Papers › Semantic Diversity-aware Prototype-based Learning for Unbiased Scene Graph Generation
Semantic Diversity-aware Prototype-based Learning for Unbiased Scene Graph Generation
Jaehyeong Jeon, Kibum Kim, Kanghoon Yoon, Chanyoung Park
The scene graph generation (SGG) task involves detecting objects within an image and predicting predicates that represent the relationships between the objects. However, in SGG benchmark datasets, each subject-object pair is annotated with a single predicate even though a single predicate may exhibit diverse semantics (i.e., semantic diversity), existing SGG models are trained to predict the one and only predicate for each pair. This in turn results in the SGG models to overlook the semantic diversity that may exist in a predicate, thus leading to biased predictions. In this paper, we propose a novel model-agnostic Semantic Diversity-aware Prototype-based Learning (DPL) framework that enables unbiased predictions based on the understanding of the semantic diversity of predicates. Specifically, DPL learns the regions in the semantic space covered by each predicate to distinguish among the various different semantics that a single predicate can represent. Extensive experiments demonstrate that our proposed model-agnostic DPL framework brings significant performance improvement on existing SGG models, and also effectively understands the semantic diversity of predicates.
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3acba312dea9c747 · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unbiased Scene Graph Generation | Visual Genome | DPL (MOTIFS-ResNeXt-101-FPN backbone; PredCls mode) | F@100 | 44.9 | #2 of 31 | Archive leaderboard | report |
| Unbiased Scene Graph Generation | Visual Genome | DPL (MOTIFS-ResNeXt-101-FPN backbone; PredCls mode) | mR@20 | 26.2 | #2 of 31 | Archive leaderboard | report |
| Unbiased Scene Graph Generation | Visual Genome | DPL (MOTIFS-ResNeXt-101-FPN backbone; PredCls mode) | ng-mR@20 | 31.3 | #2 of 31 | Archive leaderboard | report |
| Unbiased Scene Graph Generation | Visual Genome | DPL (MOTIFS-ResNeXt-101-FPN backbone; SGCls mode) | F@100 | 25.2 | #11 of 31 | Archive leaderboard | report |
| Unbiased Scene Graph Generation | Visual Genome | DPL (MOTIFS-ResNeXt-101-FPN backbone; SGCls mode) | mR@20 | 14.1 | #11 of 31 | Archive leaderboard | report |
| Unbiased Scene Graph Generation | Visual Genome | DPL (MOTIFS-ResNeXt-101-FPN backbone; SGCls mode) | ng-mR@20 | 18.5 | #11 of 31 | Archive leaderboard | report |
| Unbiased Scene Graph Generation | Visual Genome | DPL (MOTIFS-ResNeXt-101-FPN backbone; SGDet mode) | F@100 | 20.2 | #20 of 31 | Archive leaderboard | report |
| Unbiased Scene Graph Generation | Visual Genome | DPL (MOTIFS-ResNeXt-101-FPN backbone; SGDet mode) | mR@20 | 9.4 | #20 of 31 | Archive leaderboard | report |
| Unbiased Scene Graph Generation | Visual Genome | DPL (MOTIFS-ResNeXt-101-FPN backbone; SGDet mode) | ng-mR@20 | 10.0 | #20 of 31 | 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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