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Spatial Relation Recognition datasets

archive 2025-07-28

5 datasets carry the task tag "Spatial Relation Recognition" (the task itself: Spatial Relation Recognition), ordered by the archive's paper count. Page 1 of 1: 5 shown of 5. Facet routes are this site's own (the archive records the tag string, not a page).

The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.

Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets

Spatial Relation Recognition datasets 1–5 of 5

MRR-Benchmark (Multi-Modal Reading Benchmark)
Multi-Modal Reading (MMR) Benchmark includes 550 annotated question-answer pairs across 11 distinct tasks involving texts, fonts, visual elements, bounding boxes, spatial relations, and grounding, with carefully designed evaluation metrics.
11 papers · 1 benchmark
Understanding spatial relations (e.g., “laptop on table”) in visual input is important for both humans and robots.
7 papers · 1 benchmark
ResQ (Real-world Spatial Question Answering)
ReSQ is a real-world Spatial Question Answering dataset with human-generated questions built on an existing corpus with SpRL annotations.
3 papers · 0 benchmarks
SpatialSense Benchmark is a dataset specializing in spatial relation recognition which captures a broad spectrum of such challenges, allowing for proper benchmarking of computer vision techniques.
2 papers · 0 benchmarks

Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.