论文类型：论文集
第一作者：Youming Deng
合写作者：Yansheng Li,Yongjun Zhang,Xiang Xiang,Jian Wang,Jingdong Chen,Jiayi Ma
发表刊物：Proceedings of European Conference on Computer Vision (ECCV) 2022
收录刊物：EI
刊物所在地：德国
学科门类：工学
一级学科：控制科学与工程
文献类型：C
发表时间：2022-07-06
摘要：As far as Scene Graph Generation (SGG), coarse and fine predicates mix in the dataset due to the crowd-sourced labeling, and the long-tail problem is also pronounced. Given this tricky situation, many existing SGG methods treat the predicates equally and learn the model under the supervision of mixed-granularity predicates in one stage, leading to relatively coarse predictions. In order to alleviate the negative impact of the suboptimum mixed-granularity annotation and long-tail effect problems, this paper proposes a novel Hierarchical Memory Learning (HML) framework to learn the model from simple to complex, which is similar to the human beings' hierarchical memory learning process. After the autonomous partition of coarse and fine predicates, the model is first trained on the coarse predicates and then learns the fine predicates. In order to realize this hierarchical learning pattern, this paper, for the first time, formulates the HML framework using the new Concept Reconstruction (CR) and Model Reconstruction (MR) constraints. It is worth noticing that the HML framework can be taken as one general optimization strategy to improve various SGG models, and significant improvement can be achieved on the SGG benchmark (i.e., Visual Genome).