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Capsule networks aim to parse images into a hierarchy of objects, parts and relations.
Subjective contours
Kanizsa, G · 1976
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Principles of object perception
Spelke, E. S · 1990
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Real-time recognition with the entire brodatz texture database
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Representing moving images with layers
Wang, J. and Adelson, E. H · 1994
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Smoothness in layers: Motion segmentation using nonparametric mixture estima-tion
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Learning flexible sprites in video layers
Jojic, N. and Frey, B · 2001
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Part-based representations of visual shape and implications for visual cognition
Singh, M. and Hoffman, D · 2001
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A layered motion representation with occlusion and compact spatial support
Jepson, A., Fleet, D. J., and Black, M. J · 2002
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Transforming auto-encoders
Hinton, G. E., Krizhevsky, A., and Wang, S. D · 2011
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A century of gestalt psychology in visual perception: I. perceptual grouping and figure–ground organization
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2014
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Efficient inference in occlusion-aware generative models of images
Huang, J. and Murphy, K · 2016
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Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothnes
Jason, J., Harley, A., and Derpanis, K · 2016
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Self-normalizing neural networks
Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S · 2017
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Dynamic routing between capsules
Sabour, S., Frosst, N., and Hinton, G. E · 2017
Cited alongside, same era.
Sfm-net: Learning of structure and motion from video
Vijayanarasimhan, S., Ricco, S., Schmid, C., Sukthankar, R., and Fragkiadaki, K · 2017
Cited alongside, same era.
Videocapsulenet: A simplified network for action detection
Duarte, K., Rawat, Y., and Shah, M · 2018
Cited alongside, same era.
Matrix capsules with EM routing
Hinton, G. E., Sabour, S., and Frosst, N · 2018
Cited alongside, same era.
Capsules for object segmentation
LaLonde, R. and Bagci, U · 2018
Cited alongside, same era.
Self-supervised segmentation by grouping optical-flow
Mahendran, A., Thewlis, J., and Vedaldi, A · 2018
Occupancy networks: Learning 3d reconstruction in function space
Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., and Geiger, A · 2019
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On the spectral bias of neural networks
Rahaman, N., Baratin, A., Arpit, D., Draxler, F., Lin, M., Hamprecht, F., Bengio, Y., and Courville, A · 2019
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Explicit disentanglement of appearance and perspective in generative models
Skafte, N. and Hauberg, S · 2019
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Geometric capsule autoencoders for 3d point clouds
Srivastava, N., Goh, H., and Salakhutdinov, R · 2019
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Unsupervised discovery of parts, structure, and dynamics
Xu, Z., Liu, Z., Sun, C., Murphy, K., Freeman, W. T., Tenenbaum, J. B., and Wu, J · 2019
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3d point capsule networks
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Cited alongside, same era.
Sparse unsupervised capsules generalize better
Rawlinson, D., Ahmed, A., and Kowadlo, G · 2018
Cited alongside, same era.
Relational neural expectation maximization: Unsupervised discovery of objects and their interactions
Van Steenkiste, S., Chang, M., Greff, K., and Schmidhuber, J · 2018
Cited alongside, same era.
Star-caps: Capsule networks with straight-through attentive routing
Ahmed, K. and Torresani, L · 2019
Cited alongside, same era.
Monet: Unsupervised scene decomposition and representation
Burgess, C. P., Matthey, L., Watters, N., Kabra, R., Higgins, I., Botvinick, M., and Lerchner, A · 2019
Cited alongside, same era.
Learning Implicit Fields for Generative Shape Modeling
Chen, Z. and Zhang, H · 2019
Cited alongside, same era.
Shape reconstruction using differentiable projections and deep priors
Gadelha, M., Wang, R., and Maji, S · 2019
Cited alongside, same era.
Zhao, Y., Birdal, T., Deng, H., and Tombari, F · 2019
Later among the works it cites.
Sal: Sign agnostic learning of shapes from raw data
Atzmon, M. and Lipman, Y · 2020
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Frequency bias in neural networks for input of non-uniform density
Basri, R., Galun, M., Geifman, A., Jacobs, D., Kasten, Y., and Kritchman, S · 2020
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Learning physical graph representations from visual scenes
Bear, D., Fan, C., Mrowca, D., Li, Y., Alter, S., Nayebi, A., Schwartz, J., Fei-Fei, L. F., Wu, J., Tenenbaum, J., and Yamins, D. L · 2020
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Object-centric learning with slot attention
Locatello, F., Weissenborn, D., Unterthiner, T., Mahendran, A., Heigold, G., Uszkoreit, J., Dosovitskiy, A., and Kipf, T · 2020
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Canonical capsules: Unsupervised capsules in canonical pose
Sun, W., Tagliasacchi, A., Deng, B., Sabour, S., Yazdani, S., Hinton, G. E., and Yi, K. M · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Tancik, M., Srinivasan, P. P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J. T., and Ng, R · 2020
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Entity abstraction in visual model-based reinforcement learning
Veerapaneni, R., Co-Reyes, J. D., Chang, M., Janner, M., Finn, C., Wu, J., Tenenbaum, J., and Levine, S · 2020
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Generative scene graph networks
Deng, F., Zhi, Z., Lee, D., and Ahn, S · 2021
Closest in time.