Papers › CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation
CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation
Jing Yu, Yuan Chai, Yujing Wang, Yue Hu, Qi Wu
Scene graphs are semantic abstraction of images that encourage visual understanding and reasoning. However, the performance of Scene Graph Generation (SGG) is unsatisfactory when faced with biased data in real-world scenarios. Conventional debiasing research mainly studies from the view of balancing data distribution or learning unbiased models and representations, ignoring the correlations among the biased classes. In this work, we analyze this problem from a novel cognition perspective: automatically building a hierarchical cognitive structure from the biased predictions and navigating that hierarchy to locate the relationships, making the tail relationships receive more attention in a coarse-to-fine mode. To this end, we propose a novel debiasing Cognition Tree (CogTree) loss for unbiased SGG. We first build a cognitive structure CogTree to organize the relationships based on the prediction of a biased SGG model. The CogTree distinguishes remarkably different relationships at first and then focuses on a small portion of easily confused ones. Then, we propose a debiasing loss specially for this cognitive structure, which supports coarse-to-fine distinction for the correct relationships. The loss is model-agnostic and consistently boosting the performance of several state-of-the-art models. The code is available at: https://github.com/CYVincent/Scene-Graph-Transformer-CogTree.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Scene Graph Generation | Visual Genome | CogTree | mean Recall @20 | 7.9 | #16 of 19 | Archive leaderboard | report |
| Unbiased Scene Graph Generation | Visual Genome | CogTree (VCTree-ResNeXt-101-FPN backbone; PredCls mode) | F@100 | 35.9 | #26 of 31 | Archive leaderboard | report |
| Unbiased Scene Graph Generation | Visual Genome | CogTree (VCTree-ResNeXt-101-FPN backbone; PredCls mode) | mR@20 | 22.0 | #26 of 31 | Archive leaderboard | report |
| Unbiased Scene Graph Generation | Visual Genome | CogTree (MOTIFS-ResNeXt-101-FPN backbone; PredCls mode) | F@100 | 32.4 | #27 of 31 | Archive leaderboard | report |
| Unbiased Scene Graph Generation | Visual Genome | CogTree (MOTIFS-ResNeXt-101-FPN backbone; PredCls mode) | mR@20 | 20.9 | #27 of 31 | Archive leaderboard | report |
| Unbiased Scene Graph Generation | Visual Genome | CogTree (VCTree-ResNeXt-101-FPN backbone; SGCls mode) | mR@20 | 15.4 | #28 of 31 | Archive leaderboard | report |
| Unbiased Scene Graph Generation | Visual Genome | CogTree (MOTIFS-ResNeXt-101-FPN backbone; SGCls mode) | mR@20 | 12.1 | #29 of 31 | Archive leaderboard | report |
| Unbiased Scene Graph Generation | Visual Genome | CogTree (MOTIFS-ResNeXt-101-FPN backbone; SGDet mode) | mR@20 | 7.9 | #30 of 31 | Archive leaderboard | report |
| Unbiased Scene Graph Generation | Visual Genome | CogTree (VCTree-ResNeXt-101-FPN backbone; SGDet mode) | mR@20 | 7.8 | #31 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.
Methods
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