You're currently viewing an old version of this dataset. To see the current version, click here.

NYUv2

Multi-task learning (MTL) research is broadly divided into two categories: one is to learn the correlation between tasks through model structures, and the other is to balance the joint training process of all tasks through optimization algorithms.

Data and Resources

Cite this as

Jiaming Liu, Qizhe Zhang, Jianing Li, Ming Lu, Tiejun Huang, Shanghang Zhang (2024). Dataset: NYUv2. https://doi.org/10.57702/7osc5sda

DOI retrieved: December 2, 2024

Additional Info

Field Value
Created December 2, 2024
Last update December 2, 2024
Defined In https://doi.org/10.48550/arXiv.2007.05441
Citation
  • https://doi.org/10.48550/arXiv.1901.10034
  • https://doi.org/10.48550/arXiv.2307.15429
  • https://doi.org/10.48550/arXiv.2007.13727
  • https://doi.org/10.48550/arXiv.2306.05242
  • https://doi.org/10.48550/arXiv.2009.09976
  • https://doi.org/10.48550/arXiv.2211.14037
  • https://doi.org/10.48550/arXiv.2002.12114
Author Jiaming Liu
More Authors
Qizhe Zhang
Jianing Li
Ming Lu
Tiejun Huang
Shanghang Zhang
Homepage https://www.cs.nyu.edu/~ylclab/research/datasets/nyu-depth-v2.html