Pytorch code for our ECCV 2018 paper "Graph R-CNN for Scene Graph Generation"

This project is a set of reimplemented representative scene graph generation models based on Pytorch 1.0, including:

• Graph R-CNN for Scene Graph Generation, our own. ECCV 2018.
• Scene Graph Generation by Iterative Message Passing, Xu et al. CVPR 2017
• Scene Graph Generation from Objects, Phrases and Region Captions, Li et al. ICCV 2017
• Neural Motifs: Scene Graph Parsing with Global Context, Zellers et al. CVPR 2018

Our reimplementations are based on the following repositories:

• maskrcnn-benchmark
• faster-rcnn
• scene-graph-TF-release
• MSDN
• neural-motifs

The goal of gathering all these representative methods into a single repo is to establish a more fair comparison across different methods under the same settings. As you may notice in recent literatures, the reported numbers for IMP, MSDN, Graph R-CNN and Neural Motifs are usually confusing, especially due to the big gap between IMP style methods (first three) and Neural Motifs-style methods (neural motifs paper and other variants built on it). We hope this repo can establish a good benchmark for various scene graph generation methods, and contribute to the research community!

• Faster R-CNN Baseline (:balloon: 2019-07-04)
• Scene Graph Generation Baseline (:balloon: 2019-07-06)
• Iterative Message Passing (IMP) (:balloon: 2019-07-07)
• Multi-level Scene Description Network (MSDN)
• Neural Motif (Frequency Prior Baseline) (:balloon: 2019-07-08)
• Neural Motif
• Graph R-CNN