Learning 3D Object-Centric Representation Through Prediction
The author argues that by using prediction as the main learning objective, a novel network architecture called OPPLE can simultaneously learn object segmentation, depth perception, and 3D object localization without supervision. This approach is inspired by how infants develop perceptual abilities.
3D Object-Centric Representation Learning Through Prediction
Objects are learned through prediction, mimicking human infant abilities.
Simplified and Scalable Object-Centric Representation Learning with Competitive Slot Extraction
A simple, scalable, and non-iterative method called SAMP (Simplified Slot Attention with Max Pool Priors) that learns object-centric representations from images by inducing competition and specialization among slots.
Unsupervised Learning of Object Representations: Exploring the Trade-off Between Disentanglement and Entanglement in Distributed Models
Training models to predict future states in dynamic environments can lead to the emergence of linearly separable object representations, even in models without explicit object-centric architectural priors, suggesting that partially entangled representations can be beneficial for generalization.
