Open-set segmentation of images by means of negative examples

Bevandić, Petra

prikaz prve stranice dokumenta Open-set segmentation of images by means of negative examples

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doctoral thesis

Open-set segmentation of images by means of negative examples

Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:468120

Bevandić, Petra

University of Zagreb
Faculty of Electrical Engineering and Computing

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APA 6th Edition

Bevandić, P. (2023). Open-set segmentation of images by means of negative examples (Doctoral thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:468120

MLA 8th Edition

Bevandić, Petra. "Open-set segmentation of images by means of negative examples." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:468120

Chicago 17th Edition

Harvard

Bevandić, P. (2023). 'Open-set segmentation of images by means of negative examples', Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 08 September 2024, https://urn.nsk.hr/urn:nbn:hr:168:468120

Vancouver

Bevandić P. Open-set segmentation of images by means of negative examples [Doctoral thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 September 08] Available at: https://urn.nsk.hr/urn:nbn:hr:168:468120

IEEE

P. Bevandić, "Open-set segmentation of images by means of negative examples", Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:468120

Cite this item: https://urn.nsk.hr/urn:nbn:hr:168:468120

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Metadata

Title	Open-set segmentation of images by means of negative examples
Title (croatian)	Segmentacija slika nad otvorenim skupom razreda putem negativnih primjera
Author	Petra Bevandić
Mentor	Siniša Šegvić (mentor)
Committee member	Zoran Kalafatić (predsjednik povjerenstva)
Committee member	Marko Subašić (član povjerenstva)
Granter	University of Zagreb Faculty of Electrical Engineering and Computing Zagreb
Defense date and country	2023-07-12, Croatia
Scientific / art field, discipline and subdiscipline	TECHNICAL SCIENCES Computing
Universal decimal classification (UDC)	004 - Computer science and technology. Computing. Data processing
Abstract	Open-set recognition simulates real-world settings by introducing outlier samples into model evaluation. This problem is more complex in the dense prediction setting because input can contain a mix of in-distribution and out-of-distribution segments. Open-set recognition tasks can be approached through simultaneous classification and anomaly detection. This work investigates methods for open-set prediction which are trained by means of negative examples. We assume that we train domain-specific models and can thus sample negatives from large and diverse datasets (e.g. ImageNet). These types of negatives introduce additional noise into training because they may also contain some positive content. This thesis will investigate various facets of training on noisy negative samples: from choosing the best architecture and loss to different methods of combining positive and negative samples during batch formation. The presented experiments validate our contributions on standard open-set recognition benchmarks.
Abstract (croatian)	Prepoznavanje nad otvorenim skupom razreda simulira rad modela u stvarnim uvjetima uvođenjem izvandistribucijskih primjera u evaluaciju modela. U gustoj predikciji postoji i dodatna razina složenosti jer ulazni primjeri mogu biti mješavina unutardistribucijskih i izvandistribucijskih segmenata. Problem prepoznavanja nad otvorenim skupom razreda možemo rješavati modelima koji istovremeno provode klasifikaciju i detekciju anomalija. U ovome ćemo radu istražiti metode za gustu predikciju nad otvorenim skupom razreda uključivanjem negativnih primjera u postupak učenja. Pretpostavljamo da su modeli koje učimo specijalizirani zbog čega se kao negativi mogu koristiti primjeri iz velikih, raznolikih skupova slika (npr. ImageNet). Takvi negativi unose šum u treniranje s obzirom da mogu sadržavati i unutardistribucijske uzorke. Disertacija će proučiti postupak uključivanja šumovitih negativa u postupak treniranja - od odabira arhitekture modela i gubitka do kombiniranja pozitivnih i negativnih primjera prilikom učenja. Doprinosi će biti vrednovani na standardnim skupovima za validaciju guste detekcije izvandistribucijskih primjera.
Keywords
Keywords (croatian)
Language	english
URN:NBN	urn:nbn:hr:168:468120
Promotion	2024
Study programme	Title: Computer Science Study programme type: university Study level: postgraduate Academic / professional title: Doktor znanosti (Doktor znanosti)
Type of resource	Text
Extent	xi, 82 str. : graf. prikazi.
File origin	Born digital
Access conditions	Open access
Terms of use
Repository	FER Repository
Created on	2024-02-21 14:51:28