Despite remarkable recent advances, making object-centric learning work for complex natural scenes remains the main challenge. The recent success of adopting the transformer-based image generative model in object-centric learning suggests that having a highly expressive image generator is crucial for dealing with complex scenes. In this paper, inspired by this observation, we aim to answer the following question: can we benefit from the other pillar of modern deep generative models, i.e., the diffusion models, for object-centric learning and what are the pros and cons of such a model? To this end, we propose a new object-centric learning model, Latent Slot Diffusion (LSD). LSD can be seen from two perspectives. From the perspective of object-centric learning, it replaces the conventional slot decoders with a latent diffusion model conditioned on the object slots. Conversely, from the perspective of diffusion models, it is the first unsupervised compositional conditional diffusion model which, unlike traditional diffusion models, does not require supervised annotation such as a text description to learn to compose. In experiments on various object-centric tasks, including the FFHQ dataset for the first time in this line of research, we demonstrate that LSD significantly outperforms the state-of-the-art transformer-based decoder, particularly when the scene is more complex. We also show a superior quality in unsupervised compositional generation.

翻译：尽管近年来取得了显著进展，但使面向对象学习适用于复杂自然场景仍是主要挑战。基于Transformer的图像生成模型在面向对象学习中的近期成功表明，拥有高表达能力的图像生成器对于处理复杂场景至关重要。受此启发，本文旨在回答以下问题：现代深度生成模型的另一支柱——即扩散模型——能否为面向对象学习带来益处，以及此类模型的优缺点是什么？为此，我们提出了一种新的面向对象学习模型——潜槽扩散（Latent Slot Diffusion, LSD）。LSD可从两个视角审视：从面向对象学习视角看，它用基于对象槽条件生成的潜扩散模型替代了传统的槽解码器；反之，从扩散模型视角看，它是首个无监督组合条件扩散模型，与传统扩散模型不同，它无需依赖文本描述等监督标注即可学习组合生成。在多个面向对象任务（包括该研究领域首次引入的FFHQ数据集）上的实验表明，LSD显著优于最先进的基于Transformer的解码器，尤其在场景更为复杂时表现突出。此外，我们在无监督组合生成任务中展现了卓越的生成质量。