Enhanced SOMHunter for Known-item Search in Lifelog Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10433625" target="_blank" >RIV/00216208:11320/21:10433625 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1145/3463948.3469074" target="_blank" >https://doi.org/10.1145/3463948.3469074</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3463948.3469074" target="_blank" >10.1145/3463948.3469074</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhanced SOMHunter for Known-item Search in Lifelog Data

Popis výsledku v původním jazyce

SOMHunter represents a modern light-weight framework for known-item search in datasets of visual data like images or videos. The framework combines an effective W2VV++ text-to-image search approach, a traditional Bayesian like model for maintenance of relevance scores influenced by positive examples, and several types of exploration and exploitation displays. With this initial setting in 2020, already the first prototype of the system turned out to be highly competitive in comparison with other state-of-the-art systems at Video Browser Showdown and Lifelog Search Challenge competitions. In this paper, we present a new version of the system further extending the list of visual data search capabilities. The new version combines localized text queries with collage queries tested at VBS 2021 in two separate systems by our team. Furthermore, the new version of SOMHunter will integrate also the new CLIP text search model recently released by OpenAI. We believe that all the extensions will improve chances to effectively initialize the search that can continue with already supported browsing capabilities.

Název v anglickém jazyce

Enhanced SOMHunter for Known-item Search in Lifelog Data

Popis výsledku anglicky

SOMHunter represents a modern light-weight framework for known-item search in datasets of visual data like images or videos. The framework combines an effective W2VV++ text-to-image search approach, a traditional Bayesian like model for maintenance of relevance scores influenced by positive examples, and several types of exploration and exploitation displays. With this initial setting in 2020, already the first prototype of the system turned out to be highly competitive in comparison with other state-of-the-art systems at Video Browser Showdown and Lifelog Search Challenge competitions. In this paper, we present a new version of the system further extending the list of visual data search capabilities. The new version combines localized text queries with collage queries tested at VBS 2021 in two separate systems by our team. Furthermore, the new version of SOMHunter will integrate also the new CLIP text search model recently released by OpenAI. We believe that all the extensions will improve chances to effectively initialize the search that can continue with already supported browsing capabilities.

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)

Projekt

<a href="/cs/project/GJ19-22071Y" target="_blank" >GJ19-22071Y: Flexibilní modely pro hledání známé scény v rozsáhlých kolekcích videa</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

LSC &apos;21: Proceedings of the 4th Annual on Lifelog Search Challenge

ISBN

978-1-4503-8533-6

ISSN

—

e-ISSN

—

Počet stran výsledku

3

Strana od-do

71-73

Název nakladatele

ACM

Místo vydání

New YorkNYUnited States

Místo konání akce

Taipei, Taiwan

Datum konání akce

21. 8. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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