Efficiently Scaling up Crowdsourced Video Annotation

By Carl Vondrick, Donald J. Patterson, Deva Ramanan. 2012

Subfields: Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Evaluation & Benchmarks

1. Vittorio Ferrari
2. Li Fei-Fei
3. Trevor Darrell
4. Antonio Torralba
5. Andrea Vedaldi
6. Alan L. Yuille
7. Abhinav Kumar Gupta
8. Kaiming He

Citations in the corpus

• ImageNet Large Scale Visual Recognition Challenge (2014, background): “(Vondrick et al., 2012) provides a detailed overview of crowdsourcing video annotation.”
• Extreme Clicking for Efficient Object Annotation (2017, background): “Quality control is a common process when crowd-sourcing image annotations [4, 36, 43, 53, 56, 60, 62, 66, 70].”
• SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels (2013, methodology): “Although there are several easy-to-use tools for annotating 2D images and videos [18, 26, 22], they would be suboptimal to use for RGB-D sequences because they do not exploit the 3D structure of the problem in label propagation.”
• Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection (2017, background): “Depending on the type of data, human annotations can be augmented with object tracking or 3D sensor information [1, 13, 24, 46, 52].”