---
_id: '7640'
abstract:
- lang: eng
text: We propose a new model for detecting visual relationships, such as "person
riding motorcycle" or "bottle on table". This task is an important step towards
comprehensive structured mage understanding, going beyond detecting individual
objects. Our main novelty is a Box Attention mechanism that allows to model pairwise
interactions between objects using standard object detection pipelines. The resulting
model is conceptually clean, expressive and relies on well-justified training
and prediction procedures. Moreover, unlike previously proposed approaches, our
model does not introduce any additional complex components or hyperparameters
on top of those already required by the underlying detection model. We conduct
an experimental evaluation on two datasets, V-COCO and Open Images, demonstrating
strong quantitative and qualitative results.
article_number: 1749-1753
article_processing_charge: No
arxiv: 1
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Alina
full_name: Kuznetsova, Alina
last_name: Kuznetsova
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Vittorio
full_name: Ferrari, Vittorio
last_name: Ferrari
citation:
ama: 'Kolesnikov A, Kuznetsova A, Lampert C, Ferrari V. Detecting visual relationships
using box attention. In: Proceedings of the 2019 International Conference on
Computer Vision Workshop. IEEE; 2019. doi:10.1109/ICCVW.2019.00217'
apa: 'Kolesnikov, A., Kuznetsova, A., Lampert, C., & Ferrari, V. (2019). Detecting
visual relationships using box attention. In Proceedings of the 2019 International
Conference on Computer Vision Workshop. Seoul, South Korea: IEEE. https://doi.org/10.1109/ICCVW.2019.00217'
chicago: Kolesnikov, Alexander, Alina Kuznetsova, Christoph Lampert, and Vittorio
Ferrari. “Detecting Visual Relationships Using Box Attention.” In Proceedings
of the 2019 International Conference on Computer Vision Workshop. IEEE, 2019.
https://doi.org/10.1109/ICCVW.2019.00217.
ieee: A. Kolesnikov, A. Kuznetsova, C. Lampert, and V. Ferrari, “Detecting visual
relationships using box attention,” in Proceedings of the 2019 International
Conference on Computer Vision Workshop, Seoul, South Korea, 2019.
ista: 'Kolesnikov A, Kuznetsova A, Lampert C, Ferrari V. 2019. Detecting visual
relationships using box attention. Proceedings of the 2019 International Conference
on Computer Vision Workshop. ICCVW: International Conference on Computer Vision
Workshop, 1749–1753.'
mla: Kolesnikov, Alexander, et al. “Detecting Visual Relationships Using Box Attention.”
Proceedings of the 2019 International Conference on Computer Vision Workshop,
1749–1753, IEEE, 2019, doi:10.1109/ICCVW.2019.00217.
short: A. Kolesnikov, A. Kuznetsova, C. Lampert, V. Ferrari, in:, Proceedings of
the 2019 International Conference on Computer Vision Workshop, IEEE, 2019.
conference:
end_date: 2019-10-28
location: Seoul, South Korea
name: 'ICCVW: International Conference on Computer Vision Workshop'
start_date: 2019-10-27
date_created: 2020-04-05T22:00:51Z
date_published: 2019-10-01T00:00:00Z
date_updated: 2025-04-15T07:10:23Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/ICCVW.2019.00217
ec_funded: 1
external_id:
arxiv:
- '1807.02136'
isi:
- '000554591601098'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1807.02136
month: '10'
oa: 1
oa_version: Preprint
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Proceedings of the 2019 International Conference on Computer Vision Workshop
publication_identifier:
isbn:
- '9781728150239'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Detecting visual relationships using box attention
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2019'
...
---
OA_place: publisher
_id: '197'
abstract:
- lang: eng
text: Modern computer vision systems heavily rely on statistical machine learning
models, which typically require large amounts of labeled data to be learned reliably.
