Scott Seidenberger

Khoi Trinh, Jay Rothenberger, Scott Seidenberger et al.

Iterative prompt refinement is central to reproducing target images with text to image generative models. Previous studies have incorporated image similarity metrics (ISMs) as additional feedback to human users. Existing ISMs such as LPIPS and CLIP provide objective measures of image likeness but often fail to align with human judgments, particularly in context specific or user driven tasks. In this paper, we introduce Customized Learned Perceptual Image Patch Similarity (CLPIPS), a customized extension of LPIPS that adapts a metric's notion of similarity directly to human judgments. We aim to explore whether lightweight, human augmented fine tuning can meaningfully improve perceptual alignment, positioning similarity metrics as adaptive components for human in the loop workflows with text to image tools. We evaluate CLPIPS on a human subject dataset in which participants iteratively regenerate target images and rank generated outputs by perceived similarity. Using margin ranking loss on human ranked image pairs, we fine tune only the LPIPS layer combination weights and assess alignment via Spearman rank correlation and Intraclass Correlation Coefficient. Our results show that CLPIPS achieves stronger correlation and agreement with human judgments than baseline LPIPS. Rather than optimizing absolute metric performance, our work emphasizes improving alignment consistency between metric predictions and human ranks, demonstrating that even limited human specific fine tuning can meaningfully enhance perceptual alignment in human in the loop text to image workflows.

Khoi Trinh, Scott Seidenberger, Raveen Wijewickrama et al.

With AI-generated content becoming ubiquitous across the web, social media, and other digital platforms, it is vital to examine how such content are inspired and generated. The creation of AI-generated images often involves refining the input prompt iteratively to achieve desired visual outcomes. This study focuses on the relatively underexplored concept of image regeneration using AI, in which a human operator attempts to closely recreate a specific target image by iteratively refining their prompt. Image regeneration is distinct from normal image generation, which lacks any predefined visual reference. A separate challenge lies in determining whether existing image similarity metrics (ISMs) can provide reliable, objective feedback in iterative workflows, given that we do not fully understand if subjective human judgments of similarity align with these metrics. Consequently, we must first validate their alignment with human perception before assessing their potential as a feedback mechanism in the iterative prompt refinement process. To address these research gaps, we present a structured user study evaluating how iterative prompt refinement affects the similarity of regenerated images relative to their targets, while also examining whether ISMs capture the same improvements perceived by human observers. Our findings suggest that incremental prompt adjustments substantially improve alignment, verified through both subjective evaluations and quantitative measures, underscoring the broader potential of iterative workflows to enhance generative AI content creation across various application domains.