Corpus ID: 195750617

Unsupervised Learning of Object Keypoints for Perception and Control

@inproceedings{Kulkarni2019UnsupervisedLO,
  title={Unsupervised Learning of Object Keypoints for Perception and Control},
  author={T. Kulkarni and A. Gupta and Catalin Ionescu and Sebastian Borgeaud and M. Reynolds and Andrew Zisserman and V. Mnih},
  booktitle={NeurIPS},
  year={2019}
}
  • T. Kulkarni, A. Gupta, +4 authors V. Mnih
  • Published in NeurIPS 2019
  • Computer Science
  • The study of object representations in computer vision has primarily focused on developing representations that are useful for image classification, object detection, or semantic segmentation as downstream tasks. [...] Key Method Our method learns from raw video frames in a fully unsupervised manner, by transporting learnt image features between video frames using a keypoint bottleneck. The discovered keypoints track objects and object parts across long time-horizons more accurately than recent similar methods…Expand Abstract
    Unsupervised Learning of Object Structure and Dynamics from Videos
    • 24
    • PDF
    Entity Abstraction in Visual Model-Based Reinforcement Learning
    • 17
    • Highly Influenced
    • PDF
    Object-Centric Learning with Slot Attention
    • 6
    • PDF
    Self-Supervised Correspondence in Visuomotor Policy Learning
    • 9
    • PDF
    Neuroevolution of self-interpretable agents
    • 7
    • PDF
    Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning.
    Self-Supervised Object-Level Deep Reinforcement Learning
    AlignNet: Unsupervised Entity Alignment
    • 1
    • PDF

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 48 REFERENCES
    Learning to Refine Object Segments
    • 555
    • PDF
    Deep spatial autoencoders for visuomotor learning
    • 278
    • PDF
    ImageNet classification with deep convolutional neural networks
    • 52,359
    • PDF
    Unsupervised Discovery of Object Landmarks as Structural Representations
    • 66
    • Highly Influential
    • PDF
    Unsupervised Learning of Object Landmarks by Factorized Spatial Embeddings
    • 74
    • PDF
    Unsupervised Video Object Segmentation for Deep Reinforcement Learning
    • 30
    • PDF
    Adam: A Method for Stochastic Optimization
    • 49,762
    • PDF
    Multi-Object Representation Learning with Iterative Variational Inference
    • 77
    • PDF
    Human-level control through deep reinforcement learning
    • 9,811
    • PDF