{"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2010.13118"}],"language":[{"iso":"eng"}],"publication":"arXiv:2010.13118","type":"preprint","date_updated":"2022-01-06T06:54:23Z","year":"2020","status":"public","abstract":[{"text":"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.","lang":"eng"}],"oa":"1","author":[{"last_name":"Lienen","id":"44040","full_name":"Lienen, Julian","first_name":"Julian"},{"full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier","first_name":"Eyke"}],"_id":"20211","title":"Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model","date_created":"2020-10-27T07:48:40Z","citation":{"ama":"Lienen J, Hüllermeier E. Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model. <i>arXiv:201013118</i>. 2020.","mla":"Lienen, Julian, and Eyke Hüllermeier. “Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce  Model.” <i>ArXiv:2010.13118</i>, 2020.","ieee":"J. Lienen and E. Hüllermeier, “Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model,” <i>arXiv:2010.13118</i>. 2020.","apa":"Lienen, J., &#38; Hüllermeier, E. (2020). Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model. <i>ArXiv:2010.13118</i>.","bibtex":"@article{Lienen_Hüllermeier_2020, title={Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model}, journal={arXiv:2010.13118}, author={Lienen, Julian and Hüllermeier, Eyke}, year={2020} }","short":"J. Lienen, E. Hüllermeier, ArXiv:2010.13118 (2020).","chicago":"Lienen, Julian, and Eyke Hüllermeier. “Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce  Model.” <i>ArXiv:2010.13118</i>, 2020."},"user_id":"44040"}