mlsanityData to 14 Oct 2026

Peer Learning for Unbiased Scene Graph Generation

arXiv 2301.00146 ↗First posted 31 Dec 2022cs.CVcited by 4

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Abstract (arXiv)

Unbiased scene graph generation (USGG) is a challenging task that requires predicting diverse and heavily imbalanced predicates between objects in an image. To address this, we propose a novel framework peer learning that uses predicate sampling and consensus voting (PSCV) to encourage multiple peers to learn from each other. Predicate sampling divides the predicate classes into sub-distributions based on frequency, and assigns different peers to handle each sub-distribution or combinations of them. Consensus voting ensembles the peers' complementary predicate knowledge by emphasizing majority opinion and diminishing minority opinion. Experiments on Visual Genome show that PSCV outperforms previous methods and achieves a new state-of-the-art on SGCls task with 31.6 mean.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 17 of its 21 references that are in the map. First posted on arXiv 31 Dec 2022.

Builds on

17 of 21 references.

References · 17

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  2. 2017Scene Graph Generation by Iterative Message Passingcited by 462
  3. 2017Neural Motifs: Scene Graph Parsing with Global Contextcited by 414
  4. 2020Unbiased Scene Graph Generation from Biased Trainingcited by 356
  5. 2018Learning to Compose Dynamic Tree Structures for Visual Contextscited by 259
  6. 2020GPS-Net: Graph Property Sensing Network for Scene Graph Generationcited by 166
  7. 2019Knowledge-Embedded Routing Network for Scene Graph Generationcited by 164
  8. 2021Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generationcited by 137
  9. 2021Energy-Based Learning for Scene Graph Generationcited by 104
  10. 2020Bridging Knowledge Graphs to Generate Scene Graphscited by 86
  11. 2022Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generationcited by 67
  12. 2020CogTree: Cognition Tree Loss for Unbiased Scene Graph Generationcited by 62
5 more
  1. 2022The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generationcited by 60
  2. 2021Segmentation-grounded Scene Graph Generationcited by 25
  3. 2022Resistance Training using Prior Bias: toward Unbiased Scene Graph Generationcited by 22
  4. 2021Not All Relations are Equal: Mining Informative Labels for Scene Graph Generationcited by 21
  5. 2022Context-aware Mixture-of-Experts for Unbiased Scene Graph Generationcited by 3

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2023Compositional Feature Augmentation for Unbiased Scene Graph Generationcited by 15
  2. 2024Scene Graph Generation Strategy with Co-occurrence Knowledge and Learnable Term Frequencycited by 4
  3. 2025A Reverse Causal Framework to Mitigate Spurious Correlations for Debiasing Scene Graph Generationcited by 1
  4. 2024Unbiased Scene Graph Generation by Type-Aware Message Passing on Heterogeneous and Dual Graphscited by 0