Researchers have introduced RAP, a new dataset and benchmark designed for generating conference posters from academic papers. This dataset comprises over 25,000 verified paper-poster pairs, annotated with a content taxonomy, and includes a benchmark set of 150 test papers with their corresponding human-created posters. RAP facilitates paper-to-poster generation as a visual coding task, enabling both retrieval-based methods like PosterRAG and supervised fine-tuning with reinforcement learning. AI
IMPACT This dataset could accelerate research into AI systems capable of complex visual summarization and design tasks.
RANK_REASON The cluster describes a new dataset and benchmark for a specific AI task (paper-to-poster generation).
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- alphaXiv
- arXiv
- CatalyzeX
- Connected Papers
- CORE Recommender
- DagsHub
- Gotit.pub
- Hugging Face
- Litmaps
- PosterRAG
- Reconstruct A Poster
- ScienceCast
- scite Smart Citations
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