  Scene Graph Generation (SGG) as a critical task in image understanding,
facing the challenge of head-biased prediction caused by the long-tail
distribution of predicates. However, current unbiased SGG methods can easily
prioritize improving the prediction of tail predicates while ignoring the
substantial sacrifice in the prediction of head predicates, leading to a shift
from head bias to tail bias. To address this issue, we propose a model-agnostic
Head-Tail Collaborative Learning (HTCL) network that includes head-prefer and
tail-prefer feature representation branches that collaborate to achieve
accurate recognition of both head and tail predicates. We also propose a
self-supervised learning approach to enhance the prediction ability of the
tail-prefer feature representation branch by constraining tail-prefer predicate
features. Specifically, self-supervised learning converges head predicate
features to their class centers while dispersing tail predicate features as
much as possible through contrast learning and head center loss. We demonstrate
the effectiveness of our HTCL by applying it to various SGG models on VG150,
Open Images V6 and GQA200 datasets. The results show that our method achieves
higher mean Recall with a minimal sacrifice in Recall and achieves a new
state-of-the-art overall performance. Our code is available at
https://github.com/wanglei0618/HTCL.
