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master's thesis
Semantičko prognoziranje regresijom pomaka značajki
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:116971
Sović, Iva
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Sović, I. (2021). Semantičko prognoziranje regresijom pomaka značajki (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:116971

Sović, Iva. "Semantičko prognoziranje regresijom pomaka značajki." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:116971

Sović, Iva. "Semantičko prognoziranje regresijom pomaka značajki." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:116971

Sović, I. (2021). 'Semantičko prognoziranje regresijom pomaka značajki', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 10 August 2024, https://urn.nsk.hr/urn:nbn:hr:168:116971

Sović I. Semantičko prognoziranje regresijom pomaka značajki [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 August 10] Available at: https://urn.nsk.hr/urn:nbn:hr:168:116971

I. Sović, "Semantičko prognoziranje regresijom pomaka značajki", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:116971

Metadata
TitleSemantičko prognoziranje regresijom pomaka značajki
Title (english)Semantic forecasting by regressing feature motion
AuthorIva Sović
MentorSiniša Šegvić (mentor)
Committee memberSiniša Šegvić (predsjednik povjerenstva)
Committee memberMarko Čupić (član povjerenstva)
Committee memberZoran Kalafatić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-16, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj rad se bavi kratkoročnim i srednjoročnim semantičkim prognoziranjem budućnosti koje predviđa semantičku mapu određenog budućeg trenutka. Definiraju se i opisuju F2F i F2M pristupi prognoziranju. Implementirana su dva glavna modela kao okosnica za testiranje izložene teorije: \emph{single-frame} model za semantičku segmentaciju te F2MF model za prognoziranje. Predstavljen je novi način učenja \emph{single-frame} modela tako da mu je omogućeno mješovito učenje s ili bez preskočnih veza. F2MF model je nadograđen tako da može prognozirati preskočne veze i iskoristiti njihove vrijednosti za poboljšanje točnosti prognoziranja. Za kratkoročno predviđanje postignuta je točnost od 69.6 postotnih bodova, a za srednjoročno predviđanje točnost od 57.4 postotnih bodova. Oba dobivena rezultata su usporediva s rezultatima u literaturi, točnije bolji su za 0.9 odnosno za 0.6 postotnih bodova u usporedbi s odgovarajućim eksperimentima.
Abstract (english)
This work addresses the topic of short-term and mid-term semantic forecasting. The definitions of the F2F and the F2M forecasting approaches are presented. In the scope of this work, two main models were implemented and used as backbones for testing the presented theory: a single-frame model for semantic segmentation and a mixed model for forecasting. We presented a novel learning approach for the single-frame model where learning can be conducted using all, some or no skip connections. The F2MF model was extended so that it can both forecast the skip connections and also use them to improve the accuracy of its forecasting. The tested model achieved mIoU accuracies of 69.6 percentage points and 57.4 percentage points for short-term and mid-term forecasting, respectively. This constitutes an accuracy improvement compared to the current state-of-the art by 0.9 and 0.6 percentage points, respectively.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:116971
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-15 11:14:43