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| Hauptseite > Publikationsdatenbank > Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks > print |
| Hauptseite > Publikationsdatenbank > Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks > print |
| 001 | 1033882 | ||
| 005 | 20251024202101.0 | ||
| 037 | _ | _ | |a FZJ-2024-06720 |
| 100 | 1 | _ | |a Quercia, Alessio |0 P:(DE-Juel1)188471 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a IEEE/CVF Winter Conference on Applications of Computer Vision |g WACV |c Tucson |d 2025-02-28 - 2025-03-04 |w USA |
| 245 | _ | _ | |a Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks |
| 260 | _ | _ | |c 2025 |
| 300 | _ | _ | |a 8 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1761306747_21804 |2 PUB:(DE-HGF) |
| 520 | _ | _ | |a Monocular depth estimation (MDE) is a challenging task in computer vision, often hindered by the cost and scarcity of high-quality labeled datasets. We tackle this challenge using auxiliary datasets from related vision tasks for an alternating training scheme with a shared decoder built on top of a pre-trained vision foundation model, while giving a higher weight to MDE. Through extensive experiments we demonstrate the benefits of incorporating various in-domain auxiliary datasets and tasks to improve MDE quality on average by ~11%. Our experimental analysis shows that auxiliary tasks have different impacts, confirming the importance of task selection, highlighting that quality gains are not achieved by merely adding data. Remarkably, our study reveals that using semantic segmentation datasets as Multi-Label Dense Classification (MLDC) often results in additional quality gains. Lastly, our method significantly improves the data efficiency for the considered MDE datasets, enhancing their quality while reducing their size by at least 80%. This paves the way for using auxiliary data from related tasks to improve MDE quality despite limited availability of high-quality labeled data. Code is available at https://jugit.fz-juelich.de/ias-8/mdeaux. |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
| 700 | 1 | _ | |a Yildiz, Erenus |0 P:(DE-Juel1)191034 |b 1 |u fzj |
| 700 | 1 | _ | |a Cao, Zhuo |0 P:(DE-Juel1)199019 |b 2 |u fzj |
| 700 | 1 | _ | |a Krajsek, Kai |0 P:(DE-Juel1)129347 |b 3 |u fzj |
| 700 | 1 | _ | |a Morrison, Abigail |0 P:(DE-Juel1)151166 |b 4 |u fzj |
| 700 | 1 | _ | |a Assent, Ira |0 P:(DE-Juel1)188313 |b 5 |u fzj |
| 700 | 1 | _ | |a Scharr, Hanno |0 P:(DE-Juel1)129394 |b 6 |u fzj |
| 909 | C | O | |o oai:juser.fz-juelich.de:1033882 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)188471 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)191034 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)199019 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)129347 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)151166 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)188313 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)129394 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
| 914 | 1 | _ | |y 2025 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-8-20210421 |k IAS-8 |l Datenanalyse und Maschinenlernen |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Computational and Systems Neuroscience |x 1 |
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| 980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 980 | _ | _ | |a UNRESTRICTED |
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