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Task Graph Learning

2 papers with code · 0 benchmarks · 0 datasets archive 2025-07-28

Task Graph Learning involves constructing and learning directed acyclic graphs (DAGs) that represent procedural tasks, where nodes correspond to atomic actions or sub-tasks and edges capture dependencies or transitions between these actions.

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Benchmarks archive 2025-07-28

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Graph Learning

Most implemented papers archive 2025-07-28

2 shown of 2 papers with code (2 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.

Task Graph Maximum Likelihood Estimation for Procedural Activity Understanding in Egocentric Videos

25 Feb 2025 1 repository listed

We introduce a gradient-based approach for learning task graphs from procedural activities, improving over hand-crafted methods.
Differentiable Task Graph Learning: Procedural Activity Representation and Online Mistake Detection from Egocentric Videos

3 Jun 2024 1 repository listed

Task graphs learned with our approach are also shown to significantly enhance online mistake detection in procedural egocentric videos, achieving notable gains of +19.

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