A reinforced self-evolving framework (L2L) for semi-supervised referring expression segmentation that jointly optimizes the segmentation model and pseudo-labels using multimodal priors and adaptive selection.
When constructing the foreground/background/ignored regions, we suppress ambiguous boundary supervision by marking a 3-pixel-wide band around region boundaries as ignored
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Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation
A reinforced self-evolving framework (L2L) for semi-supervised referring expression segmentation that jointly optimizes the segmentation model and pseudo-labels using multimodal priors and adaptive selection.