The Pascal Visual Object Classes Challenge 2006 ( VOC 2006 ) Results
By Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc Van Gool. 2006
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
- Dacheng Tao
- Xiaogang Wang
- Ming-Hsuan Yang
- Trevor Darrell
- Jitendra Malik
- Andrea Vedaldi
- Andrew Zisserman
In Evaluation & Benchmarks
- Jitendra Malik
- Antonio Torralba
- Trevor Darrell
- Luc Van Gool
- Li Fei-Fei
- Alexei A. Efros
- Kristen Grauman
- Bernt Schiele
Citations in the corpus
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015, methodology): “We evaluate our method on the PASCAL VOC detection benchmarks [4], where RPNs with Fast R-CNNs produce detection accuracy better than the strong baseline of Selective Search with Fast R-CNNs.”
- Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model (2015, neutral): “Both of them were trained on PASCAL VOC2007 [6] trainval set and tested on the test set of the same challenge.”
- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition (2014, methodology): “The classification task in Pascal VOC 2007 [22] involves 9,963 images in 20 categories.”
- Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics (2013, methodology): “We use 128 color words, 256 texture words and 256 SIFT words, obtained in an unsupervised fashion by K-means clustering on 1,000 points of 200 images from the PASCAL 2008 data set (Everingham et al., 2008).”
- Colorful Image Colorization (2016, methodology): “Task and Dataset Generalization on PASCAL Classification and detection on PASCAL VOC 2007 [39] and segmentation on PASCAL VOC 2012 [40], using standard mean average precision (mAP) and mean intersection over union (mIU) metrics for each task.”
- Objects as Points (2019, methodology): “The supplement contains additional experiments on PascalVOC [14].”
- Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction (2016, methodology): “Our proposed methods outperform these alternatives, which indicates that (i) our choice of aggregating complementary signals improves performance (ii) concatenation is an appropriate choice of combining cross-channel encoders.”
- Getting to 99% Accuracy in Interactive Segmentation (2020, methodology): “The two most popular training sets used in interactive segmentation are PAS-CAL 2012 [5, 37] and SBD [41].”