用于文本到图像上下文学习的思维树推理
Tree-of-Thoughts Reasoning for Text-to-Image In-Context Learning
AI总结：
研究文本到图像上下文学习中模型的推理问题，提出思维树推理框架，通过多阶段推理和选择层减轻提示模糊性与组合错误，实验表明该结构化多分支推理能提升图像生成效果，无需额外训练或微调。
摘要：
在文本到图像的上下文学习（T2I-ICL）中，模型必须从少样本示例中推断潜在的组合模式以生成查询图像。近期研究表明，最先进的多模态大语言模型在这种情况下存在困难，尤其是由于组合推理有限和对提示构建敏感。本文提出了一种用于T2I-ICL的思维树（ToT）推理框架，引入了一个多阶段推理和选择层，在构建最终图像合成提示之前，对多个候选假设进行生成、评估和选择。通过探索替代推理分支并选择连贯的解释，该方法减轻了提示模糊性和组合错误。我们在完整的ToT-T2IICL推理管道中实现了该方法，并在CoBSAT基准上进行评估。定性和定量结果表明，与基线和思维链提示策略相比，结构化多分支推理能带来更一致且语义对齐的图像生成，无需任何额外训练或微调。
英文摘要：
In text-to-image in-context learning (T2I-ICL), a model has to infer a latent compositional pattern from fewshot demonstrations for generating a query image. Recent studies show that state-of-the-art multimodal large language models struggle with this setting, particularly due to limited compositional reasoning and sensitivity to prompt construction. In this work, we propose a Tree-of-Thoughts (ToT) reasoning framework for T2I-ICL that introduces a multi-stage reasoning and selection layer that generates, evaluates, and selects among multiple candidate hypotheses before constructing the final prompt for image synthesis. By exploring alternative reasoning branches and selecting a coherent interpretation, the proposed approach mitigates prompt ambiguity and compositional errors. We implement the proposed approach in a complete ToT-T2IICL inference pipeline and evaluate it on the CoBSAT benchmark. Both qualitative and quantitative results show that structured multi-branch reasoning leads to more consistent and semantically aligned image generation compared to baseline and Chain-of-Thought prompting strategies, without any additional training or fine-tuning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
