We propose an iterative clustering-based label super-resolution approach and epitome-based approach to weakly supervised semantic segmentation, as well as a deep learning-based postprocessing step for land cover segmentation. An ensemble of the iterative clustering and epitome approaches with the proposed postprocessing step results in a top validation leaderboard average accuracy of 70.43%. A similar ensemble, that also considers class accuracy feedback from the leaderboard, achieves a top Track 1 leaderboard average accuracy of 57.49%.<\/p>\n","protected":false},"excerpt":{"rendered":"
We propose an iterative clustering-based label super-resolution approach and epitome-based approach to weakly supervised semantic segmentation, as well as a deep learning-based postprocessing step for land cover segmentation. An ensemble of the iterative clustering and epitome approaches with the proposed postprocessing step results in a top validation leaderboard average accuracy of 70.43%. 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