  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 end, we propose a novel NoIsy
label CorrEction and Sample Training strategy for SGG: NICEST. Specifically, it
consists of two parts: NICE and NIST, which rule out these noisy label issues
by generating high-quality samples and the effective training strategy,
respectively. NICE first detects noisy samples and then reassigns them more
high-quality soft predicate labels. NIST is a multi-teacher knowledge
distillation based training strategy, which enables the model to learn unbiased
fusion knowledge. And a dynamic trade-off weighting strategy in NIST is
designed to penalize the bias of different teachers. Due to the model-agnostic
nature of both NICE and NIST, our NICEST can be seamlessly incorporated into
any SGG architecture to boost its performance on different predicate
categories. In addition, to better evaluate the generalization of SGG models,
we further propose a new benchmark VG-OOD, by re-organizing the prevalent VG
dataset and deliberately making the predicate distributions of the training and
test sets as different as possible for each subject-object category pair. This
new benchmark helps disentangle the influence of subject-object category based
frequency biases. Extensive ablations and results on different backbones and
tasks have attested to the effectiveness and generalization ability of each
component of NICEST.
