Superpixel Image Classification

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

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.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

5 shown of 5 papers with code (7 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.

• Escaping the Big Data Paradigm with Compact Transformers
  12 Apr 2021 9 repositories listed Syntology ran 3 of 6 samples · 3 unverified · 1 pointer-only (licence)
  Our models are flexible in terms of model size, and can have as little as 0.
• SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
  24 Nov 2017 5 repositories listed
  We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e.
• Geometric deep learning on graphs and manifolds using mixture model CNNs
  25 Nov 2016 4 repositories listed Syntology ran 0 of 5 samples · 5 unverified
  Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications…
• A Dynamic Reduction Network for Point Clouds
  18 Mar 2020 1 repository listed
  Classifying whole images is a classic problem in machine learning, and graph neural networks are a powerful methodology to learn highly irregular geometries.
• Superpixel Image Classification with Graph Attention Networks
  13 Feb 2020 1 repository listed
  This paper presents a methodology for image classification using Graph Neural Network (GNN) models.

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.