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Unsupervised Object-Centric Learning from Multiple Unspecified Viewpoints
This ability is essential for humans to identify the same object while moving and to learn from vision efficiently.
OCTScenes: A Versatile Real-World Dataset of Tabletop Scenes for Object-Centric Learning
The results demonstrate the shortcomings of state-of-the-art methods for learning meaningful representations from real-world data, despite their impressive performance on complex synthesis datasets.
Compositional Scene Modeling with Global Object-Centric Representations
Inspired by such an ability of humans, this paper proposes a compositional scene modeling method to infer global representations of canonical images of objects without any supervision.
Compositional Scene Representation Learning via Reconstruction: A Survey
In this survey, we first outline the current progress on reconstruction-based compositional scene representation learning with deep neural networks, including development history and categorizations of existing methods from the perspectives of the modeling of visual scenes and the inference of scene representations; then provide benchmarks, including an open source toolbox to reproduce the benchmark experiments, of representative methods that consider the most extensively studied problem setting and form the foundation for other methods; and finally discuss the limitations of existing methods and future directions of this research topic.
