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awesome-t2i-eval
A curated list of papers and resources for text-to-image evaluation.
https://github.com/zhangjiewu/awesome-t2i-eval
Last synced: about 16 hours ago
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Metrics
- PyTorch - score-pytorch?style=social&label=Star)](https://github.com/sbarratt/inception-score-pytorch)
- PyTorch - fid?style=social&label=Star)](https://github.com/mseitzer/pytorch-fid)
- CLIP Score
- ![Star
- BLIP Score
- ![Star
- ![arXiv
- IS (Inception Score)
- ![Star - gan)
- FID (Fréchet Inception Distance)
- ![Star - jku/TTUR)
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Papers
- What You See is What You Read? Improving Text-Image Alignment Evaluation
- ![Star
- Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation
- Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback
- ![Star - Evaluate-and-Refine)
- ![Star
- T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation
- ![Star - Huang/T2I-CompBench)
- Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis
- ![Star
- Visual Programming for Text-to-Image Generation and Evaluation
- ![Star
- LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation
- ![Star
- X-IQE: eXplainable Image Quality Evaluation for Text-to-Image Generation with Visual Large Language Models
- ![Star - Awesome-Diffusion-Models)
- Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation
- ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation
- ![Star
- Better Aligning Text-to-Image Models with Human Preference
- ![Star
- TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering
- ![Star - Hu/tifa)
- DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers
- ![Star - min/DallEval)
- ![Website - benchmark.github.io/)
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