Different objects in the same scene are more or less related to each other, but only a limited number of these relationships are noteworthy. Inspired by Detection Transformer, which excels in object detection, we view scene graph generation as a set prediction problem. In this article, we propose an end-to-end scene graph generation model Relation Transformer (RelTR), which has an encoder-decoder architecture. The encoder reasons about the visual feature context while the decoder infers a fixed-size set of triplets subject-predicate-object using different types of attention mechanisms with coupled subject and object queries. We design a set prediction loss performing the matching between the ground truth and predicted triplets for the end-to-end training. In contrast to most existing scene graph generation methods, RelTR is a one-stage method that predicts sparse scene graphs directly only using visual appearance without combining entities and labeling all possible predicates. Extensive experiments on the Visual Genome, Open Images V6, and VRD datasets demonstrate the superior performance and fast inference of our model.

Original languageEnglish
Pages (from-to)11169-11183
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number9
Early online date19 Apr 2023
Publication statusPublished - 1 Sept 2023


  • One-stage
  • scene graph generation
  • scene understanding
  • visual relationship detection

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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