Hippocampal Memory-Like Separation-Completion Collaborative Network for Unbiased Scene Graph Generation

Scene Graph Generation (SGG) is a challenging cross-modal task, which aims to identify entities and relationships in a scene simultaneously. Due to highly skewed long-tailed distribution, the generated scene graphs are dominated by relation categories of head samples. Current works address this problem by designing re-balancing strategies at the data level or refining relation representations at the feature level. Different from them, we attribute this impact to catastrophic interference, that is, the subsequent learning of dominant relations tends to overwrite the earlier learning of rare relations. To address it at the modeling level, a Hippocampal Memory-Like Separation-Completion Collaborative Network (HMSC²) is proposed here, which imitates the hippocampal encoding and retrieval process. Inspired by the pattern separation of dentate gyrus during memory encoding, a Gradient Separation Classifier and a Prototype Separation Learning module are proposed to relieve the catastrophic interference of tail categories by modeling the separated classifier and prototypes. In addition, inspired by the pattern completion of area CA3 of hippocampus during memory retrieval, a Prototype Completion Module is designed to supplement the incomplete information of prototypes by introducing relation representations as cues. Finally, the completed prototype and relation representations are connected within a hypersphere space by a Contrastive Connected Module. Experimental results on Visual Genome and GQA datasets show our HMSC² achieves state-of-the-art performance on the unbiased SGG task, effectively relieving the long-tailed problem. The source codes are released on GitHub: https://github.com/Nora-Zhang98/HMSC2.