Fetching the paper…
Reading the bibliography…
Unsupervised object-centric learning aims to represent the modular, compositional, and causal structure of a scene as a set of object representations and thereby promises to resolve many critical limitations of traditional single-vector representations such as poor systematic generalization.
Exploiting spatial invariance for scalable unsupervised object tracking
Crawford, E. and Pineau, J · 1911
Earlier work this paper cites.
Unsupervised discovery of 3d physical objects from video
Du, Y., Smith, K. A., Ulman, T., Tenenbaum, J. B., and Wu, J · 2007
Earlier work this paper cites.
Object segmentation by long term analysis of point trajectories
Brox, T. and Malik, J · 2010
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y · 2014
Earlier work this paper cites.
Describing textures in the wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., , and Vedaldi, A · 2014
Earlier work this paper cites.
It’s moving! a probabilistic model for causal motion segmentation in moving camera videos
Bideau, P. and Learned-Miller, E. G · 2016
Earlier work this paper cites.
Attend, infer, repeat: Fast scene understanding with generative models
Eslami, S. A., Heess, N., Weber, T., Tassa, Y., Szepesvari, D., and Hinton, G. E · 2016
Earlier work this paper cites.
Tagger: Deep unsupervised perceptual grouping
Greff, K., Rasmus, A., Berglund, M., Hao, T., Valpola, H., and Schmidhuber, J · 2016
Earlier work this paper cites.
Neural expectation maximization
Greff, K., van Steenkiste, S., and Schmidhuber, J · 2017
Earlier work this paper cites.
Unsupervised deep learning for optical flow estimation
Ren, Z., Yan, J., Ni, B., Liu, B., Yang, X., and Zha, H · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Unsupervised learning of multi-frame optical flow with occlusions
Janai, J., Güney, F., Ranjan, A., Black, M. J., and Geiger, A · 2018
Earlier work this paper cites.
Sequential attend, infer, repeat: Generative modelling of moving objects
Kosiorek, A., Kim, H., Teh, Y. W., and Posner, I · 2018
Earlier work this paper cites.
Relational neural expectation maximization: Unsupervised discovery of objects and their interactions
Van Steenkiste, S., Chang, M., Greff, K., and Schmidhuber, J · 2018
Earlier work this paper cites.
Tracking emerges by colorizing videos
Vondrick, C., Shrivastava, A., Fathi, A., Guadarrama, S., and Murphy, K. P · 2018
Earlier work this paper cites.
Occlusion aware unsupervised learning of optical flow
Wang, Y., Yang, Y., Yang, Z., Zhao, L., and Xu, W · 2018
Earlier work this paper cites.
Structured agents for physical construction
Bapst, V., Sanchez-Gonzalez, A., Doersch, C., Stachenfeld, K. L., Kohli, P., Battaglia, P. W., and Hamrick, J. B · 2019
Earlier work this paper cites.
Monet: Unsupervised scene decomposition and representation
Burgess, C. P., Matthey, L., Watters, N., Kabra, R., Higgins, I., Botvinick, M., and Lerchner, A · 2019
Earlier work this paper cites.
Multi-object representation learning with iterative variational inference
Greff, K., Kaufmann, R. L., Kabra, R., Watters, N., Burgess, C., Zoran, D., Matthey, L., Botvinick, M., and Lerchner, A · 2019
Earlier work this paper cites.
Tracking by animation: Unsupervised learning of multi-object attentive trackers
He, Z., Li, J., Liu, D., He, H., and Barber, D · 2019
Earlier work this paper cites.
Contrastive learning of structured world models
Kipf, T., van der Pol, E., and Welling, M · 2019
Earlier work this paper cites.
Self-supervised learning for video correspondence flow
Lai, Z. and Xie, W · 2019
Earlier work this paper cites.
Video object segmentation using space-time memory networks
Oh, S. W., Lee, J.-Y., Xu, N., and Kim, S. J · 2019
Earlier work this paper cites.
