Browse State-of-the-Art › Mistake Detection
Mistake Detection
5 papers with code · 0 benchmarks · 2 datasets archive 2025-07-28
Mistakes are natural occurrences in many tasks and an opportunity for an AR assistant to provide help. Identifying such mistakes requires modelling procedural knowledge and retaining long-range sequence information. In its simplest form Mistake Detection aims to classify each coarse action segment into one of the three classes: {“correct”, “mistake”, “correction”}.
Description from the archive archive 2025-07-28.
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
2 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
1 subtask in the archive's task tree.
Most implemented papers archive 2025-07-28
5 shown of 5 papers with code (14 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.
-
25 Feb 2025 1 repository listedWe introduce a gradient-based approach for learning task graphs from procedural activities, improving over hand-crafted methods.
-
4 Nov 2024 1 repository listed Syntology ran 2 of 2 samples · 0 unverifiedMistakes are detected as mismatches between the currently recognized action and the action predicted by the anticipation module.
-
3 Jun 2024 1 repository listedTask graphs learned with our approach are also shown to significantly enhance online mistake detection in procedural egocentric videos, achieving notable gains of +19.
-
2 Apr 2024 1 repository listed Syntology ran 10 of 11 samples · 1 unverifiedWe propose PREGO, the first online one-class classification model for mistake detection in PRocedural EGOcentric videos.
-
28 Mar 2022 1 repository listedAssembly101 is a new procedural activity dataset featuring 4321 videos of people assembling and disassembling 101 "take-apart" toy vehicles.
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.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections