Naslov
Memory-efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment

Autori
Markuš, Nenad ; Gogić, Ivan ; Pandžić, Igor Sunday ; Ahlberg, Jörgen

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of British Machine Vision Conference BMVC 2018 / - , 2018, 1-11

Skup
British Machine Vision Conference BMVC

Mjesto i datum
Newcastle upon Tyne, Ujedinjeno Kraljevstvo, 03.09.2018. - 06.09.2018

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
decision tree

Sažetak
Ren et al. recently introduced a method for aggregating multiple decision trees into a strong predictor by interpreting a path taken by a sample down each tree as a binary vector and performing linear regression on top of these vectors stacked together. They provided experimental evidence that the method offers advantages over the usual approaches for combining decision trees (random forests and boosting). The method truly shines when the regression target is a large vector with correlated dimensions, such as a 2D face shape represented with the positions of several facial landmarks. However, we argue that their basic method is not applicable in many practical scenarios due to large memory requirements. This paper shows how this issue can be solved through the use of quantization and architectural changes of the predictor that maps decision tree-derived encodings to the desired output.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA