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Scene Graph Generation (SGG) represents objects and their interactions with agraph structure. Recently, many works are devoted to solving the imbalancedproblem in SGG. However, underestimating the head predicates in the wholetraining process, they wreck the features of head predicates that providegeneral features for tail ones. Besides, assigning excessive attention to thetail predicates leads to semantic deviation. Based on this, we propose a novelSGG framework, learning to generate scene graphs from Head to Tail (SGG-HT),containing Curriculum Re-weight Mechanism (CRM) and Semantic Context Module(SCM). CRM learns head/easy samples firstly for robust features of headpredicates and then gradually focuses on tail/hard ones. SCM is proposed torelieve semantic deviation by ensuring the semantic consistency between thegenerated scene graph and the ground truth in global and local representations.Experiments show that SGG-HT significantly alleviates the biased problem andchieves state-of-the-art performances on Visual Genome.

Learning To Generate Scene Graph from Head to Tail