# SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition Source: [SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:08:20.129Z Authors: Zhixuan Lin, Yi-Fu Wu, Skand Vishwanath Peri, Weihao Sun, Gautam Singh, Fei Deng, Jindong Jiang, Sungjin Ahn Published: 2020-01-08T07:44:32.000Z ## Abstract The ability to decompose complex multi-object scenes into meaningful abstractions like objects is fundamental to achieve higher-level cognition. Previous approaches for unsupervised object-oriented scene representation learning are either based on spatial-attention or scene-mixture approaches and limited in scalability which is a main obstacle towards modeling real-world scenes. In this paper, we propose a generative latent variable model, called SPACE, that provides a unified probabilistic modeling framework that combines the best of spatial-attention and scene-mixture approaches. SPACE can explicitly provide factorized object representations for foreground objects while also decomposing background segments of complex morphology. Previous models are good at either of these, but not both. SPACE also resolves the scalability problems of previous methods by incorporating parallel spatial-attention and thus is applicable to scenes with a large number of objects without performance degradations. We show through experiments on Atari and 3D-Rooms that SPACE achieves the above properties consistently in comparison to SPAIR, IODINE, and GENESIS. Results of our experiments can be found on our project website: https://sites.google.com/view/space-project-page ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Object Detection on Multi-MNIST](): Object Detection; Multi-MNIST; F1 Score: 88.7. - [Object Detection on Sprites](): Object Detection; Sprites; F1 Score: 88.5. - [Object Detection on Tetrominoes](): Object Detection; Tetrominoes; F1 Score: 76. - [Object Detection on Cells](): Object Detection; CELLS; F1 Score: 0. ## Original sources and implementation [Original paper]() [PDF]()