Moreover, very recently computer vision research widely adopted techniques for
representation learning, which further increase the demand for labeled data. However,
for many important practical problems there is relatively small amount of labeled
data available, so it is problematic to leverage full potential of the representation
learning methods. One way to overcome this obstacle is to invest substantial resources
into producing large labelled datasets. Unfortunately, this can be prohibitively
expensive in practice. In this thesis we focus on the alternative way of tackling
the aforementioned issue. We concentrate on methods, which make use of weakly-labeled
or even unlabeled data. Specifically, the first half of the thesis is dedicated
to the semantic image segmentation task. We develop a technique, which achieves
competitive segmentation performance and only requires annotations in a form of
global image-level labels instead of dense segmentation masks. Subsequently, we
present a new methodology, which further improves segmentation performance by
leveraging tiny additional feedback from a human annotator. By using our methods
practitioners can greatly reduce the amount of data annotation effort, which is
required to learn modern image segmentation models. In the second half of the
thesis we focus on methods for learning from unlabeled visual data. We study a
family of autoregressive models for modeling structure of natural images and discuss
potential applications of these models. Moreover, we conduct in-depth study of
one of these applications, where we develop the state-of-the-art model for the
probabilistic image colorization task.
acknowledgement: I also gratefully acknowledge the support of NVIDIA Corporation with
the donation of the GPUs used for this research.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
citation:
ama: Kolesnikov A. Weakly-Supervised Segmentation and Unsupervised Modeling of Natural
Images. 2018. doi:10.15479/AT:ISTA:th_1021
apa: Kolesnikov, A. (2018). Weakly-Supervised Segmentation and Unsupervised Modeling
of Natural Images. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:th_1021
chicago: Kolesnikov, Alexander. “Weakly-Supervised Segmentation and Unsupervised
Modeling of Natural Images.” Institute of Science and Technology Austria, 2018.
https://doi.org/10.15479/AT:ISTA:th_1021.
ieee: A. Kolesnikov, “Weakly-Supervised Segmentation and Unsupervised Modeling of
Natural Images,” Institute of Science and Technology Austria, 2018.
ista: Kolesnikov A. 2018. Weakly-Supervised Segmentation and Unsupervised Modeling
of Natural Images. Institute of Science and Technology Austria.
mla: Kolesnikov, Alexander. Weakly-Supervised Segmentation and Unsupervised Modeling
of Natural Images. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:th_1021.
short: A. Kolesnikov, Weakly-Supervised Segmentation and Unsupervised Modeling of
Natural Images, Institute of Science and Technology Austria, 2018.
corr_author: '1'
date_created: 2018-12-11T11:45:09Z
date_published: 2018-05-25T00:00:00Z
date_updated: 2026-04-08T14:05:16Z
day: '25'
ddc:
- '004'
degree_awarded: PhD
department:
- _id: ChLa
doi: 10.15479/AT:ISTA:th_1021
ec_funded: 1
file:
- access_level: open_access
checksum: bc678e02468d8ebc39dc7267dfb0a1c4
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:14:57Z
date_updated: 2020-07-14T12:45:22Z
file_id: '5113'
file_name: IST-2018-1021-v1+1_thesis-unsigned-pdfa.pdf
file_size: 12918758
relation: main_file
- access_level: closed
checksum: bc66973b086da5a043f1162dcfb1fde4
content_type: application/zip
creator: dernst
date_created: 2019-04-05T09:34:49Z
date_updated: 2020-07-14T12:45:22Z
file_id: '6225'
file_name: 2018_Thesis_Kolesnikov_source.zip
file_size: 55973760
relation: source_file
file_date_updated: 2020-07-14T12:45:22Z
has_accepted_license: '1'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: '113'
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '7718'
pubrep_id: '1021'
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images
type: dissertation
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
year: '2018'
...
---
_id: '563'
abstract:
- lang: eng
text: "In continuous populations with local migration, nearby pairs of individuals
have on average more similar genotypes\r\nthan geographically well separated pairs.
A barrier to gene flow distorts this classical pattern of isolation by distance.
Genetic similarity is decreased for sample pairs on different sides of the barrier
and increased for pairs on the same side near the barrier. Here, we introduce
an inference scheme that utilizes this signal to detect and estimate the strength
of a linear barrier to gene flow in two-dimensions. We use a diffusion approximation
to model the effects of a barrier on the geographical spread of ancestry backwards
in time. This approach allows us to calculate the chance of recent coalescence
and probability of identity by descent. We introduce an inference scheme that
fits these theoretical results to the geographical covariance structure of bialleleic
genetic markers. It can estimate the strength of the barrier as well as several
demographic parameters. We investigate the power of our inference scheme to detect
barriers by applying it to a wide range of simulated data. We also showcase an
example application to a Antirrhinum majus (snapdragon) flower color hybrid zone,
where we do not detect any signal of a strong genome wide barrier to gene flow."
