Mistake Detection
3 papers with code • 0 benchmarks • 2 datasets
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”}.
Benchmarks
These leaderboards are used to track progress in Mistake Detection
Assembly101
ImageNet-X
Most implemented papers
Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities
Assembly101 is a new procedural activity dataset featuring 4321 videos of people assembling and disassembling 101 "take-apart" toy vehicles.
PREGO: online mistake detection in PRocedural EGOcentric videos
aleflabo/prego
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We propose PREGO, the first online one-class classification model for mistake detection in PRocedural EGOcentric videos.
Differentiable Task Graph Learning: Procedural Activity Representation and Online Mistake Detection from Egocentric Videos
fpv-iplab/differentiable-task-graph-learning
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Task graphs learned with our approach are also shown to significantly enhance online mistake detection in procedural egocentric videos, achieving notable gains of +19. 8% and +7. 5% on the Assembly101 and EPIC-Tent datasets.
