Papers › A Hierarchical Approach for Generating Descriptive Image Paragraphs
A Hierarchical Approach for Generating Descriptive Image Paragraphs
Jonathan Krause, Justin Johnson, Ranjay Krishna, Li Fei-Fei
Recent progress on image captioning has made it possible to generate novel sentences describing images in natural language, but compressing an image into a single sentence can describe visual content in only coarse detail. While one new captioning approach, dense captioning, can potentially describe images in finer levels of detail by captioning many regions within an image, it in turn is unable to produce a coherent story for an image. In this paper we overcome these limitations by generating entire paragraphs for describing images, which can tell detailed, unified stories. We develop a model that decomposes both images and paragraphs into their constituent parts, detecting semantic regions in images and using a hierarchical recurrent neural network to reason about language. Linguistic analysis confirms the complexity of the paragraph generation task, and thorough experiments on a new dataset of image and paragraph pairs demonstrate the effectiveness of our approach.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Paragraph Captioning | Image Paragraph Captioning | Regions-Hierarchical (ours) | BLEU-4 | 8.69 | #7 of 10 | Archive leaderboard | report |
| Image Paragraph Captioning | Image Paragraph Captioning | Regions-Hierarchical (ours) | CIDEr | 13.52 | #7 of 10 | Archive leaderboard | report |
| Image Paragraph Captioning | Image Paragraph Captioning | Regions-Hierarchical (ours) | METEOR | 15.95 | #7 of 10 | 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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