| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0645094 |
| utime |
20260126101547.5 |
| mtime |
20260126235959.9 |
| title
(primary) (eng) |
Harmformer: Harmonic Networks Meet Transformers for Continuous Roto-Translation Equivariance |
| specification |
| page_count |
27 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0645102 |
| ISSN |
2640-3498 |
| title
|
NeurIPS 2024 Workshop on Symmetry and Geometry in Neural Representations |
| publisher |
| place |
San Diego |
| name |
ML Research Press |
| year |
2026 |
|
|
| keyword |
transformers |
| keyword |
roto-translation |
| keyword |
geometric deep learning |
| keyword |
invariance |
| keyword |
equivariance |
| keyword |
robustness |
| author
(primary) |
| ARLID |
cav_un_auth*0438860 |
| name1 |
Karella |
| name2 |
Tomáš |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování obrazové informace |
| full_dept (eng) |
Department of Image Processing |
| department (cz) |
ZOI |
| department (eng) |
ZOI |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0439197 |
| name1 |
Harmanec |
| name2 |
Adam |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování obrazové informace |
| full_dept |
Department of Image Processing |
| department (cz) |
ZOI |
| department |
ZOI |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0293863 |
| name1 |
Kotera |
| name2 |
Jan |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování obrazové informace |
| full_dept |
Department of Image Processing |
| department (cz) |
ZOI |
| department |
ZOI |
| full_dept |
Department of Image Processing |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0254045 |
| name1 |
Blažek |
| name2 |
Jan |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování obrazové informace |
| full_dept |
Department of Image Processing |
| department (cz) |
ZOI |
| department |
ZOI |
| full_dept |
Department of Image Processing |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101209 |
| name1 |
Šroubek |
| name2 |
Filip |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování obrazové informace |
| full_dept |
Department of Image Processing |
| department (cz) |
ZOI |
| department |
ZOI |
| full_dept |
Department of Image Processing |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA24-10069S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0472834 |
|
| project |
| project_id |
VJ02010029 |
| agency |
GA MV |
| country |
CZ |
| ARLID |
cav_un_auth*0449227 |
|
| abstract
(eng) |
Convolutional Neural Networks exhibit inherent equivariance to image translation, leading to efficient parameter and data usage, faster learning, and improved robustness. The concept of translation equivariant networks has been successfully extended to rotation transformation using group convolution for discrete rotation groups and harmonic functions for the continuous rotation group encompassing 360°. We explore the compatibility of the Self-Attention mechanism with full rotation equivariance, in contrast to previous studies that focused on discrete rotation. We introduce the Harmformer, a harmonic transformer with a convolutional stem that achieves equivariance for both translation and continuous rotation. Accompanied by an end-to-end equivariance proof, the Harmformer not only outperforms previous equivariant transformers, but also demonstrates inherent stability under any continuous rotation, even without seeing rotated samples during training. |
| action |
| ARLID |
cav_un_auth*0502239 |
| name |
NeurIPS 2024 Workshop on Symmetry and Geometry in Neural Representations |
| dates |
20241214 |
| mrcbC20-s |
20241215 |
| place |
Vancouver |
| country |
CA |
|
| RIV |
JD |
| reportyear |
2026 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0374942 |
| confidential |
S |
| mrcbC96 |
https://arxiv.org/pdf/2411.03794 |
| arlyear |
2026 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0645102 NeurIPS 2024 Workshop on Symmetry and Geometry in Neural Representations ML Research Press 2026 San Diego 2640-3498 2640-3498 |
|