Browse State-of-the-Art › Multimedia Generative Script Learning
Multimedia Generative Script Learning
1 paper with code · 0 benchmarks · 0 datasets archive 2025-07-28
Given an activity goal G, an optional subgoal M that specifies the concrete needs, and the previous multimedia step history Hₙ={(S₁,V₁),...,(Sₙ,Vₙ)} with length n, a model is expected to predict the next possible step Sₙ₊₁, where Sᵢ is a text sequence and Vᵢ is an image.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
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Libraries
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Datasets archive 2025-07-28
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Subtasks archive 2025-07-28
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Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
1 shown of 1 paper with code (1 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.
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25 Aug 2022 1 repository listed Syntology ran 3 of 9 samples · 6 unverifiedGoal-oriented generative script learning aims to generate subsequent steps to reach a particular goal, which is an essential task to assist robots or humans in performing stereotypical activities.
Syntology lines on 1 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.
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