The PASCAL visual object classes challenge 2006 (VOC2006) results
By Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc Van Gool. 2006
208 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, 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 Computer Vision and Pattern Recognition
- Luc Van Gool
- Jitendra Malik
- Andrew Zisserman
- Andrea Vedaldi
- Trevor Darrell
- Dacheng Tao
- Ming-Hsuan Yang
- Xiaogang Wang
In Evaluation & Benchmarks
- Antonio Torralba
- Deva Ramanan
- Martial Hebert
- Ross B. Girshick
- Rob Fergus
- William T. Freeman
- James Hays
- Julien Mairal
Citations in the corpus
- Discriminative Subsequence Mining for Action Classification (2007, methodology): “In current computer vision research, it is common to represent each data instance (i.e., video or image) as a histogram of visual words, see for example the recent VOC2006 object classification challenge [ 6 ].”
- Spatial pyramid matching (2009, unknown): “Besides Caltech-101, another major benchmark in the recognition com-14 munity is the PASCAL Visual Object Classes Challenge (Everingham et al., 2006).”
- Using Segmentation to Verify Object Hypotheses (2007, methodology): “We show that such a strategy leads to stateof-the-art performance (10-20% improvement over existing systems) across a number of difficult datasets, such as the Pascal VOC2006 challenge [4], the INRIAPerson database [3], and LabelMe [14].”