Visual Analogies

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

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.

Most implemented papers archive 2025-07-28

5 shown of 5 papers with code (10 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.

• Tile2Vec: Unsupervised representation learning for spatially distributed data
  8 May 2018 4 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks.
• KiVA: Kid-inspired Visual Analogies for Testing Large Multimodal Models
  25 Jul 2024 1 repository listed Syntology ran 4 of 4 samples · 0 unverified
  This paper investigates visual analogical reasoning in large multimodal models (LMMs) compared to human adults and children.
• Do Large Language Models Solve ARC Visual Analogies Like People Do?
  13 Mar 2024 1 repository listed
  The Abstraction Reasoning Corpus (ARC) is a visual analogical reasoning test designed for humans and machines (Chollet, 2019).
• Solving ARC visual analogies with neural embeddings and vector arithmetic: A generalized method
  14 Nov 2023 1 repository listed
  This project focuses on visual analogical reasoning and applies the initial generalized mechanism used to solve verbal analogies to the visual realm.
• VASR: Visual Analogies of Situation Recognition
  8 Dec 2022 1 repository listed Syntology ran 0 of 8 samples · 8 unverified
  We leverage situation recognition annotations and the CLIP model to generate a large set of 500k candidate analogies.

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.