CusConcept：基于扩散模型的定制化视觉概念分解
CusConcept: Customized Visual Concept Decomposition with Diffusion Models
AI总结：
本文提出两阶段框架 CusConcept，利用扩散模型从单张图像中沿人类指定概念轴分解并生成定制化视觉概念，同时构建评估基准，实验验证了其有效性。
摘要：
使生成模型能够从单张图像中分解视觉概念是一个复杂且具有挑战性的问题。本文研究了一个新的具有挑战性的任务——定制化概念分解，其目标是利用扩散模型对单张图像进行分解，并从不同视角生成视觉概念。为应对这一挑战，我们提出了一个两阶段框架 CusConcept（定制化视觉概念分解的缩写），用于提取可嵌入提示词中以进行文本到图像生成的定制化视觉概念嵌入向量。在第一阶段，CusConcept 采用词汇引导的概念分解机制，沿人类指定的概念轴构建词汇表。分解得到的概念通过检索相应词汇表并学习锚点权重来获得。在第二阶段，执行联合概念精炼以提升生成图像的保真度和质量。我们进一步构建了一个评估基准，用于评估开放世界概念分解任务的性能。我们的方法能够有效生成分解概念的高质量图像，并产生相关的词汇预测作为次要结果。大量定性和定量实验证明了 CusConcept 的有效性。
英文摘要：
Enabling generative models to decompose visual concepts from a single image is a complex and challenging problem. In this paper, we study a new and challenging task, customized concept decomposition, wherein the objective is to leverage diffusion models to decompose a single image and generate visual concepts from various perspectives. To address this challenge, we propose a two-stage framework, CusConcept (short for Customized Visual Concept Decomposition), to extract customized visual concept embedding vectors that can be embedded into prompts for text-to-image generation. In the first stage, CusConcept employs a vocabulary-guided concept decomposition mechanism to build vocabularies along human-specified conceptual axes. The decomposed concepts are obtained by retrieving corresponding vocabularies and learning anchor weights. In the second stage, joint concept refinement is performed to enhance the fidelity and quality of generated images. We further curate an evaluation benchmark for assessing the performance of the open-world concept decomposition task. Our approach can effectively generate high-quality images of the decomposed concepts and produce related lexical predictions as secondary outcomes. Extensive qualitative and quantitative experiments demonstrate the effectiveness of CusConcept.
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