources
provided by Scientific\r\nComputing. This project has received funding from the
European Research Council (ERC) under the European Union’s Horizon 2020 research
and innovation programme under grant agreement No. 638176. Rahul Narain is supported
by a Pankaj Gupta Young Faculty Fellowship and a gift from Adobe Inc."
article_number: '168'
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Georg
full_name: Sperl, Georg
id: 4DD40360-F248-11E8-B48F-1D18A9856A87
last_name: Sperl
- first_name: Rahul
full_name: Narain, Rahul
last_name: Narain
- first_name: Christopher J
full_name: Wojtan, Christopher J
id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87
last_name: Wojtan
orcid: 0000-0001-6646-5546
citation:
ama: Sperl G, Narain R, Wojtan C. Mechanics-aware deformation of yarn pattern geometry.
ACM Transactions on Graphics. 2021;40(4). doi:10.1145/3450626.3459816
apa: Sperl, G., Narain, R., & Wojtan, C. (2021). Mechanics-aware deformation
of yarn pattern geometry. ACM Transactions on Graphics. Association for
Computing Machinery. https://doi.org/10.1145/3450626.3459816
chicago: Sperl, Georg, Rahul Narain, and Chris Wojtan. “Mechanics-Aware Deformation
of Yarn Pattern Geometry.” ACM Transactions on Graphics. Association for
Computing Machinery, 2021. https://doi.org/10.1145/3450626.3459816.
ieee: G. Sperl, R. Narain, and C. Wojtan, “Mechanics-aware deformation of yarn pattern
geometry,” ACM Transactions on Graphics, vol. 40, no. 4. Association for
Computing Machinery, 2021.
ista: Sperl G, Narain R, Wojtan C. 2021. Mechanics-aware deformation of yarn pattern
geometry. ACM Transactions on Graphics. 40(4), 168.
mla: Sperl, Georg, et al. “Mechanics-Aware Deformation of Yarn Pattern Geometry.”
ACM Transactions on Graphics, vol. 40, no. 4, 168, Association for Computing
Machinery, 2021, doi:10.1145/3450626.3459816.
short: G. Sperl, R. Narain, C. Wojtan, ACM Transactions on Graphics 40 (2021).
date_created: 2021-08-08T22:01:27Z
date_published: 2021-08-01T00:00:00Z
date_updated: 2023-08-10T14:24:36Z
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- _id: ChWo
doi: 10.1145/3450626.3459816
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project:
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call_identifier: H2020
grant_number: '638176'
name: Efficient Simulation of Natural Phenomena at Extremely Large Scales
publication: ACM Transactions on Graphics
publication_identifier:
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issn:
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publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
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relation: press_release
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title: Mechanics-aware deformation of yarn pattern geometry
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 40
year: '2021'
...
---
_id: '8385'
abstract:
- lang: eng
text: 'We present a method for animating yarn-level cloth effects using a thin-shell
solver. We accomplish this through numerical homogenization: we first use a large
number of yarn-level simulations to build a model of the potential energy density
of the cloth, and then use this energy density function to compute forces in a
thin shell simulator. We model several yarn-based materials, including both woven
and knitted fabrics. Our model faithfully reproduces expected effects like the
stiffness of woven fabrics, and the highly deformable nature and anisotropy of
knitted fabrics. Our approach does not require any real-world experiments nor
measurements; because the method is based entirely on simulations, it can generate
entirely new material models quickly, without the need for testing apparatuses
or human intervention. We provide data-driven models of several woven and knitted
fabrics, which can be used for efficient simulation with an off-the-shelf cloth
solver.'
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "We wish to thank the anonymous reviewers and the members of the
Visual Computing Group at IST Austria for their valuable feedback. We also thank
the creators of the Berkeley Garment Library [de Joya et al. 2012] for providing
garment meshes, [Krishnamurthy and Levoy 1996] and [Turk and Levoy 1994] for the
armadillo and bunny meshes, the creators of libWetCloth [Fei et al. 2018] for their
implementation of discrete elastic rod forces, and Tomáš Skřivan for\r\ninspiring
discussions and help with Mathematica code generation. This research was supported
by the Scientific Service Units (SSU) of IST Austria through resources provided
by Scientific Computing. This project has received funding from the European Research
Council (ERC) under the European Union’s Horizon 2020 research and innovation programme
under grant agreement No. 638176. Rahul Narain is supported by a Pankaj Gupta Young
Faculty Fellowship and a gift from Adobe Inc."
article_number: '48'
article_processing_charge: No
article_type: original
author:
- first_name: Georg
full_name: Sperl, Georg
id: 4DD40360-F248-11E8-B48F-1D18A9856A87
last_name: Sperl
- first_name: Rahul
full_name: Narain, Rahul
last_name: Narain
- first_name: Christopher J
full_name: Wojtan, Christopher J
id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87
last_name: Wojtan
orcid: 0000-0001-6646-5546
citation:
ama: Sperl G, Narain R, Wojtan C. Homogenized yarn-level cloth. ACM Transactions
on Graphics. 2020;39(4). doi:10.1145/3386569.3392412
apa: Sperl, G., Narain, R., & Wojtan, C. (2020). Homogenized yarn-level cloth.
