5 papers
Learning Global Object-Centric Representations via Disentangled Slot Attention
Tonglin Chen, Yinxuan Huang, Zhimeng Shen +3
Humans can discern scene-independent features of objects across various environments, allowing them to swiftly identify objects amidst changing factors such as lighting, perspectiv…
Unsupervised Object-Centric Learning from Multiple Unspecified Viewpoints
Jinyang Yuan, Tonglin Chen, Zhimeng Shen +2
Visual scenes are extremely diverse, not only because there are infinite possible combinations of objects and backgrounds but also because the observations of the same scene may va…
OCTScenes: A Versatile Real-World Dataset of Tabletop Scenes for Object-Centric Learning
Yinxuan Huang, Tonglin Chen, Zhimeng Shen +3
Humans possess the cognitive ability to comprehend scenes in a compositional manner. To empower AI systems with similar capabilities, object-centric learning aims to acquire repres…
Compositional Scene Modeling with Global Object-Centric Representations
Tonglin Chen, Bin Li, Zhimeng Shen +1
The appearance of the same object may vary in different scene images due to perspectives and occlusions between objects. Humans can easily identify the same object, even if occlusi…
Compositional Scene Representation Learning via Reconstruction: A Survey
Jinyang Yuan, Tonglin Chen, Bin Li +1
Visual scenes are composed of visual concepts and have the property of combinatorial explosion. An important reason for humans to efficiently learn from diverse visual scenes is th…