graph-rcnn.pytorch

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

Introduction

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
Graphical Contrastive Losses for Scene Graph Generation, Zhang et al, CVPR 2019

Our reimplementations are based on the following repositories:

Why we need this repository?

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!