Weakly Supervised Semantic Segmentation in the 2020 IEEE GRSS Data Fusion Contest - Robinson, Caleb; Malkin, Kolya; Hu, Lucas; Dilkina, Bistra; Jojic, Nebojsa (IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2020)
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%.