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Found 3 papers, 1 papers with code
Unsupervised Object-Based Transition Models for 3D Partially Observable Environments
We present a slot-wise, object-based transition model that decomposes a scene into objects, aligns them (with respect to a slot-wise object memory) to maintain a consistent order across time, and predicts how those objects evolve over successive frames.
AlignNet: Unsupervised Entity Alignment
Recently developed deep learning models are able to learn to segment scenes into component objects without supervision.
Multi-Object Representation Learning with Iterative Variational Inference
Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities.
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