article_processing_charge: No
author:
- first_name: Harald
full_name: Ringbauer, Harald
id: 417FCFF4-F248-11E8-B48F-1D18A9856A87
last_name: Ringbauer
orcid: 0000-0002-4884-9682
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: David
full_name: Field, David
last_name: Field
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
citation:
ama: Ringbauer H, Kolesnikov A, Field D, Barton NH. Estimating barriers to gene
flow from distorted isolation-by-distance patterns. Genetics. 2018;208(3):1231-1245.
doi:10.1534/genetics.117.300638
apa: Ringbauer, H., Kolesnikov, A., Field, D., & Barton, N. H. (2018). Estimating
barriers to gene flow from distorted isolation-by-distance patterns. Genetics.
Genetics Society of America. https://doi.org/10.1534/genetics.117.300638
chicago: Ringbauer, Harald, Alexander Kolesnikov, David Field, and Nicholas H Barton.
“Estimating Barriers to Gene Flow from Distorted Isolation-by-Distance Patterns.”
Genetics. Genetics Society of America, 2018. https://doi.org/10.1534/genetics.117.300638.
ieee: H. Ringbauer, A. Kolesnikov, D. Field, and N. H. Barton, “Estimating barriers
to gene flow from distorted isolation-by-distance patterns,” Genetics,
vol. 208, no. 3. Genetics Society of America, pp. 1231–1245, 2018.
ista: Ringbauer H, Kolesnikov A, Field D, Barton NH. 2018. Estimating barriers to
gene flow from distorted isolation-by-distance patterns. Genetics. 208(3), 1231–1245.
mla: Ringbauer, Harald, et al. “Estimating Barriers to Gene Flow from Distorted
Isolation-by-Distance Patterns.” Genetics, vol. 208, no. 3, Genetics Society
of America, 2018, pp. 1231–45, doi:10.1534/genetics.117.300638.
short: H. Ringbauer, A. Kolesnikov, D. Field, N.H. Barton, Genetics 208 (2018) 1231–1245.
corr_author: '1'
date_created: 2018-12-11T11:47:12Z
date_published: 2018-03-01T00:00:00Z
date_updated: 2026-04-08T14:06:35Z
day: '01'
department:
- _id: NiBa
- _id: ChLa
doi: 10.1534/genetics.117.300638
external_id:
isi:
- '000426219600025'
intvolume: ' 208'
isi: 1
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.biorxiv.org/content/10.1101/205484v1
month: '03'
oa: 1
oa_version: Preprint
page: 1231-1245
publication: Genetics
publication_status: published
publisher: Genetics Society of America
publist_id: '7251'
quality_controlled: '1'
related_material:
record:
- id: '200'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Estimating barriers to gene flow from distorted isolation-by-distance patterns
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 208
year: '2018'
...
---
_id: '1000'
abstract:
- lang: eng
text: 'We study probabilistic models of natural images and extend the autoregressive
family of PixelCNN models by incorporating latent variables. Subsequently, we
describe two new generative image models that exploit different image transformations
as latent variables: a quantized grayscale view of the image or a multi-resolution
image pyramid. The proposed models tackle two known shortcomings of existing PixelCNN
models: 1) their tendency to focus on low-level image details, while largely ignoring
high-level image information, such as object shapes, and 2) their computationally
costly procedure for image sampling. We experimentally demonstrate benefits of
our LatentPixelCNN models, in particular showing that they produce much more realistically
looking image samples than previous state-of-the-art probabilistic models. '
acknowledgement: We thank Tim Salimans for spotting a mistake in our preliminary arXiv
manuscript. This work was funded by the European Research Council under the European
Unions Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no 308036.
article_processing_charge: No
arxiv: 1
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kolesnikov A, Lampert C. PixelCNN models with auxiliary variables for natural
image modeling. In: 34th International Conference on Machine Learning.
