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The current studies of Scene Graph Generation (SGG) focus on solving thelong-tailed problem for generating unbiased scene graphs. However, mostde-biasing methods overemphasize the tail predicates and underestimate headones throughout training, thereby wrecking the representation ability of headpredicate features. Furthermore, these impaired features from head predicatesharm the learning of tail predicates. In fact, the inference of tail predicatesheavily depends on the general patterns learned from head ones, e.g., "standingon" depends on "on". Thus, these de-biasing SGG methods can neither achieveexcellent 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 aCoarse-grained Learning Branch (CLB) and a Fine-grained Learning Branch (FLB).Specifically, the CLB is responsible for learning expertise and robust featuresof head predicates, while the FLB is expected to predict informative tailpredicates. Furthermore, DHL is equipped with a Branch Curriculum Schedule(BCS) to make the two branches work well together. Experiments show that ourapproach achieves a new state-of-the-art performance on VG and GQA datasets andmakes a trade-off between the performance of tail predicates and head ones.Moreover, extensive experiments on two downstream tasks (i.e., Image Captioningand Sentence-to-Graph Retrieval) further verify the generalization andpracticability of our method.

Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation