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Z
Zitong Xu
Shanghai Jiao Tong University
H
Huiyu Duan
Shanghai Jiao Tong University
Z
Zhongpeng Ji
Vivo Mobile Communication Co., Ltd
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Image experts on LinkedIn & GitHub
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Founder's Pitch
"A million-scale human preference dataset and evaluation model for optimizing text-guided image editing."
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0-10 scaleHigh Potential
4/4 signals
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Why It Matters
This research provides the largest dataset of human feedback for image editing, which is crucial for training evaluation models that align more closely with human preferences, a current gap in the development of AI-driven image editing tools that are capable of high-quality, user-preferred outcomes.
Product Angle
Offering an API or integrated tool for popular image editing software that reviews and provides feedback on edits based on human preferences could greatly assist photographers and editors in refining their work according to proven aesthetic standards.
Disruption
This solution could replace traditional manual review processes, offering scalable and quicker feedback that aligns closely with human tastes. It can also refine current AI editing tools that often lack nuanced and context-sensitive evaluation.
Product Opportunity
The product can target professional photographers, design firms, and hobbyists who demand high-quality image edits and professional feedback. Companies like Adobe or Canva could integrate this into their products to enhance their user experience.
Use Case Idea
Develop an API service for photo editing applications allowing users to submit images and receive automated feedback based on human preferences, helping users improve their image editing outcomes.
Science
EditHF-1M is a comprehensive dataset comprising over one million edited images with human preference rankings and scores. It evaluates edits in terms of visual quality, instruction alignment, and attribute preservation. The EditHF model uses this data to provide human-aligned feedback, employing a multimodal language model for evaluation. EditHF-Reward incorporates these evaluations as reward signals for reinforcement learning, optimizing image editing model performance.
Method & Eval
The dataset EditHF-1M incorporates 29M human preference pairs and 148K human ratings. EditHF rewards models are fine-tuned using these large-scale human feedback through reinforcement learning. Extensive experiments confirm its superior alignment and generalization across editing tasks, validated by achieving state-of-the-art performance in evaluations.
Caveats
The performance and applicability may vary for images and edits not covered within the 43 task categories or for different cultural aesthetics. The dataset may not fully capture the diversity in human tastes globally. There is also a risk of bias in annotations if not diverse.
Author Intelligence
Zitong Xu
Shanghai Jiao Tong University
Huiyu Duan
Shanghai Jiao Tong University
Zhongpeng Ji
Vivo Mobile Communication Co., Ltd