R-sqair: relational sequential attend, infer, repeat
Stanić, A. and Schmidhuber, J · 2019
Earlier work this paper cites.
Entity abstraction in visual model-based reinforcement learning
Veerapaneni, R., Co-Reyes, J. D., Chang, M., Janner, M., Finn, C., Wu, J., Tenenbaum, J. B., and Levine, S · 2019
Earlier work this paper cites.
Unsupervised deep tracking
Wang, N., Song, Y., Ma, C., gang Zhou, W., Liu, W., and Li, H · 2019
Earlier work this paper cites.
Learning correspondence from the cycle-consistency of time
Wang, X., Jabri, A., and Efros, A. A · 2019
Earlier work this paper cites.
Watters, N., Matthey, L., Bosnjak, M., Burgess, C. P., and Lerchner, A · 2019
Cited alongside, same era.
Unsupervised moving object detection via contextual information separation
Yang, Y., Loquercio, A., Scaramuzza, D., and Soatto, S · 2019
Cited alongside, same era.
Object-centric image generation with factored depths, locations, and appearances
Anciukevicius, T., Lampert, C. H., and Henderson, P · 2020
Cited alongside, same era.
Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Cited alongside, same era.
Roots: Object-centric representation and rendering of 3d scenes
Chen, C., Deng, F., and Ahn, S · 2021
Later among the works it cites.
Unsupervised object-based transition models for 3d partially observable environments
Creswell, A., Kabra, R., Burgess, C., and Shanahan, M · 2021
Later among the works it cites.
Generalization and robustness implications in object-centric learning
Dittadi, A., Papa, S., De Vita, M., Schölkopf, B., Winther, O., and Locatello, F · 2021
Later among the works it cites.
Genesis-v2: Inferring unordered object representations without iterative refinement
Engelcke, M., Jones, O. P., and Posner, I · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Carvalho, W., Liang, A., Lee, K., Sohn, S., Lee, H., Lewis, R. L., and Singh, S · 2020
Cited alongside, same era.
Learning 3d object-oriented world models from unlabeled videos
Crawford, E. and Pineau, J · 2020
Cited alongside, same era.
Alignnet: Unsupervised entity alignment
Creswell, A., Nikiforou, K., Vinyals, O., Saraiva, A., Kabra, R., Matthey, L., Burgess, C., Reynolds, M., Tanburn, R., Garnelo, M., et al · 2020
Cited alongside, same era.
Generative scene graph networks
Deng, F., Zhi, Z., Lee, D., and Ahn, S · 2020
Cited alongside, same era.
Unsupervised discovery of 3d physical objects from video
Du, Y., Smith, K., Ulman, T., Tenenbaum, J., and Wu, J · 2020
Cited alongside, same era.
Genesis: Generative scene inference and sampling with object-centric latent representations
Engelcke, M., Kosiorek, A. R., Jones, O. P., and Posner, I · 2020
Cited alongside, same era.
CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning
Girdhar, R. and Ramanan, D · 2020
Cited alongside, same era.
On the binding problem in artificial neural networks
Greff, K., van Steenkiste, S., and Schmidhuber, J · 2020
Cited alongside, same era.
Kabra, R., Zoran, D., Erdogan, G., Matthey, L., Creswell, A., Botvinick, M., Lerchner, A., and Burgess, C. P · 2021
Later among the works it cites.
Clevrtex: A texture-rich benchmark for unsupervised multi-object segmentation
Karazija, L., Laina, I., and Rupprecht, C · 2021
Later among the works it cites.
Conditional Object-Centric Learning from Video
Kipf, T., Elsayed, G. F., Mahendran, A., Stone, A., Sabour, S., Heigold, G., Jonschkowski, R., Dosovitskiy, A., and Greff, K · 2021
Later among the works it cites.
