International Journal of Computer Vision manuscript No. (will be inserted by the editor) The PASCAL Visual Object Classes (VOC) Challenge

By Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John M. Winn, Andrew Zisserman. 2010

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Citations in the corpus

Object Detection Networks on Convolutional Feature Maps (2015, methodology): “Firstly we present carefully designed ablation experi142 ments on the PASCAL VOC dataset [26].”
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification (2015, methodology): “We evaluate these models on PASCAL VOC 2007 object detection [5].”
Very Deep Convolutional Networks for Large-Scale Image Recognition (2014, methodology): “We begin with the evaluation on the image classification task of PASCAL VOC-2007 and VOC-2012 benchma rks [6].”
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition (2014, methodology): “Next we describe our training solution that takes advantage of these GPU implementations while still preserving the spatial pyramid pooling behaviors.”
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation (2013, background): “First-layer filters can be visualized directly and are easy to understand [25].”
Aggregating Local Image Descriptors into Compact Codes (2012, methodology): “We also do not consider semantic queries such as “retrieve all images containing cars,” which are usually addressed by supervised classification techniques such as those evaluated in the PASCAL visual object recognition competition [5].”
Do Better ImageNet Models Transfer Better? (2018, background): “These datasets covered a wide range of image classification tasks, including superordinate-level object classification (CIFAR-10 [37], CIFAR-100 [37], PASCAL VOC 2007 [19], Caltech-101 [20]); fine-grained object classification (Food-101 [5], Birdsnap [4], Stanford Cars [36], FGVC Aircraft [48], Oxfo”
AI and the Everything in the Whole Wide World Benchmark (2021, background): “However, there is a notable difference between such competitions and those involving what are presented as “general” benchmarks — namely, the PASCAL VOC challenge running from 2005 to 2012, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) running annually from 2010 to 2017, and the GLU”
Self-Supervised MultiModal Versatile Networks (2020, methodology): “Finally, to evaluate how well our video representation transfers to image tasks we use the PASCAL VOC 2007 [21] and ImageNet [73] classification tasks.”
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (2020, neutral): “, SUN397 [79] or VOC2007 [80], and textures, e.”
VirTex: Learning Visual Representations from Textual Annotations (2020, methodology): “We evaluate features learned using VirTex on four downstream tasks with fine-tuning: (a) Instance Segmentation on COCO [30]; (b) Instance Segmentation on LVIS [31]; and (c) Object Detection on PASCAL VOC [99]; (d) Finegrained Classification on iNaturalist 2018 [107].”
Self-Supervised Learning of Visual Features through Embedding Images into Text Topic Spaces (2017, positive): “First we analyze the quality of TextTopicNet top layers features for image classification on the PASCAL VOC2007 dataset [10].”