End-to-End Learning of Visual Representations from Uncurated Instructional Videos
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F20%3A00343640" target="_blank" >RIV/68407700:21730/20:00343640 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/CVPR42600.2020.00990" target="_blank" >https://doi.org/10.1109/CVPR42600.2020.00990</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR42600.2020.00990" target="_blank" >10.1109/CVPR42600.2020.00990</a>
Alternative languages
Result language
angličtina
Original language name
End-to-End Learning of Visual Representations from Uncurated Instructional Videos
Original language description
Annotating videos is cumbersome, expensive and not scalable. Yet, many strong video models still rely on manually annotated data. With the recent introduction of the HowTo100M dataset, narrated videos now offer the possibility of learning video representations without manual supervision. In this work we propose a new learning approach, MIL-NCE, capable of addressing mis- alignments inherent in narrated videos. With this approach we are able to learn strong video representations from scratch, without the need for any manual annotation. We evaluate our representations on a wide range of four downstream tasks over eight datasets: action recognition (HMDB-51, UCF-101, Kinetics-700), text-to- video retrieval (YouCook2, MSR-VTT), action localization (YouTube-8M Segments, CrossTask) and action segmentation (COIN). Our method outperforms all published self-supervised approaches for these tasks as well as several fully supervised baselines.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition
ISBN
978-1-7281-7169-2
ISSN
1063-6919
e-ISSN
2575-7075
Number of pages
11
Pages from-to
9876-9886
Publisher name
IEEE Computer Society
Place of publication
USA
Event location
Seattle
Event date
Jun 13, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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