预训练扩散模型的槽位引导自适应用于以对象为中心的学习与组合生成
Slot-Guided Adaptation of Pre-trained Diffusion Models for Object-Centric Learning and Compositional Generation
AI总结：
提出SlotAdapt方法，通过引入基于槽位条件化的适配器将槽位注意力与预训练扩散模型结合，并加入引导损失对齐交叉注意力，在无需外部监督下提升了对象发现、图像生成及复杂组合生成任务的性能。
摘要：
我们提出了SlotAdapt，一种以对象为中心的学习方法，通过引入基于槽位条件化的适配器，将槽位注意力与预训练扩散模型相结合。该方法保留了预训练扩散模型的生成能力，同时避免了其以文本为中心的条件化偏差。我们还在架构中引入了额外的引导损失，以对齐来自适配器层的交叉注意力与槽位注意力。这无需外部监督即可增强模型与输入图像中对象的对齐。实验结果表明，在包括真实图像在内的多个数据集上，该方法在对象发现和图像生成任务中均优于现有最先进技术。此外，实验证明，与其他基于槽位的生成方法相比，该方法在复杂真实世界图像的组合生成任务中表现尤为出色。项目页面位于 https://kaanakan.github.io/SlotAdapt/。
英文摘要：
We present SlotAdapt, an object-centric learning method that combines slot attention with pretrained diffusion models by introducing adapters for slot-based conditioning. Our method preserves the generative power of pretrained diffusion models, while avoiding their text-centric conditioning bias. We also incorporate an additional guidance loss into our architecture to align cross-attention from adapter layers with slot attention. This enhances the alignment of our model with the objects in the input image without using external supervision. Experimental results show that our method outperforms state-of-the-art techniques in object discovery and image generation tasks across multiple datasets, including those with real images. Furthermore, we demonstrate through experiments that our method performs remarkably well on complex real-world images for compositional generation, in contrast to other slot-based generative methods in the literature. The project page can be found at https://kaanakan.github.io/SlotAdapt/.
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。
