Summary of Pqpp: a Joint Benchmark For Text-to-image Prompt and Query Performance Prediction, by Eduard Poesina et al.
PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction
by Eduard Poesina, Adriana Valentina Costache, Adrian-Gabriel Chifu, Josiane Mothe, Radu Tudor Ionescu
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, the authors introduce a new dataset for text-to-image generation and retrieval, focusing on analyzing the difficulty of prompts (queries) in generating images. They manually annotated over 10K queries to establish a joint benchmark for prompt and query performance prediction across both tasks. This benchmark enables researchers to evaluate and compare different methods for predicting prompt/query difficulty in image generation and retrieval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re looking at pictures on the internet, but instead of searching for specific words, you can describe what you want to see! That’s what text-to-image generation is all about. In this study, scientists created a special dataset with lots of prompts (like “show me a picture of a cat”) and asked people to rate how well each prompt worked in generating an image. They also collected ratings for text-to-image retrieval, where you search for specific images based on words. The goal is to make it easier for computers to understand what we want when searching or generating images. |
Keywords
» Artificial intelligence » Image generation » Prompt
