Unbiased Scene Graph Generation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

16 shown of 16 papers with code (30 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Unbiased Scene Graph Generation from Biased Training
  27 Feb 2020 6 repositories listed Syntology ran 3 of 6 samples · 3 unverified · 6 pointer-only (licence)
  Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e.
• Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation
  1 Apr 2021 4 repositories listed
  Scene graph generation is an important visual understanding task with a broad range of vision applications.
• Fine-Grained Scene Graph Generation with Data Transfer
  22 Mar 2022 2 repositories listed
  Scene graph generation (SGG) is designed to extract (subject, predicate, object) triplets in images.
• Semantic Diversity-aware Prototype-based Learning for Unbiased Scene Graph Generation
  22 Jul 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  The scene graph generation (SGG) task involves detecting objects within an image and predicting predicates that represent the relationships between the objects.
• Head-Tail Cooperative Learning Network for Unbiased Scene Graph Generation
  23 Aug 2023 1 repository listed
  We also propose a self-supervised learning approach to enhance the prediction ability of the tail-prefer feature representation branch by constraining tail-prefer predicate features.
• Vision Relation Transformer for Unbiased Scene Graph Generation
  18 Aug 2023 1 repository listed Syntology ran 5 of 7 samples · 2 unverified
  Recent years have seen a growing interest in Scene Graph Generation (SGG), a comprehensive visual scene understanding task that aims to predict entity relationships using a relation encoder-decoder pipeline stacked on…
• Compositional Feature Augmentation for Unbiased Scene Graph Generation
  13 Aug 2023 1 repository listed Syntology ran 0 of 3 samples · 3 unverified
  Specifically, we first decompose each relation triplet feature into two components: intrinsic feature and extrinsic feature, which correspond to the intrinsic characteristics and extrinsic contexts of a relation…
• Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph Generation
  16 Jun 2023 1 repository listed
  This paper investigates the problem of scene graph generation in videos with the aim of capturing semantic relations between subjects and objects in the form of ⟨subject, predicate, object⟩ triplets.
• Unbiased Scene Graph Generation in Videos
  3 Apr 2023 1 repository listed
  The task of dynamic scene graph generation (SGG) from videos is complicated and challenging due to the inherent dynamics of a scene, temporal fluctuation of model predictions, and the long-tailed distribution of the…
• Skew Class-balanced Re-weighting for Unbiased Scene Graph Generation
  1 Jan 2023 1 repository listed
  An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-balanced Re-weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution.
• Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation
  16 Jul 2022 1 repository listed
  Experiments show that our approach achieves a new state-of-the-art performance on VG and GQA datasets and makes a trade-off between the performance of tail predicates and head ones.
• Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation
  18 Mar 2022 1 repository listed Syntology ran 3 of 7 samples · 4 unverified · 7 pointer-only (licence)
  Scene Graph Generation, which generally follows a regular encoder-decoder pipeline, aims to first encode the visual contents within the given image and then parse them into a compact summary graph.
• Resistance Training using Prior Bias: toward Unbiased Scene Graph Generation
  18 Jan 2022 1 repository listed Syntology ran 0 of 3 samples · 3 unverified
  To address this problem, we propose Resistance Training using Prior Bias (RTPB) for the scene graph generation.
• Recovering the Unbiased Scene Graphs from the Biased Ones
  5 Jul 2021 1 repository listed
  Given input images, scene graph generation (SGG) aims to produce comprehensive, graphical representations describing visual relationships among salient objects.
• CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation
  16 Sep 2020 1 repository listed
  We first build a cognitive structure CogTree to organize the relationships based on the prediction of a biased SGG model.
• PCPL: Predicate-Correlation Perception Learning for Unbiased Scene Graph Generation
  2 Sep 2020 1 repository listed
  Today, scene graph generation(SGG) task is largely limited in realistic scenarios, mainly due to the extremely long-tailed bias of predicate annotation distribution.

Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.