  Panoptic Scene Graph Generation (PSG) involves the detection of objects and
the prediction of their corresponding relationships (predicates). However, the
presence of biased predicate annotations poses a significant challenge for PSG
models, as it hinders their ability to establish a clear decision boundary
among different predicates. This issue substantially impedes the practical
utility and real-world applicability of PSG models. To address the intrinsic
bias above, we propose a novel framework to infer potentially biased
annotations by measuring the predicate prediction risks within each
subject-object pair (domain), and adaptively transfer the biased annotations to
consistent ones by learning invariant predicate representation embeddings.
Experiments show that our method significantly improves the performance of
benchmark models, achieving a new state-of-the-art performance, and shows great
generalization and effectiveness on PSG dataset.

Computer Vision and Pattern Recognition