Vol 70. JMLR; 2017:1905-1914.'
apa: 'Kolesnikov, A., & Lampert, C. (2017). PixelCNN models with auxiliary variables
for natural image modeling. In 34th International Conference on Machine Learning
(Vol. 70, pp. 1905–1914). Sydney, Australia: JMLR.'
chicago: Kolesnikov, Alexander, and Christoph Lampert. “PixelCNN Models with Auxiliary
Variables for Natural Image Modeling.” In 34th International Conference on
Machine Learning, 70:1905–14. JMLR, 2017.
ieee: A. Kolesnikov and C. Lampert, “PixelCNN models with auxiliary variables for
natural image modeling,” in 34th International Conference on Machine Learning,
Sydney, Australia, 2017, vol. 70, pp. 1905–1914.
ista: 'Kolesnikov A, Lampert C. 2017. PixelCNN models with auxiliary variables for
natural image modeling. 34th International Conference on Machine Learning. ICML:
International Conference on Machine Learning vol. 70, 1905–1914.'
mla: Kolesnikov, Alexander, and Christoph Lampert. “PixelCNN Models with Auxiliary
Variables for Natural Image Modeling.” 34th International Conference on Machine
Learning, vol. 70, JMLR, 2017, pp. 1905–14.
short: A. Kolesnikov, C. Lampert, in:, 34th International Conference on Machine
Learning, JMLR, 2017, pp. 1905–1914.
conference:
end_date: 2017-08-11
location: Sydney, Australia
name: 'ICML: International Conference on Machine Learning'
start_date: 2017-08-06
date_created: 2018-12-11T11:49:37Z
date_published: 2017-08-01T00:00:00Z
date_updated: 2025-04-15T07:10:22Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '1612.08185'
isi:
- '000683309501102'
has_accepted_license: '1'
intvolume: ' 70'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1612.08185
month: '08'
oa: 1
oa_version: Submitted Version
page: 1905 - 1914
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: 34th International Conference on Machine Learning
publication_identifier:
isbn:
- 978-151085514-4
publication_status: published
publisher: JMLR
publist_id: '6398'
quality_controlled: '1'
scopus_import: '1'
status: public
title: PixelCNN models with auxiliary variables for natural image modeling
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 70
year: '2017'
...
---
_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
arxiv: 1
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: 2025-06-04T08:18:32Z
day: '14'
department:
- _id: ChLa
- _id: ChWo
doi: 10.1109/CVPR.2017.587
ec_funded: 1
external_id:
arxiv:
- '1611.07725'
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2017
year: '2017'
...
---
_id: '911'
abstract:
- lang: eng
text: We develop a probabilistic technique for colorizing grayscale natural images.
In light of the intrinsic uncertainty of this task, the proposed probabilistic
framework has numerous desirable properties. In particular, our model is able
to produce multiple plausible and vivid colorizations for a given grayscale image
and is one of the first colorization models to provide a proper stochastic sampling
scheme. Moreover, our training procedure is supported by a rigorous theoretical
framework that does not require any ad hoc heuristics and allows for efficient
modeling and learning of the joint pixel color distribution.We demonstrate strong
quantitative and qualitative experimental results on the CIFAR-10 dataset and
the challenging ILSVRC 2012 dataset.
article_processing_charge: No
arxiv: 1
author:
- first_name: Amélie
full_name: Royer, Amélie
id: 3811D890-F248-11E8-B48F-1D18A9856A87
last_name: Royer
orcid: 0000-0002-8407-0705
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Royer A, Kolesnikov A, Lampert C. Probabilistic image colorization. In: BMVA
Press; 2017:85.1-85.12. doi:10.5244/c.31.85'
apa: 'Royer, A., Kolesnikov, A., & Lampert, C. (2017). Probabilistic image colorization
(p. 85.1-85.12). Presented at the BMVC: British Machine Vision Conference, London,
United Kingdom: BMVA Press. https://doi.org/10.5244/c.31.85'
chicago: Royer, Amélie, Alexander Kolesnikov, and Christoph Lampert. “Probabilistic
Image Colorization,” 85.1-85.12. BMVA Press, 2017. https://doi.org/10.5244/c.31.85.
ieee: 'A. Royer, A. Kolesnikov, and C. Lampert, “Probabilistic image colorization,”
presented at the BMVC: British Machine Vision Conference, London, United Kingdom,
2017, p. 85.1-85.12.'
ista: 'Royer A, Kolesnikov A, Lampert C. 2017. Probabilistic image colorization.