Transformers with competitive ensembles of independent mechanisms
Lamb, A., He, D., Goyal, A., Ke, G., Liao, C.-F., Ravanelli, M., and Bengio, Y · 2021
Later among the works it cites.
Information-theoretic segmentation by inpainting error maximization
Savarese, P., Kim, S. S. Y., Maire, M., Shakhnarovich, G., and McAllester, D · 2021
Later among the works it cites.
Toward causal representation learning
Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., and Bengio, Y · 2021
Later among the works it cites.
Structured world belief for reinforcement learning in pomdp
Singh, G., Peri, S., Kim, J., Kim, H., and Ahn, S · 2021
Later among the works it cites.
Decomposing 3d scenes into objects via unsupervised volume segmentation
Stelzner, K., Kersting, K., and Kosiorek, A. R · 2021
Later among the works it cites.
Smurf: Self-teaching multi-frame unsupervised raft with full-image warping
Stone, A., Maurer, D., Ayvaci, A., Angelova, A., and Jonschkowski, R · 2021
Later among the works it cites.
Unsupervised object learning via common fate
Tangemann, M., Schneider, S., von Kügelgen, J., Locatello, F., Gehler, P., Brox, T., Kümmerer, M., Bethge, M., and Schölkopf, B · 2021
Later among the works it cites.
Generative video transformer: Can objects be the words?
Wu, Y.-F., Yoon, J., and Ahn, S · 2021
Later among the works it cites.
Self-supervised video object segmentation by motion grouping
Yang, C., Lamdouar, H., Lu, E., Zisserman, A., and Xie, W · 2021
Later among the works it cites.
Unsupervised foreground extraction via deep region competition
Yu, P., Xie, S., Ma, X., Zhu, Y., Wu, Y. N., and Zhu, S.-C · 2021
Later among the works it cites.
Hopper: Multi-hop transformer for spatiotemporal reasoning
Zhou, H., Kadav, A., Lai, F., Niculescu-Mizil, A., Min, M. R., Kapadia, M., and Graf, H. P · 2021
Later among the works it cites.
Parts: Unsupervised segmentation with slots, attention and independence maximization
Zoran, D., Kabra, R., Lerchner, A., and Rezende, D. J · 2021
Later among the works it cites.
Discovering objects that can move
Bao, Z., Tokmakov, P., Jabri, A., Wang, Y.-X., Gaidon, A., and Hebert, M · 2022
Closest in time.
Blocks assemble! learning to assemble with large-scale structured reinforcement learning
Ghasemipour, S. K. S., Freeman, D., David, B., Kataoka, S., Mordatch, I., et al · 2022
Closest in time.
Kubric: A scalable dataset generator
Greff, K., Belletti, F., Beyer, L., Doersch, C., Du, Y., Duckworth, D., Fleet, D., Gnanapragasam, D., Golemo, F., Herrmann, C., Kipf, T., Kundu, A., Lagun, D., Laradji, I. H., Liu, H.-T., Meyer, H., Miao, Y., Nowrouzezahrai, D., Oztireli, C., Pot, E., Radwan, N., Rebain, D., Sabour, S., Sajjadi, M. S. M., Sela, M., Sitzmann, V., Stone, A., Sun, D., Vora, S., Wang, Z., Wu, T., Yi, K. M., Zhong, F., and Tagliasacchi, A · 2022
Closest in time.
Complex-valued autoencoders for object discovery
Lowe, S., Lippe, P., Rudolph, M. R., and Welling, M · 2022
Closest in time.
Compositional multi-object reinforcement learning with linear relation networks
Mambelli, D., Träuble, F., Bauer, S., Schölkopf, B., and Locatello, F · 2022
Closest in time.
Illiterate dall-e learns to compose
Singh, G., Deng, F., and Ahn, S · 2022
Closest in time.
Self-supervised transformers for unsupervised object discovery using normalized cut
Wang, Y., Shen, X., Hu, S. X., Yuan, Y., Crowley, J. L., and Vaufreydaz, D · 2022
Closest in time.