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Weakly Supervised Semantic Segmentation via Adversarial Learning of Classifier and Reconstructor

CVPR 2023 · Detection and segmentation › Weakly supervised detection and segmentation · approach: CAM refinement and prototypes · 88 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
adversarial training of classifier and reconstructor instead of frozen pre-trained reconstructor (ablation)CAM classifier with reconstructor · PASCAL VOC 2012 trainmIoU (%): +2.9 (52.9 to 55.8)Helped
adding Stochastic Remnant Feeding (SRF) to the reconstructor (ablation)adversarial and pre-trained reconstructor settings · PASCAL VOC 2012 trainmIoU (%): +4.5 (55.8 to 60.3) adversarial; +1.7 (52.9 to 54.6) pre-trainedHelped
adding the reconstruction-based target and non-target losses to the classifier loss (ablation)CAM classifier · PASCAL VOC 2012 trainmIoU (%): +11.9 (48.4 to 60.3) over classification-only baselineHelped
removing non-target loss term from classifier objective (ablation)CAM classifier · PASCAL VOC 2012 trainmIoU (%): -6.9 (60.3 to 53.4)Hurt
removing target loss term from classifier objective (ablation)CAM classifier · PASCAL VOC 2012 trainmIoU (%): -8.8 (60.3 to 51.5)Hurt
adversarial classifier-reconstructor framework (ACR) vs AE-based AEFTWSSS pipeline with WRN38 · PASCAL VOC 2012 valmIoU (%): +1.0 (70.9 to 71.9)Helped
ACR with ViT-based CAM refinementACR pipeline · PASCAL VOC 2012 valmIoU (%): +0.5 (71.9 to 72.4)Helped

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