Omkar2118/datasets huggingface dataset:
GenAI-Bench
Full name: GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual Generation

GenAI-Bench is a benchmarking framework designed to evaluate and improve compositional text-to-visual generation models. It was developed by researchers from Carnegie Mellon University and Meta. The key aspects of GenAI-Bench include:

- **Compositional Text-to-Visual Generation**: It focuses on the ability of generative models to handle compositional text prompts that involve attributes, relationships, and higher-order reasoning such as logic and comparison¹.
  
- **Diverse Text Prompts**: GenAI-Bench uses 1,600 text prompts collected from professional graphic designers to cover a wide range of compositional reasoning skills¹.
  
- **Human Studies**: The framework includes human annotators who rate the performance of leading generative models like DALL-E 3, Stable Diffusion, and others based on image-text or video-text alignment¹.
  
- **Automated Evaluation Metrics**: It aims to benchmark automated evaluation metrics that measure the alignment between an image and a text prompt².
  
- **Improving Generation**: GenAI-Bench also explores how VQAScore, an automated metric, can improve image generation by selecting the highest-scoring images from generated candidates¹.

Overall, GenAI-Bench provides a comprehensive and challenging testbed for state-of-the-art text-to-visual generative models, pushing the boundaries of what these models can achieve in terms of understanding and creating complex visual compositions based on textual descriptions.

(1) GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual .... https://linzhiqiu.github.io/papers/genai_bench/.
(2) GenAI-Bench: A Holistic Benchmark for Compositional Text-to-Visual .... https://openreview.net/pdf?id=hJm7qnW3ym.
(3) Evaluating Text-to-Visual Generation with Image-to-Text Generation. https://linzhiqiu.github.io/papers/vqascore/.