Pascal Visual Object Classes Challenge Results

By Mark Everingham, Luc Van, G.Hannah Chris, Andrew Williams, Zisserman, Ilkay Ulusoy, MPITuebingen, J. Eichhorn, Olivier Chapelle, Jason D. R. Farquhar and 3 more. 2005

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

1. Luc Van Gool
2. Xiaogang Wang
3. Trevor Darrell
4. Ming-Hsuan Yang
5. Dacheng Tao
6. Jitendra Malik
7. Alan L. Yuille
8. Yi Yang

In Evaluation & Benchmarks

1. Vittorio Ferrari
2. Jitendra Malik
3. Sanja Fidler
4. Jasper R. R. Uijlings
5. Ming-Hsuan Yang
6. Zhuowen Tu
7. Ali Farhadi
8. Michael Gygli

Citations in the corpus

• Unmasking Clever Hans predictors and assessing what machines really learn (2019, methodology): “We therefore choose to report our results in Average Precision [151] (AP), which is in use for model evaluation in the Pascal VOC challenge since 2007 [152] and yields more representative measures in highly class-imbalanced object retrieval settings compared to accuracy.”
• MAST: A Memory-Augmented Self-Supervised Tracker (2020, background): “For instance, an ImageNet [10] pre-trained ResNet [18] is typically adopted as a feature encoder, and further fine-tuned on images or video frames annotated with fine-grained, pixelwise segmentation masks, e.g. COCO [40], Pascal [13], DAVIS [48] and YouTube-VOS [71].”