Scene Graph Detection
8 papers with code • 3 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Scene Graph Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Visual Genome
VRD
ImageCLEF-DA
Home Action Genome
MOMA-LRG
Most implemented papers
Fine-Grained Scene Graph Generation with Data Transfer
waxnkw/ietrans-sgg.pytorch
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Scene graph generation (SGG) is designed to extract (subject, predicate, object) triplets in images.
Energy-Based Learning for Scene Graph Generation
mods333/energy-based-scene-graph
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CVPR 2021
The proposed formulation allows for efficiently incorporating the structure of scene graphs in the output space.
Recovering the Unbiased Scene Graphs from the Biased Ones
coldmanck/recovering-unbiased-scene-graphs
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Given input images, scene graph generation (SGG) aims to produce comprehensive, graphical representations describing visual relationships among salient objects.
Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs
Dynamic scene graph generation from a video is challenging due to the temporal dynamics of the scene and the inherent temporal fluctuations of predictions.
Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and Reasoning
jaleedkhan/neusire
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European Semantic Web Conference (ESWC) 2022
These results depict the effectiveness of commonsense knowledge infusion in improving the performance and expressiveness of scene graph generation for visual understanding and reasoning tasks.
NeuSyRE: Neuro-Symbolic Visual Understanding and Reasoning Framework based on Scene Graph Enrichment
jaleedkhan/neusire
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Semantic Web 2023
We present a loosely-coupled neuro-symbolic visual understanding and reasoning framework that employs a DNN-based pipeline for object detection and multi-modal pairwise relationship prediction for scene graph generation and leverages common sense knowledge in heterogenous knowledge graphs to enrich scene graphs for improved downstream reasoning.
Enhancing Scene Graph Generation with Hierarchical Relationships and Commonsense Knowledge
bowen-upenn/scene_graph_commonsense
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This work introduces an enhanced approach to generating scene graphs by incorporating both a relationship hierarchy and commonsense knowledge.
DSGG: Dense Relation Transformer for an End-to-end Scene Graph Generation
Scene graph generation aims to capture detailed spatial and semantic relationships between objects in an image, which is challenging due to incomplete labelling, long-tailed relationship categories, and relational semantic overlap.
