Timezone: »
Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep learning approaches learn distributed representations that do not capture the compositional properties of natural scenes. In this paper, we present the Slot Attention module, an architectural component that interfaces with perceptual representations such as the output of a convolutional neural network and produces a set of task-dependent abstract representations which we call slots. These slots are exchangeable and can bind to any object in the input by specializing through a competitive procedure over multiple rounds of attention. We empirically demonstrate that Slot Attention can extract object-centric representations that enable generalization to unseen compositions when trained on unsupervised object discovery and supervised property prediction tasks.
Author Information
Francesco Locatello (ETH Zürich - MPI Tübingen)
Dirk Weissenborn (Google)
Thomas Unterthiner (Google Research, Brain Team)
Aravindh Mahendran (Google)
Georg Heigold (Google)
Jakob Uszkoreit (Google, Inc.)
Alexey Dosovitskiy (Google Research)
Thomas Kipf (Google Research)
Related Events (a corresponding poster, oral, or spotlight)
-
2020 Spotlight: Object-Centric Learning with Slot Attention »
Thu. Dec 10th 03:10 -- 03:20 PM Room Orals & Spotlights: Unsupervised/Probabilistic
More from the Same Authors
-
2022 : Test-time adaptation with slot-centric models »
Mihir Prabhudesai · Sujoy Paul · Sjoerd van Steenkiste · Mehdi S. M. Sajjadi · Anirudh Goyal · Deepak Pathak · Katerina Fragkiadaki · Gaurav Aggarwal · Thomas Kipf -
2022 : Spatial Symmetry in Slot Attention »
Ondrej Biza · Sjoerd van Steenkiste · Mehdi S. M. Sajjadi · Gamaleldin Elsayed · Aravindh Mahendran · Thomas Kipf -
2022 : Test-time adaptation with slot-centric models »
Mihir Prabhudesai · Sujoy Paul · Sjoerd van Steenkiste · Mehdi S. M. Sajjadi · Anirudh Goyal · Deepak Pathak · Katerina Fragkiadaki · Gaurav Aggarwal · Thomas Kipf -
2022 Workshop: Workshop on neuro Causal and Symbolic AI (nCSI) »
Matej Zečević · Devendra Dhami · Christina Winkler · Thomas Kipf · Robert Peharz · Petar Veličković -
2022 Poster: SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos »
Gamaleldin Elsayed · Aravindh Mahendran · Sjoerd van Steenkiste · Klaus Greff · Michael Mozer · Thomas Kipf -
2022 Poster: Object Scene Representation Transformer »
Mehdi S. M. Sajjadi · Daniel Duckworth · Aravindh Mahendran · Sjoerd van Steenkiste · Filip Pavetic · Mario Lucic · Leonidas Guibas · Klaus Greff · Thomas Kipf -
2021 Poster: MLP-Mixer: An all-MLP Architecture for Vision »
Ilya Tolstikhin · Neil Houlsby · Alexander Kolesnikov · Lucas Beyer · Xiaohua Zhai · Thomas Unterthiner · Jessica Yung · Andreas Steiner · Daniel Keysers · Jakob Uszkoreit · Mario Lucic · Alexey Dosovitskiy -
2021 Poster: Do Vision Transformers See Like Convolutional Neural Networks? »
Maithra Raghu · Thomas Unterthiner · Simon Kornblith · Chiyuan Zhang · Alexey Dosovitskiy -
2019 Poster: Are Disentangled Representations Helpful for Abstract Visual Reasoning? »
Sjoerd van Steenkiste · Francesco Locatello · Jürgen Schmidhuber · Olivier Bachem -
2019 Poster: On the Fairness of Disentangled Representations »
Francesco Locatello · Gabriele Abbati · Thomas Rainforth · Stefan Bauer · Bernhard Schölkopf · Olivier Bachem -
2019 Poster: On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset »
Muhammad Waleed Gondal · Manuel Wuethrich · Djordje Miladinovic · Francesco Locatello · Martin Breidt · Valentin Volchkov · Joel Akpo · Olivier Bachem · Bernhard Schölkopf · Stefan Bauer -
2019 Poster: RUDDER: Return Decomposition for Delayed Rewards »
Jose A. Arjona-Medina · Michael Gillhofer · Michael Widrich · Thomas Unterthiner · Johannes Brandstetter · Sepp Hochreiter -
2019 Poster: Stochastic Frank-Wolfe for Composite Convex Minimization »
Francesco Locatello · Alp Yurtsever · Olivier Fercoq · Volkan Cevher -
2018 : Panel »
Paroma Varma · Aditya Grover · Will Hamilton · Jessica Hamrick · Thomas Kipf · Marinka Zitnik -
2018 : Compositional Imitation Learning: Explaining and executing one task at a time »
Thomas Kipf -
2018 Poster: Boosting Black Box Variational Inference »
Francesco Locatello · Gideon Dresdner · Rajiv Khanna · Isabel Valera · Gunnar Ratsch -
2018 Spotlight: Boosting Black Box Variational Inference »
Francesco Locatello · Gideon Dresdner · Rajiv Khanna · Isabel Valera · Gunnar Ratsch -
2018 Poster: Unsupervised Learning of Shape and Pose with Differentiable Point Clouds »
Eldar Insafutdinov · Alexey Dosovitskiy -
2017 : Poster Spotlights »
Francesco Locatello · Ari Pakman · Da Tang · Thomas Rainforth · Zalan Borsos · Marko Järvenpää · Eric Nalisnick · Gabriele Abbati · XIAOYU LU · Jonathan Huggins · Rachit Singh · Rui Luo -
2017 Poster: Attention is All you Need »
Ashish Vaswani · Noam Shazeer · Niki Parmar · Jakob Uszkoreit · Llion Jones · Aidan Gomez · Łukasz Kaiser · Illia Polosukhin -
2017 Spotlight: Attention is All you Need »
Ashish Vaswani · Noam Shazeer · Niki Parmar · Jakob Uszkoreit · Llion Jones · Aidan Gomez · Łukasz Kaiser · Illia Polosukhin -
2017 Poster: Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees »
Francesco Locatello · Michael Tschannen · Gunnar Ratsch · Martin Jaggi -
2012 Workshop: Log-Linear Models »
Dimitri Kanevsky · Tony Jebara · Li Deng · Stephen Wright · Georg Heigold · Avishy Carmi