PASCAL Context

Introduced by Mottaghi et al. in The Role of Context for Object Detection and Semantic Segmentation in the Wild

The PASCAL Context dataset is an extension of the PASCAL VOC 2010 detection challenge, and it contains pixel-wise labels for all training images. It contains more than 400 classes (including the original 20 classes plus backgrounds from PASCAL VOC segmentation), divided into three categories (objects, stuff, and hybrids). Many of the object categories of this dataset are too sparse and; therefore, a subset of 59 frequent classes are usually selected for use.

Source: Image Segmentation Using Deep Learning:A Survey

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Benchmarks

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Task	Dataset Variant	Best Model
Semantic Segmentation	PASCAL Context	PlainSeg
Zero-Shot Learning	PASCAL Context	ZS3Net
Human Parsing	PASCAL Context	InvPT
Saliency Detection	PASCAL Context	InvPT
Surface Normals Estimation	PASCAL Context	InvPT
Boundary Detection	PASCAL Context	InvPT