NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation

arXiv:2207.13316v2 Announce Type: replace
Abstract: Nearly all existing scene graph generation (SGG) models have overlooked the ground-truth annotation qualities of mainstream SGG datasets, i.e., they assume: 1) all the manually annotated positive samples are equally correct; 2) all the un-annotated negative samples are absolutely background. In this paper, we argue that neither of the assumptions applies to SGG: there are numerous noisy ground-truth predicate labels that break these two assumptions and harm the training of unbiased SGG models. To this …

abstract annotation arxiv cs.cv datasets graph ground-truth negative paper positive robust samples training truth type