Papers › Scene Graph Generation by Iterative Message Passing
Scene Graph Generation by Iterative Message Passing
Danfei Xu, Yuke Zhu, Christopher B. Choy, Li Fei-Fei
Understanding a visual scene goes beyond recognizing individual objects in isolation. Relationships between objects also constitute rich semantic information about the scene. In this work, we explicitly model the objects and their relationships using scene graphs, a visually-grounded graphical structure of an image. We propose a novel end-to-end model that generates such structured scene representation from an input image. The model solves the scene graph inference problem using standard RNNs and learns to iteratively improves its predictions via message passing. Our joint inference model can take advantage of contextual cues to make better predictions on objects and their relationships. The experiments show that our model significantly outperforms previous methods for generating scene graphs using Visual Genome dataset and inferring support relations with NYU Depth v2 dataset.
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Code
Syntology Ran 3 of 5 code samples harvested from 2 repositories linked to this paper; 2 have no recorded run. Of those that ran: 2 ran · our draft was wrong; 1 ran · fixture could not drive it.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Panoptic Scene Graph Generation | PSG Dataset | IMP | R@20 | 16.5 | #9 of 9 | Archive leaderboard | report |
| Panoptic Scene Graph Generation | PSG Dataset | IMP | mR@20 | 6.52 | #9 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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