Unsupervised Domain Adaptation for Video Object Grounding with Cascaded Debiasing Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A9T2MQ5DC" target="_blank" >RIV/00216208:11320/23:9T2MQ5DC - isvavai.cz</a>
Result on the web
<a href="https://dl.acm.org/doi/10.1145/3581783.3612314" target="_blank" >https://dl.acm.org/doi/10.1145/3581783.3612314</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3581783.3612314" target="_blank" >10.1145/3581783.3612314</a>
Alternative languages
Result language
angličtina
Original language name
Unsupervised Domain Adaptation for Video Object Grounding with Cascaded Debiasing Learning
Original language description
"This paper addresses the Unsupervised Domain Adaptation (UDA) for the dense frame prediction task - Video Object Grounding (VOG). This investigation springs from the recognition of the limited generalization capabilities of data-driven approaches when confronted with unseen test scenarios."
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
—
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
"Proceedings of the 31st ACM International Conference on Multimedia"
ISBN
9798400701085
ISSN
—
e-ISSN
—
Number of pages
10
Pages from-to
3807-3816
Publisher name
ACM
Place of publication
Ottawa ON Canada
Event location
Ottawa ON Canada
Event date
Jan 1, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—