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Learning What and Where: Disentangling Location and Identity Tracking
  Without Supervision

Learning What and Where: Disentangling Location and Identity Tracking Without Supervision

26 May 2022
Manuel Traub
S. Otte
Tobias Menge
Matthias Karlbauer
Jannik Thummel
Martin Volker Butz
ArXivPDFHTML

Papers citing "Learning What and Where: Disentangling Location and Identity Tracking Without Supervision"

12 / 12 papers shown
Title
Entity-Centric Reinforcement Learning for Object Manipulation from
  Pixels
Entity-Centric Reinforcement Learning for Object Manipulation from Pixels
Dan Haramati
Tal Daniel
Aviv Tamar
LM&Ro
OffRL
OCL
27
10
0
01 Apr 2024
Loci-Segmented: Improving Scene Segmentation Learning
Loci-Segmented: Improving Scene Segmentation Learning
Manuel Traub
Frederic Becker
Adrian Sauter
S. Otte
Martin Volker Butz
18
2
0
16 Oct 2023
Learning Object Permanence from Videos via Latent Imaginations
Learning Object Permanence from Videos via Latent Imaginations
Manuel Traub
Frederic Becker
S. Otte
Martin Volker Butz
25
1
0
16 Oct 2023
Does Visual Pretraining Help End-to-End Reasoning?
Does Visual Pretraining Help End-to-End Reasoning?
Chen Sun
Calvin Luo
Xingyi Zhou
Anurag Arnab
Cordelia Schmid
OCL
LRM
ViT
28
3
0
17 Jul 2023
Neural Foundations of Mental Simulation: Future Prediction of Latent
  Representations on Dynamic Scenes
Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenes
Aran Nayebi
R. Rajalingham
M. Jazayeri
G. R. Yang
21
17
0
19 May 2023
TrackFormer: Multi-Object Tracking with Transformers
TrackFormer: Multi-Object Tracking with Transformers
Tim Meinhardt
A. Kirillov
Laura Leal-Taixe
Christoph Feichtenhofer
VOT
208
732
0
07 Jan 2021
On the Binding Problem in Artificial Neural Networks
On the Binding Problem in Artificial Neural Networks
Klaus Greff
Sjoerd van Steenkiste
Jürgen Schmidhuber
OCL
224
252
0
09 Dec 2020
Generative Neurosymbolic Machines
Generative Neurosymbolic Machines
Jindong Jiang
Sungjin Ahn
BDL
OCL
202
68
0
23 Oct 2020
Learning Object Permanence from Video
Learning Object Permanence from Video
Aviv Shamsian
Ofri Kleinfeld
Amir Globerson
Gal Chechik
SSL
29
31
0
23 Mar 2020
Disentangling Physical Dynamics from Unknown Factors for Unsupervised
  Video Prediction
Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction
Vincent Le Guen
Nicolas Thome
AI4CE
PINN
78
284
0
03 Mar 2020
A Compositional Object-Based Approach to Learning Physical Dynamics
A Compositional Object-Based Approach to Learning Physical Dynamics
Michael Chang
T. Ullman
Antonio Torralba
J. Tenenbaum
AI4CE
OCL
226
438
0
01 Dec 2016
Interaction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and Physics
Peter W. Battaglia
Razvan Pascanu
Matthew Lai
Danilo Jimenez Rezende
Koray Kavukcuoglu
AI4CE
OCL
PINN
GNN
255
1,394
0
01 Dec 2016
1