Search Results for author: Jinyang Yuan

Found 5 papers, 2 papers with code

Unsupervised Object-Centric Learning from Multiple Unspecified Viewpoints

no code implementations3 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.

Object

Compositional Scene Representation Learning via Reconstruction: A Survey

no code implementations15 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.

Representation Learning

Unsupervised Learning of Compositional Scene Representations from Multiple Unspecified Viewpoints

no code implementations7 Dec 2021 Jinyang Yuan, Bin Li, xiangyang xue

When observing a visual scene that contains multiple objects from multiple viewpoints, humans are able to perceive the scene in a compositional way from each viewpoint, while achieving the so-called "object constancy" across different viewpoints, even though the exact viewpoints are untold.

Knowledge-Guided Object Discovery with Acquired Deep Impressions

1 code implementation19 Mar 2021 Jinyang Yuan, Bin Li, xiangyang xue

The proposed ADI framework focuses on the acquisition and utilization of knowledge, and is complementary to existing deep generative models proposed for compositional scene representation.

Object Object Discovery +1

Spatial Mixture Models with Learnable Deep Priors for Perceptual Grouping

1 code implementation7 Feb 2019 Jinyang Yuan, Bin Li, xiangyang xue

Different from existing methods, the proposed method disentangles the attributes of an object into ``shape'' and ``appearance'' which are modeled separately by the mixture weights and the mixture components.

Object

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