Video scene graph generation

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

2 datasets 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.

Most implemented papers archive 2025-07-28

7 shown of 7 papers with code (16 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.

• Panoptic Video Scene Graph Generation
  28 Nov 2023 3 repositories listed Syntology ran 11 of 11 samples · 0 unverified · 3 pointer-only (licence)
  PVSG relates to the existing video scene graph generation (VidSGG) problem, which focuses on temporal interactions between humans and objects grounded with bounding boxes in videos.
• DIFFVSGG: Diffusion-Driven Online Video Scene Graph Generation
  18 Mar 2025 1 repository listed
  This embedding then serves as the input to task-specific heads for object classification, scene graph generation, etc.
• Weakly Supervised Video Scene Graph Generation via Natural Language Supervision
  21 Feb 2025 1 repository listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Existing Video Scene Graph Generation (VidSGG) studies are trained in a fully supervised manner, which requires all frames in a video to be annotated, thereby incurring high annotation cost compared to Image Scene Graph…
• SportsHHI: A Dataset for Human-Human Interaction Detection in Sports Videos
  6 Apr 2024 1 repository listed
  We hope that SportsHHI can stimulate research on human interaction understanding in videos and promote the development of spatio-temporal context modeling techniques in video visual relation detection.
• Spatial-Temporal Knowledge-Embedded Transformer for Video Scene Graph Generation
  23 Sep 2023 1 repository listed
  In this work, we propose a spatial-temporal knowledge-embedded transformer (STKET) that incorporates the prior spatial-temporal knowledge into the multi-head cross-attention mechanism to learn more representative…
• Taking A Closer Look at Visual Relation: Unbiased Video Scene Graph Generation with Decoupled Label Learning
  23 Mar 2023 1 repository listed
  Specifically, DLL decouples the predicate labels and adopts separate classifiers to learn actional and spatial patterns respectively.
• Target Adaptive Context Aggregation for Video Scene Graph Generation
  18 Aug 2021 1 repository listed
  Specifically, we design an efficient method for frame-level VidSGG, termed as {\em Target Adaptive Context Aggregation Network} (TRACE), with a focus on capturing spatio-temporal context information for relation…

Syntology lines on 2 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.