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Found 5 papers, 4 papers with code
Object-Centric Learning for Real-World Videos by Predicting Temporal Feature Similarities
Recently, it was shown that the reconstruction of pre-trained self-supervised features leads to object-centric representations on unconstrained real-world image datasets.
Bridging the Gap to Real-World Object-Centric Learning
Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world.
Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations
In this paper, we show that recent advances in self-supervised feature learning enable unsupervised object discovery and semantic segmentation with a performance that matches the state of the field on supervised semantic segmentation 10 years ago.
Self-supervised Reinforcement Learning with Independently Controllable Subgoals
We propose a novel self-supervised agent that estimates relations between environment components and uses them to independently control different parts of the environment state.
Self-supervised Visual Reinforcement Learning with Object-centric Representations
We show that the structure in the representations in combination with goal-conditioned attention policies helps the autonomous agent to discover and learn useful skills.
