Self-Supervised Video Similarity Learning

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Self-Supervised Video Similarity Learning

Original language description

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

Czech name

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Czech description

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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)

Project

<a href="/en/project/GM21-28830M" target="_blank" >GM21-28830M: Learning Universal Visual Representation with Limited Supervision</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

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

Number of pages

11

Pages from-to

4756-4766

Publisher name

IEEE Computer Society

Place of publication

USA

Event location

Vancouver

Event date

Jun 18, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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