Browse State-of-the-Art › Image-guided Story Ending Generation
Image-guided Story Ending Generation
5 papers with code · 2 benchmarks · 2 datasets archive 2025-07-28
Image-guided Story Ending Generation (IgSEG) aims to generate a story ending for a given multi-sentence story plot and an ending-related image.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
2 leaderboard tables shown for this task, 2 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.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| VIST-E (6 rows) | MMT | MMT: Image-guided Story Ending Generation with Multimodal Memory... | code | — | Compare |
| LSMDC-E (4 rows) | MMT | MMT: Image-guided Story Ending Generation with Multimodal Memory... | code | — | Compare |
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 (9 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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12 Jun 2017 595 repositories listed Syntology ran 600 of 946 samples · 346 unverified · 451 pointer-only (licence)The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
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17 Aug 2015 44 repositories listed Syntology ran 2 of 8 samples · 6 unverified · 2 pointer-only (licence)Our ensemble model using different attention architectures has established a new state-of-the-art result in the WMT'15 English to German translation task with 25.
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5 Sep 2023 1 repository listedIn recent years, cross-modal reasoning (CMR), the process of understanding and reasoning across different modalities, has emerged as a pivotal area with applications spanning from multimedia analysis to healthcare…
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10 Oct 2022 1 repository listedFinally, a multimodal transformer decoder constructs attention among multimodal features to learn the story dependency and generates informative, reasonable, and coherent story endings.
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30 Aug 2018 1 repository listedThis task requires not only to understand the context clues which play an important role in planning the plot but also to handle implicit knowledge to make a reasonable, coherent story.
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.
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