BMVC: British Machine Vision Conference, 85.1-85.12.'
mla: Royer, Amélie, et al. Probabilistic Image Colorization. BMVA Press,
2017, p. 85.1-85.12, doi:10.5244/c.31.85.
short: A. Royer, A. Kolesnikov, C. Lampert, in:, BMVA Press, 2017, p. 85.1-85.12.
conference:
end_date: 2017-09-07
location: London, United Kingdom
name: 'BMVC: British Machine Vision Conference'
start_date: 2017-09-04
corr_author: '1'
date_created: 2018-12-11T11:49:09Z
date_published: 2017-09-01T00:00:00Z
date_updated: 2026-04-08T07:26:44Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.5244/c.31.85
ec_funded: 1
external_id:
arxiv:
- '1705.04258'
file:
- access_level: open_access
content_type: application/pdf
creator: dernst
date_created: 2020-08-10T07:14:33Z
date_updated: 2020-08-10T07:14:33Z
file_id: '8224'
file_name: 2017_BMVC_Royer.pdf
file_size: 1625363
relation: main_file
success: 1
file_date_updated: 2020-08-10T07:14:33Z
has_accepted_license: '1'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: 85.1-85.12
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
eisbn:
- 190172560X
publication_status: published
publisher: BMVA Press
publist_id: '6532'
quality_controlled: '1'
related_material:
record:
- id: '8390'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Probabilistic image colorization
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2017'
...
---
_id: '1369'
abstract:
- lang: eng
text: 'We introduce a new loss function for the weakly-supervised training of semantic
image segmentation models based on three guiding principles: to seed with weak
localization cues, to expand objects based on the information about which classes
can occur in an image, and to constrain the segmentations to coincide with object
boundaries. We show experimentally that training a deep convolutional neural network
using the proposed loss function leads to substantially better segmentations than
previous state-of-the-art methods on the challenging PASCAL VOC 2012 dataset.
We furthermore give insight into the working mechanism of our method by a detailed
experimental study that illustrates how the segmentation quality is affected by
each term of the proposed loss function as well as their combinations.'
alternative_title:
- LNCS
article_processing_charge: No
arxiv: 1
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kolesnikov A, Lampert C. Seed, expand and constrain: Three principles for
weakly-supervised image segmentation. In: Vol 9908. Springer; 2016:695-711. doi:10.1007/978-3-319-46493-0_42'
apa: 'Kolesnikov, A., & Lampert, C. (2016). Seed, expand and constrain: Three
principles for weakly-supervised image segmentation (Vol. 9908, pp. 695–711).
Presented at the ECCV: European Conference on Computer Vision, Amsterdam, The
Netherlands: Springer. https://doi.org/10.1007/978-3-319-46493-0_42'
chicago: 'Kolesnikov, Alexander, and Christoph Lampert. “Seed, Expand and Constrain:
Three Principles for Weakly-Supervised Image Segmentation,” 9908:695–711. Springer,
2016. https://doi.org/10.1007/978-3-319-46493-0_42.'
ieee: 'A. Kolesnikov and C. Lampert, “Seed, expand and constrain: Three principles
for weakly-supervised image segmentation,” presented at the ECCV: European Conference
on Computer Vision, Amsterdam, The Netherlands, 2016, vol. 9908, pp. 695–711.'
ista: 'Kolesnikov A, Lampert C. 2016. Seed, expand and constrain: Three principles
for weakly-supervised image segmentation. ECCV: European Conference on Computer
Vision, LNCS, vol. 9908, 695–711.'
mla: 'Kolesnikov, Alexander, and Christoph Lampert. Seed, Expand and Constrain:
Three Principles for Weakly-Supervised Image Segmentation. Vol. 9908, Springer,
2016, pp. 695–711, doi:10.1007/978-3-319-46493-0_42.'
short: A. Kolesnikov, C. Lampert, in:, Springer, 2016, pp. 695–711.
conference:
end_date: 2016-10-14
location: Amsterdam, The Netherlands
name: 'ECCV: European Conference on Computer Vision'
start_date: 2016-10-11
corr_author: '1'
date_created: 2018-12-11T11:51:37Z
date_published: 2016-09-15T00:00:00Z
date_updated: 2025-09-22T07:39:37Z
day: '15'
department:
- _id: ChLa
doi: 10.1007/978-3-319-46493-0_42
ec_funded: 1
external_id:
arxiv:
- '1603.06098'
isi:
- '000389385100042'
intvolume: ' 9908'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1603.06098
month: '09'
oa: 1
oa_version: Preprint
page: 695 - 711
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: Springer
publist_id: '5842'
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Seed, expand and constrain: Three principles for weakly-supervised image segmentation'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 9908
year: '2016'
...
