Visual Programming for Text-to-Image Generation and Evaluation
- URL: http://arxiv.org/abs/2305.15328v2
- Date: Fri, 27 Oct 2023 01:44:27 GMT
- Title: Visual Programming for Text-to-Image Generation and Evaluation
- Authors: Jaemin Cho, Abhay Zala, Mohit Bansal
- Abstract summary: We propose two novel interpretable/explainable visual programming frameworks for text-to-image (T2I) generation and evaluation.
First, we introduce VPGen, an interpretable step-by-step T2I generation framework that decomposes T2I generation into three steps: object/count generation, layout generation, and image generation.
Second, we introduce VPEval, an interpretable and explainable evaluation framework for T2I generation based on visual programming.
- Score: 73.12069620086311
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As large language models have demonstrated impressive performance in many
domains, recent works have adopted language models (LMs) as controllers of
visual modules for vision-and-language tasks. While existing work focuses on
equipping LMs with visual understanding, we propose two novel
interpretable/explainable visual programming frameworks for text-to-image (T2I)
generation and evaluation. First, we introduce VPGen, an interpretable
step-by-step T2I generation framework that decomposes T2I generation into three
steps: object/count generation, layout generation, and image generation. We
employ an LM to handle the first two steps (object/count generation and layout
generation), by finetuning it on text-layout pairs. Our step-by-step T2I
generation framework provides stronger spatial control than end-to-end models,
the dominant approach for this task. Furthermore, we leverage the world
knowledge of pretrained LMs, overcoming the limitation of previous
layout-guided T2I works that can only handle predefined object classes. We
demonstrate that our VPGen has improved control in counts/spatial
relations/scales of objects than state-of-the-art T2I generation models.
Second, we introduce VPEval, an interpretable and explainable evaluation
framework for T2I generation based on visual programming. Unlike previous T2I
evaluations with a single scoring model that is accurate in some skills but
unreliable in others, VPEval produces evaluation programs that invoke a set of
visual modules that are experts in different skills, and also provides
visual+textual explanations of the evaluation results. Our analysis shows that
VPEval provides a more human-correlated evaluation for skill-specific and
open-ended prompts than widely used single model-based evaluation. We hope that
our work encourages future progress on interpretable/explainable generation and
evaluation for T2I models.
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