Predicate Detection

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 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

3 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.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

9 shown of 9 papers with code (12 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.

• Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship Detection
  20 Nov 2021 2 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  Furthermore, background knowledge represented by RWFNs can be used to alleviate the incompleteness of training sets even though the space complexity of RWFNs is much smaller than LTNs (1:27 ratio).
• VrdONE: One-stage Video Visual Relation Detection
  18 Aug 2024 1 repository listed
  Video Visual Relation Detection (VidVRD) focuses on understanding how entities interact over time and space in videos, a key step for gaining deeper insights into video scenes beyond basic visual tasks.
• Self-Supervised Learning for Visual Relationship Detection through Masked Bounding Box Reconstruction
  8 Nov 2023 1 repository listed
  We present a novel self-supervised approach for representation learning, particularly for the task of Visual Relationship Detection (VRD).
• A Novel Metric for Evaluating Semantics Preservation
  16 Oct 2021 1 repository listed
  By exploiting the property of NDD, we implement a unsupervised and even training-free algorithm for extractive sentence compression.
• Contextualized Semantic Distance between Highly Overlapped Texts
  4 Oct 2021 1 repository listed
  Overlapping frequently occurs in paired texts in natural language processing tasks like text editing and semantic similarity evaluation.
• Comparison of single and multitask learning for predicting cognitive decline based on MRI data
  21 Sep 2021 1 repository listed
  The Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) is a neuropsychological tool that has been designed to assess the severity of cognitive symptoms of dementia.
• Weighted Training for Cross-Task Learning
  28 May 2021 1 repository listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  In this paper, we introduce Target-Aware Weighted Training (TAWT), a weighted training algorithm for cross-task learning based on minimizing a representation-based task distance between the source and target tasks.
• Linguistically-Informed Self-Attention for Semantic Role Labeling
  23 Apr 2018 1 repository listed Syntology ran 0 of 14 samples · 14 unverified
  Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform…
• Deep Semantic Role Labeling: What Works and What's Next
  1 Jul 2017 1 repository listed
  We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations.

Syntology lines on 3 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.