  The current studies of Scene Graph Generation (SGG) focus on solving the
long-tailed problem for generating unbiased scene graphs. However, most
de-biasing methods overemphasize the tail predicates and underestimate head
ones throughout training, thereby wrecking the representation ability of head
predicate features. Furthermore, these impaired features from head predicates
harm the learning of tail predicates. In fact, the inference of tail predicates
heavily depends on the general patterns learned from head ones, e.g., "standing
on" depends on "on". Thus, these de-biasing SGG methods can neither achieve
excellent performance on tail predicates nor satisfying behaviors on head ones.
To address this issue, we propose a Dual-branch Hybrid Learning network (DHL)
to take care of both head predicates and tail ones for SGG, including a
Coarse-grained Learning Branch (CLB) and a Fine-grained Learning Branch (FLB).
Specifically, the CLB is responsible for learning expertise and robust features
of head predicates, while the FLB is expected to predict informative tail
predicates. Furthermore, DHL is equipped with a Branch Curriculum Schedule
(BCS) to make the two branches work well together. Experiments show that our
approach achieves a new state-of-the-art performance on VG and GQA datasets and
makes a trade-off between the performance of tail predicates and head ones.
Moreover, extensive experiments on two downstream tasks (i.e., Image Captioning
and Sentence-to-Graph Retrieval) further verify the generalization and
practicability of our method.
