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SOTA on PASCAL Context on 2021-06-14


This question is part of the Maximum Likelihood Round of the Forecasting AI Progress Tournament. You can view all other questions in this round here.

Semantic segmentation, or image segmentation, is the task of clustering parts of an image together which belong to the same object class. It is a form of pixel-level prediction because each pixel in an image is classified according to a category (Thoma, 2016).

The PASCAL-Context dataset is a challenging scene parsing dataset that contains 59 semantic classes and 1 background class (Mottaghi et al., 2014). The training set and test set consist of 4, 998 and 5,105 images respectively.

As of writing this question, the state-of-the-art model for semantic segmentation on PASCAL-Context is ResNeSt-269 (Zhang et al., 2020), which achieves 58.92 mIoU% (see their detailed results here).

An excellent reference for tracking state-of-the-art models is PapersWithCode, which tracks performance data of ML models.

What will the state-of-the-art performance on semantic segmentation of PASCAL-Context be at 2021-06-14 in mean IoU in percent (MIoU%)?

This question resolves as the highest level of performance (in MIoU%) achieved on the PASCAL-Context (2014) dataset up until 2021-06-14, 11:59PM GMT amongst models trained on only the PASCAL-Context training set—no extra training data may be used. The model's level of performance is to be evaluated on the PASCAL-Context test set.

Performance figures may be taken from e-prints, conference papers, peer-reviewed articles, and blog articles by reputable AI labs (including the associated code repositories). Published performance figures must be available before 2021-06-14, 11:59PM GMT to qualify.

In case the relevant performance figure is given as a confidence interval, the median value will be used to resolve the question.

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