no code implementations • 3 Jan 2024 • Jinyang Yuan, Tonglin Chen, Zhimeng Shen, Bin Li, xiangyang xue
This ability is essential for humans to identify the same object while moving and to learn from vision efficiently.
no code implementations • 16 Jun 2023 • Yinxuan Huang, Tonglin Chen, Zhimeng Shen, Jinghao Huang, Bin Li, xiangyang xue
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.
no code implementations • 21 Nov 2022 • Tonglin Chen, Bin Li, Zhimeng Shen, xiangyang xue
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.
no code implementations • 15 Feb 2022 • Jinyang Yuan, Tonglin Chen, Bin Li, xiangyang xue
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.