---
res:
bibo_abstract:
- "In many real-world applications, the relative depth of objects in an image is\r\ncrucial
for scene understanding, e.g., to calculate occlusions in augmented\r\nreality
scenes. Predicting depth in monocular images has recently been tackled\r\nusing
machine learning methods, mainly by treating the problem as a regression\r\ntask.
Yet, being interested in an order relation in the first place,\r\nranking methods
suggest themselves as a natural alternative to regression, and\r\nindeed, ranking
approaches leveraging pairwise comparisons as training\r\ninformation (\"object
A is closer to the camera than B\") have shown promising\r\nperformance on this
problem. In this paper, we elaborate on the use of\r\nso-called \\emph{listwise}
ranking as a generalization of the pairwise approach.\r\nListwise ranking goes
beyond pairwise comparisons between objects and considers\r\nrankings of arbitrary
length as training information. Our approach is based on\r\nthe Plackett-Luce
model, a probability distribution on rankings, which we\r\ncombine with a state-of-the-art
neural network architecture and a sampling\r\nstrategy to reduce training complexity.
An empirical evaluation on benchmark\r\ndata in a \"zero-shot\" setting demonstrates
the effectiveness of our proposal\r\ncompared to existing ranking and regression
methods.@eng"
bibo_authorlist:
- foaf_Person:
foaf_givenName: Julian
foaf_name: Lienen, Julian
foaf_surname: Lienen
foaf_workInfoHomepage: http://www.librecat.org/personId=44040
- foaf_Person:
foaf_givenName: Eyke
foaf_name: HÃ¼llermeier, Eyke
foaf_surname: HÃ¼llermeier
foaf_workInfoHomepage: http://www.librecat.org/personId=48129
dct_date: 2020^xs_gYear
dct_language: eng
dct_title: Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce model@
...