Self-Supervised Video Similarity Learning
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00370573" target="_blank" >RIV/68407700:21230/23:00370573 - isvavai.cz</a>
Výsledek na webu
<a href="https://doi.org/10.1109/CVPRW59228.2023.00504" target="_blank" >https://doi.org/10.1109/CVPRW59228.2023.00504</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPRW59228.2023.00504" target="_blank" >10.1109/CVPRW59228.2023.00504</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Self-Supervised Video Similarity Learning
Popis výsledku v původním jazyce
We introduce S2VS, a video similarity learning approach with self-supervision. Self-Supervised Learning (SSL) is typically used to train deep models on a proxy task so as to have strong transferability on target tasks after fine-tuning. Here, in contrast to prior work, SSL is used to perform video similarity learning and address multiple retrieval and detection tasks at once with no use of labeled data. This is achieved by learning via instance-discrimination with task-tailored augmentations and the widely used InfoNCE loss together with an additional loss operating jointly on self-similarity and hard-negative similarity. We benchmark our method on tasks where video relevance is defined with varying granularity, ranging from video copies to videos depicting the same incident or event. We learn a single universal model that achieves state-of-the-art performance on all tasks, surpassing previously proposed methods that use labeled data. The code and pretrained models are publicly available at: https://github.com/gkordo/s2vs
Název v anglickém jazyce
Self-Supervised Video Similarity Learning
Popis výsledku anglicky
We introduce S2VS, a video similarity learning approach with self-supervision. Self-Supervised Learning (SSL) is typically used to train deep models on a proxy task so as to have strong transferability on target tasks after fine-tuning. Here, in contrast to prior work, SSL is used to perform video similarity learning and address multiple retrieval and detection tasks at once with no use of labeled data. This is achieved by learning via instance-discrimination with task-tailored augmentations and the widely used InfoNCE loss together with an additional loss operating jointly on self-similarity and hard-negative similarity. We benchmark our method on tasks where video relevance is defined with varying granularity, ranging from video copies to videos depicting the same incident or event. We learn a single universal model that achieves state-of-the-art performance on all tasks, surpassing previously proposed methods that use labeled data. The code and pretrained models are publicly available at: https://github.com/gkordo/s2vs
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
<a href="/cs/project/GM21-28830M" target="_blank" >GM21-28830M: Učení Univerzální Vizuální Reprezentace s Omezenou Supervizí</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Whorkshops (CVPRW)
ISBN
979-8-3503-0249-3
ISSN
2160-7508
e-ISSN
2160-7516
Počet stran výsledku
11
Strana od-do
4756-4766
Název nakladatele
IEEE Computer Society
Místo vydání
USA
Místo konání akce
Vancouver
Datum konání akce
18. 6. 2023
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—