Hierarchical Relational Inference
By Aleksandar Stani'c, Sjoerd van Steenkiste, J. Urgen Schmidhuber. 2020
17 citations recorded by the source corpus.
Common-sense physical reasoning in the real world requires learning about the interactions of objects and their dynamics. The notion of an abstract object, however, encompasses a wide variety of physical objects that differ greatly in terms of the complex behaviors they support. To address this, we propose a novel approach to physical reasoning that models objects as hierarchies of parts that may locally behave separately, but also act more globally as a single whole. Unlike prior approaches, our method learns in an unsupervised fashion directly from raw visual images to discover objects, parts, and their relations. It explicitly distinguishes multiple levels of abstraction and improves over a strong baseline at modeling synthetic and real-world videos.
Subfields: Computer Vision and Pattern Recognition
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In Computer Vision and Pattern Recognition
- Sjoerd van Steenkiste
- J. Urgen Schmidhuber
- Klaus Greff
- Luc Van Gool
- Ming-Hsuan Yang
- Dacheng Tao
- Andrea Vedaldi
- Trevor Darrell
Citations in the corpus
- On the Binding Problem in Artificial Neural Networks (2020, methodology): “…reasoning (Chang et al., 2017; Battaglia et al., 2016; Janner et al., 2019), hierarchical physical reasoning (Mrowca et al., 2018; Li et al., 2020; Stanić et al., 2020), visual question answering (Santoro et al., 2017; Palm et al., 2018), abstract visual reasoning (Andreas, 2019), natural…”
- Unsupervised Object Keypoint Learning using Local Spatial Predictability (2020, methodology): “Graph neural networks (GNNs) have been previously used for intuitive physics models (Battaglia et al., 2016; Chang et al., 2017; van Steenkiste et al., 2018; Janner et al., 2019; Veerapaneni et al., 2019; Kipf et al., 2020; Stanić et al., 2021).”