ACM Transactions on Graphics. Association for Computing Machinery. https://doi.org/10.1145/3386569.3392412
chicago: Sperl, Georg, Rahul Narain, and Chris Wojtan. “Homogenized Yarn-Level Cloth.”
ACM Transactions on Graphics. Association for Computing Machinery, 2020.
https://doi.org/10.1145/3386569.3392412.
ieee: G. Sperl, R. Narain, and C. Wojtan, “Homogenized yarn-level cloth,” ACM
Transactions on Graphics, vol. 39, no. 4. Association for Computing Machinery,
2020.
ista: Sperl G, Narain R, Wojtan C. 2020. Homogenized yarn-level cloth. ACM Transactions
on Graphics. 39(4), 48.
mla: Sperl, Georg, et al. “Homogenized Yarn-Level Cloth.” ACM Transactions on
Graphics, vol. 39, no. 4, 48, Association for Computing Machinery, 2020, doi:10.1145/3386569.3392412.
short: G. Sperl, R. Narain, C. Wojtan, ACM Transactions on Graphics 39 (2020).
date_created: 2020-09-13T22:01:18Z
date_published: 2020-07-08T00:00:00Z
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has_accepted_license: '1'
intvolume: ' 39'
isi: 1
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1145/3386569.3392412
month: '07'
oa: 1
oa_version: Submitted Version
project:
- _id: 2533E772-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '638176'
name: Efficient Simulation of Natural Phenomena at Extremely Large Scales
publication: ACM Transactions on Graphics
publication_identifier:
eissn:
- '15577368'
issn:
- '07300301'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
related_material:
record:
- id: '12358'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Homogenized yarn-level cloth
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 39
year: '2020'
...
---
_id: '998'
abstract:
- lang: eng
text: 'A major open problem on the road to artificial intelligence is the development
of incrementally learning systems that learn about more and more concepts over
time from a stream of data. In this work, we introduce a new training strategy,
iCaRL, that allows learning in such a class-incremental way: only the training
data for a small number of classes has to be present at the same time and new
classes can be added progressively. iCaRL learns strong classifiers and a data
representation simultaneously. This distinguishes it from earlier works that were
fundamentally limited to fixed data representations and therefore incompatible
with deep learning architectures. We show by experiments on CIFAR-100 and ImageNet
ILSVRC 2012 data that iCaRL can learn many classes incrementally over a long period
of time where other strategies quickly fail. '
article_processing_charge: No
author:
- first_name: Sylvestre Alvise
full_name: Rebuffi, Sylvestre Alvise
last_name: Rebuffi
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Georg
full_name: Sperl, Georg
id: 4DD40360-F248-11E8-B48F-1D18A9856A87
last_name: Sperl
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Rebuffi SA, Kolesnikov A, Sperl G, Lampert C. iCaRL: Incremental classifier
and representation learning. In: Vol 2017. IEEE; 2017:5533-5542. doi:10.1109/CVPR.2017.587'
apa: 'Rebuffi, S. A., Kolesnikov, A., Sperl, G., & Lampert, C. (2017). iCaRL:
Incremental classifier and representation learning (Vol. 2017, pp. 5533–5542).
Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA,
United States: IEEE. https://doi.org/10.1109/CVPR.2017.587'
chicago: 'Rebuffi, Sylvestre Alvise, Alexander Kolesnikov, Georg Sperl, and Christoph
Lampert. “ICaRL: Incremental Classifier and Representation Learning,” 2017:5533–42.
IEEE, 2017. https://doi.org/10.1109/CVPR.2017.587.'
ieee: 'S. A. Rebuffi, A. Kolesnikov, G. Sperl, and C. Lampert, “iCaRL: Incremental
classifier and representation learning,” presented at the CVPR: Computer Vision
and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 5533–5542.'
ista: 'Rebuffi SA, Kolesnikov A, Sperl G, Lampert C. 2017. iCaRL: Incremental classifier
and representation learning. CVPR: Computer Vision and Pattern Recognition vol.
2017, 5533–5542.'
mla: 'Rebuffi, Sylvestre Alvise, et al. ICaRL: Incremental Classifier and Representation
Learning. Vol. 2017, IEEE, 2017, pp. 5533–42, doi:10.1109/CVPR.2017.587.'
short: S.A. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, in:, IEEE, 2017, pp. 5533–5542.
conference:
end_date: 2017-07-26
location: Honolulu, HA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2017-07-21
date_created: 2018-12-11T11:49:37Z
date_published: 2017-04-14T00:00:00Z
date_updated: 2023-09-22T09:51:58Z
day: '14'
department:
- _id: ChLa
- _id: ChWo
doi: 10.1109/CVPR.2017.587
ec_funded: 1
external_id:
isi:
- '000418371405066'
intvolume: ' 2017'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1611.07725
month: '04'
oa: 1
oa_version: Submitted Version
page: 5533 - 5542
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
isbn:
- 978-153860457-1
publication_status: published
publisher: IEEE
publist_id: '6400'
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'iCaRL: Incremental classifier and representation learning'
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 2017
year: '2017'
...