  Scene graph generation (SGG) has gained tremendous progress in recent years.
However, its underlying long-tailed distribution of predicate classes is a
challenging problem. For extremely unbalanced predicate distributions, existing
approaches usually construct complicated context encoders to extract the
intrinsic relevance of scene context to predicates and complex networks to
improve the learning ability of network models for highly imbalanced predicate
distributions. To address the unbiased SGG problem, we introduce a simple yet
effective method dubbed Context-Aware Mixture-of-Experts (CAME) to improve
model diversity and mitigate biased SGG without complicated design.
Specifically, we propose to integrate the mixture of experts with a divide and
ensemble strategy to remedy the severely long-tailed distribution of predicate
classes, which is applicable to the majority of unbiased scene graph
generators. The biased SGG is thereby reduced, and the model tends to
anticipate more evenly distributed predicate predictions. To differentiate
between various predicate distribution levels, experts with the same weights
are not sufficiently diverse. In order to enable the network dynamically
exploit the rich scene context and further boost the diversity of model, we
simply use the built-in module to create a context encoder. The importance of
each expert to scene context and each predicate to each expert is dynamically
associated with expert weighting (EW) and predicate weighting (PW) strategy. We
have conducted extensive experiments on three tasks using the Visual Genome
dataset, showing that CAME outperforms recent methods and achieves
state-of-the-art performance. Our code will be available publicly.
