Awesome Evaluation of Visual Generation
The "Awesome Evaluation of Visual Generation" is a comprehensive project dedicated to collating and curating various methods and metrics used to evaluate visual generation. This project aims to serve as a valuable resource for researchers and enthusiasts in the field of artificial intelligence and machine learning, specifically those working on generative models for images and videos.
What This Project Offers
This repository addresses several fundamental questions related to the evaluation of visual generation:
-
Model Evaluation: It explores how one can assess the quality of specific image or video generation models. Understanding the strengths and weaknesses of different models is crucial for advancing the field.
-
Sample/Content Evaluation: The repository provides resources for evaluating the quality of particular generated content, be it images or videos. This ensures that generated media meets desired standards of realism and relevance.
-
User Control Consistency Evaluation: The project also delves into methods for assessing how well-generated visuals align with user inputs or controls, ensuring that the output matches user expectations and requirements.
Continual Updates
The repository is regularly updated with the latest research, methodologies, and resources. It encourages contributions from the community through raising issues, nominating relevant works via pull requests, or communicating suggestions and updates via email.
Organized Structure
The content is meticulously organized into various sections for easy navigation:
-
Evaluation Metrics of Generative Models: This section covers a range of metrics used to assess the performance of image and video generative models, including well-known metrics like Inception Score (IS) and Fréchet Inception Distance (FID).
-
Evaluation Metrics of Condition Consistency: It includes metrics for evaluating the consistency of multi-modal inputs, such as text and image consistency assessed via CLIP Score.
-
Evaluation Systems of Generative Models: This area covers evaluation systems for various generative models, from unconditional image generation to neural style transfer and text-to-motion generation.
-
Improving Visual Generation with Evaluation/Feedback/Reward: Insights into enhancing generative models using evaluation results and feedback.
-
Quality Assessment for AIGC: Provides guidelines for assessing the quality of AI-generated content (AIGC).
-
Study and Rethinking: Encourages revisiting and re-evaluating existing methodologies for better outcomes.
-
Other Useful Resources: Includes additional resources and references for further exploration.
This project is a treasure trove for anyone interested in understanding the complexities and techniques involved in evaluating the ever-progressing domain of visual generative models. Whether a novice or an experienced researcher, this repository provides a wealth of knowledge to foster innovation and improvement in the field.