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

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.Comment: BMVC Newcastle 201

Centar izvrsnosti za računalni vid

Centar izvrsnosti za računalni vid na Fakultetu elektrotehnike i računarstva (FER) osnovan je 2012. godine sa ciljem okupljanja relevantnih istraživača s FER-a i s drugih sastavnica Sveučilišta. U osnivanju Centra sudjelovalo je sedam sastavnica Sveučilišta u Zagrebu. Ciljevi Centra su jačanje međunarodne vidljivosti Sveučilišta u Zagrebu u području računalnog vida, stvaranje kritične mase istraživača za zajednički nastup u većim znanstvenoistraživačkim i razvojnim projektima, poboljšanje kvalitete doktorskih istraživanja u području računalnog vida i poticanje zajedničkog nastupa prema gospodarstvu radi suradnje i transfera tehnologije. U radu je predstavljena motivacija za osnivanje Centra te znanstvene i stručne aktivnosti članova Centra

Picking out the bad apples : unsupervised biometric data filtering for refined age estimation

Introduction of large training datasets was essential for the recent advancement and success of deep learning methods. Due to the difficulties related to biometric data collection, facial image datasets with biometric trait labels are scarce and usually limited in terms of size and sample diversity. Web-scraping approaches for automatic data collection can produce large amounts of weakly labeled and noisy data. This work is focused on picking out the bad apples from web-scraped facial datasets by automatically removing erroneous samples that impair their usability. The unsupervised facial biometric data filtering method presented in this work greatly reduces label noise levels in web-scraped facial biometric data. Experiments on two large state-of-the-art web-scraped datasets demonstrate the effectiveness of the proposed method with respect to real and apparent age estimation based on five different age estimation methods. Furthermore, we apply the proposed method, together with a newly devised strategy for merging multiple datasets, to data collected from three major web-based data sources (i.e., IMDb, Wikipedia, Google) and derive the new Biometrically Filtered Famous Figure Dataset or B3FD. The proposed dataset, which is made publicly available, enables considerable performance gains for all tested age estimation methods and age estimation tasks. This work highlights the importance of training data quality compared to data quantity and selection of the estimation method.Funding: The author K. Besenic receives Ph.D. scholarship from the company Visage Technologies.</p

Centar izvrsnosti za računalni vid

Centar izvrsnosti za računalni vid na Fakultetu elektrotehnike i računarstva (FER) osnovan je 2012. godine sa ciljem okupljanja relevantnih istraživača s FER-a i s drugih sastavnica Sveučilišta. U osnivanju Centra sudjelovalo je sedam sastavnica Sveučilišta u Zagrebu. Ciljevi Centra su jačanje međunarodne vidljivosti Sveučilišta u Zagrebu u području računalnog vida, stvaranje kritične mase istraživača za zajednički nastup u većim znanstvenoistraživačkim i razvojnim projektima, poboljšanje kvalitete doktorskih istraživanja u području računalnog vida i poticanje zajedničkog nastupa prema gospodarstvu radi suradnje i transfera tehnologije. U radu je predstavljena motivacija za osnivanje Centra te znanstvene i stručne aktivnosti članova Centra