---
_id: '1102'
abstract:
- lang: eng
text: Weakly-supervised object localization methods tend to fail for object classes
that consistently co-occur with the same background elements, e.g. trains on tracks.
We propose a method to overcome these failures by adding a very small amount of
model-specific additional annotation. The main idea is to cluster a deep network\'s
mid-level representations and assign object or distractor labels to each cluster.
Experiments show substantially improved localization results on the challenging
ILSVC2014 dataset for bounding box detection and the PASCAL VOC2012 dataset for
semantic segmentation.
acknowledgement: "This work was funded in parts by the European Research Council\r\nunder
the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant\r\nagreement
no 308036. We gratefully acknowledge the support of NVIDIA Corporation with\r\nthe
donation of the GPUs used for this research."
article_processing_charge: No
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kolesnikov A, Lampert C. Improving weakly-supervised object localization by
micro-annotation. In: Proceedings of the British Machine Vision Conference
2016. Vol 2016-September. BMVA Press; 2016:92.1-92.12. doi:10.5244/C.30.92'
apa: 'Kolesnikov, A., & Lampert, C. (2016). Improving weakly-supervised object
localization by micro-annotation. In Proceedings of the British Machine Vision
Conference 2016 (Vol. 2016–September, p. 92.1-92.12). York, United Kingdom:
BMVA Press. https://doi.org/10.5244/C.30.92'
chicago: Kolesnikov, Alexander, and Christoph Lampert. “Improving Weakly-Supervised
Object Localization by Micro-Annotation.” In Proceedings of the British Machine
Vision Conference 2016, 2016–September:92.1-92.12. BMVA Press, 2016. https://doi.org/10.5244/C.30.92.
ieee: A. Kolesnikov and C. Lampert, “Improving weakly-supervised object localization
by micro-annotation,” in Proceedings of the British Machine Vision Conference
2016, York, United Kingdom, 2016, vol. 2016–September, p. 92.1-92.12.
ista: 'Kolesnikov A, Lampert C. 2016. Improving weakly-supervised object localization
by micro-annotation. Proceedings of the British Machine Vision Conference 2016.
BMVC: British Machine Vision Conference vol. 2016–September, 92.1-92.12.'
mla: Kolesnikov, Alexander, and Christoph Lampert. “Improving Weakly-Supervised
Object Localization by Micro-Annotation.” Proceedings of the British Machine
Vision Conference 2016, vol. 2016–September, BMVA Press, 2016, p. 92.1-92.12,
doi:10.5244/C.30.92.
short: A. Kolesnikov, C. Lampert, in:, Proceedings of the British Machine Vision
Conference 2016, BMVA Press, 2016, p. 92.1-92.12.
conference:
end_date: 2016-09-22
location: York, United Kingdom
name: 'BMVC: British Machine Vision Conference'
start_date: 2016-09-19
date_created: 2018-12-11T11:50:09Z
date_published: 2016-09-01T00:00:00Z
date_updated: 2026-06-18T10:46:30Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.5244/C.30.92
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.bmva.org/bmvc/2016/papers/paper092/paper092.pdf
month: '09'
oa: 1
oa_version: Published Version
page: 92.1-92.12
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Proceedings of the British Machine Vision Conference 2016
publication_status: published
publisher: BMVA Press
publist_id: '6273'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Improving weakly-supervised object localization by micro-annotation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2016-September
year: '2016'
...
---
_id: '2171'
abstract:
- lang: eng
text: We present LS-CRF, a new method for training cyclic Conditional Random Fields
(CRFs) from large datasets that is inspired by classical closed-form expressions
for the maximum likelihood parameters of a generative graphical model with tree
topology. Training a CRF with LS-CRF requires only solving a set of independent
regression problems, each of which can be solved efficiently in closed form or
by an iterative solver. This makes LS-CRF orders of magnitude faster than classical
CRF training based on probabilistic inference, and at the same time more flexible
and easier to implement than other approximate techniques, such as pseudolikelihood
or piecewise training. We apply LS-CRF to the task of semantic image segmentation,
showing that it achieves on par accuracy to other training techniques at higher
speed, thereby allowing efficient CRF training from very large training sets.
For example, training a linearly parameterized pairwise CRF on 150,000 images
requires less than one hour on a modern workstation.
alternative_title:
- LNCS
article_processing_charge: No
arxiv: 1
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Matthieu
full_name: Guillaumin, Matthieu
last_name: Guillaumin
- first_name: Vittorio
full_name: Ferrari, Vittorio
last_name: Ferrari
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kolesnikov A, Guillaumin M, Ferrari V, Lampert C. Closed-form approximate
CRF training for scalable image segmentation. In: Fleet D, Pajdla T, Schiele B,
Tuytelaars T, eds. Lecture Notes in Computer Science (Including Subseries Lecture
Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol
8691. Springer; 2014:550-565. doi:10.1007/978-3-319-10578-9_36'
apa: 'Kolesnikov, A., Guillaumin, M., Ferrari, V., & Lampert, C. (2014). Closed-form
approximate CRF training for scalable image segmentation. In D. Fleet, T. Pajdla,
B. Schiele, & T. Tuytelaars (Eds.), Lecture Notes in Computer Science (including
subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(Vol. 8691, pp. 550–565). Zurich, Switzerland: Springer. https://doi.org/10.1007/978-3-319-10578-9_36'
chicago: Kolesnikov, Alexander, Matthieu Guillaumin, Vittorio Ferrari, and Christoph
Lampert. “Closed-Form Approximate CRF Training for Scalable Image Segmentation.”
In Lecture Notes in Computer Science (Including Subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in Bioinformatics), edited by David
Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars, 8691:550–65. Springer,
2014. https://doi.org/10.1007/978-3-319-10578-9_36.
ieee: A. Kolesnikov, M. Guillaumin, V. Ferrari, and C. Lampert, “Closed-form approximate
CRF training for scalable image segmentation,” in Lecture Notes in Computer
Science (including subseries Lecture Notes in Artificial Intelligence and Lecture
Notes in Bioinformatics), Zurich, Switzerland, 2014, vol. 8691, no. PART 3,
pp. 550–565.
ista: 'Kolesnikov A, Guillaumin M, Ferrari V, Lampert C. 2014. Closed-form approximate
CRF training for scalable image segmentation. Lecture Notes in Computer Science
(including subseries Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics). ECCV: European Conference on Computer Vision, LNCS, vol. 8691,
550–565.'
mla: Kolesnikov, Alexander, et al. “Closed-Form Approximate CRF Training for Scalable
Image Segmentation.” Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
edited by David Fleet et al., vol. 8691, no. PART 3, Springer, 2014, pp. 550–65,
doi:10.1007/978-3-319-10578-9_36.
short: A. Kolesnikov, M. Guillaumin, V. Ferrari, C. Lampert, in:, D. Fleet, T. Pajdla,
B. Schiele, T. Tuytelaars (Eds.), Lecture Notes in Computer Science (Including
Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
Springer, 2014, pp. 550–565.
conference:
end_date: 2014-09-12
location: Zurich, Switzerland
name: 'ECCV: European Conference on Computer Vision'
start_date: 2014-09-06
date_created: 2018-12-11T11:56:07Z
date_published: 2014-09-01T00:00:00Z
date_updated: 2025-06-11T07:59:20Z
day: '01'
department:
- _id: ChLa
doi: 10.1007/978-3-319-10578-9_36
ec_funded: 1
editor:
- first_name: David
full_name: Fleet, David
last_name: Fleet
- first_name: Tomas
full_name: Pajdla, Tomas
last_name: Pajdla
- first_name: Bernt
full_name: Schiele, Bernt
last_name: Schiele
- first_name: Tinne
full_name: Tuytelaars, Tinne
last_name: Tuytelaars
external_id:
arxiv:
- '1403.7057'
intvolume: ' 8691'
issue: PART 3
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1403.7057
month: '09'
oa: 1
oa_version: Submitted Version
page: 550 - 565
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Lecture Notes in Computer Science (including subseries Lecture Notes
in Artificial Intelligence and Lecture Notes in Bioinformatics)
publication_status: published
publisher: Springer
publist_id: '4813'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Closed-form approximate CRF training for scalable image segmentation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 8691
year: '2014'
...