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Multi-Modal CelebA-HQ Benchmark (multimodal generation)
Multimodal generation refers to the process of generating outputs that incorporate multiple modalities, such as images, text, and sound. This can be done using deep learning models that are trained on data that includes multiple modalities, allowing the models to generate output that is informed by more than one type of data.
For example, a multimodal generation model could be trained to generate captions for images that incorporate both text and visual information. The model could learn to identify objects in the image and generate descriptions of them in natural language, while also taking into account contextual information and the relationships between the objects in the image.
Multimodal generation can also be used in other applications, such as generating realistic images from textual descriptions or generating audio descriptions of video content. By combining multiple modalities in this way, multimodal generation models can produce more accurate and comprehensive output, making them useful for a wide range of applications.
The archive carries no text for this table; the description above is the archive's text for the task multimodal generation. archive 2025-07-28
Over time archive 2025-07-28
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Direction inferred from the metric name, not from the archive: FID (lower is better). Points are placed at the row's paper date; 1 of 1 rows carry one.
Results archive 2025-07-28
Archive rows end at the archive snapshot, 2025-07-28: no result published after that date is in this table. Rank is the archive's row order at that snapshot; not re-ranked here. Metric values are the archive's strings. Column headers sort the table in your browser; each row keeps its archive rank.
All 1 rows shown. 1 link to a paper page on this site; 0 are marked as using additional training data in the archive. No GitHub stars are tracked; "Code" is the first repository the archive lists for the row. The archive carries no row tags, review links or community-submitted rows for this table; none are shown. archive 2025-07-28
Syntology Ran reads "N of M ran · U unverified": of the M code samples Syntology harvested from repositories linked to that row's paper (joined by arXiv id), N executed on a synthesized input and the other U = M−N are unverified (harvested, no recorded run). It counts code from repositories linked to that row's paper, not this result: the row's number was not reproduced and nothing here is a correctness claim. The other cell texts mean no graph line for the row: "linked, not harvested" (the archive links code, Syntology has not harvested it), "no code linked" (no code link in the archive), "not matched" (the row's paper URL matched no paper on this site). 0 rows have a graph line, from 0 distinct papers; 0 rows (0 papers) have at least one sample that ran. Counting each paper once: Syntology ran 0 of 0 samples; 0 unverified. Separately, 0 of those 0 are pointer-only (licence): the site points at that code rather than redistributing it, a licence property recorded for ran and unverified samples alike; each cell's tooltip carries the row's own pointer-only count. Read from the graph 2026-09-24. Per-sample status is on the paper page.
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