Papers › Hierarchically-Attentive RNN for Album Summarization and Storytelling
Hierarchically-Attentive RNN for Album Summarization and Storytelling
Licheng Yu, Mohit Bansal, Tamara L. Berg
We address the problem of end-to-end visual storytelling. Given a photo album, our model first selects the most representative (summary) photos, and then composes a natural language story for the album. For this task, we make use of the Visual Storytelling dataset and a model composed of three hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album photos, select representative (summary) photos, and compose the story. Automatic and human evaluations show our model achieves better performance on selection, generation, and retrieval than baselines.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Storytelling | VIST | h-attn-rank | BLEU-3 | 20.78 | #32 of 33 | Archive leaderboard | report |
| Visual Storytelling | VIST | h-attn-rank | CIDEr | 7.38 | #32 of 33 | Archive leaderboard | report |
| Visual Storytelling | VIST | h-attn-rank | METEOR | 33.94 | #32 of 33 | Archive leaderboard | report |
| Visual Storytelling | VIST | h-attn-rank | ROUGE-L | 29.82 | #32 of 33 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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