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Current visual detectors, though impressive within their training distribution, often fail to parse out-of-distribution scenes into their constituent entities.
The hungarian method for the assignment problem
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Objective criteria for the evaluation of clustering methods
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Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
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Microsoft coco: Common objects in context
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ShapeNet: An Information-Rich 3D Model Repository
Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., and Yu, F · 2015
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Attend, infer, repeat: Fast scene understanding with generative models
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Deep reconstruction-classification networks for unsupervised domain adaptation
Ghifary, M., Kleijn, W. B., Zhang, M., Balduzzi, D., and Li, W · 2016
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Tagger: Deep unsupervised perceptual grouping
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Context encoders: Feature learning by inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., and Efros, A. A · 2016
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Johnson, J., Hariharan, B., Van Der Maaten, L., Fei-Fei, L., Lawrence Zitnick, C., and Girshick, R · 2017
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Dynamic routing between capsules
Sabour, S., Frosst, N., and Hinton, G. E · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Sequential attend, infer, repeat: Generative modelling of moving objects
Kosiorek, A., Kim, H., Teh, Y. W., and Posner, I · 2018
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Meta-learning for semi-supervised few-shot classification
Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., and Zemel, R. S · 2018
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Relational neural expectation maximization: Unsupervised discovery of objects and their interactions
Van Steenkiste, S., Chang, M., Greff, K., and Schmidhuber, J · 2018
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Monet: Unsupervised scene decomposition and representation
Burgess, C. P., Matthey, L., Watters, N., Kabra, R., Higgins, I., Botvinick, M., and Lerchner, A · 2019
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Domain-specific batch normalization for unsupervised domain adaptation
Chang, W.-G., You, T., Seo, S., Kwak, S., and Han, B · 2019
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Genesis: Generative scene inference and sampling with object-centric latent representations
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Multi-object representation learning with iterative variational inference
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Multi-object datasets
Kabra, R., Burgess, C., Matthey, L., Kaufman, R. L., Greff, K., Reynolds, M., and Lerchner, A · 2019
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Stacked capsule autoencoders
Kosiorek, A., Sabour, S., Teh, Y. W., and Hinton, G. E · 2019
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Occupancy networks: Learning 3d reconstruction in function space
Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., and Geiger, A · 2019
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Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding
Mo, K., Zhu, S., Chang, A. X., Yi, L., Tripathi, S., Guibas, L. J., and Su, H · 2019
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Learning to infer and execute 3d shape programs
Tian, Y., Luo, A., Sun, X., Ellis, K., Freeman, W. T., Tenenbaum, J. B., and Wu, J · 2019
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Spatial broadcast decoder: A simple architecture for learning disentangled representations in vaes
Recurrent independent mechanisms
Goyal, A., Lamb, A., Hoffmann, J., Sodhani, S., Levine, S., Bengio, Y., and Schölkopf, B · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
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Test-time classifier adjustment module for model-agnostic domain generalization
Iwasawa, Y. and Matsuo, Y · 2021
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Clevrtex: A texture-rich benchmark for unsupervised multi-object segmentation
Karazija, L., Laina, I., and Rupprecht, C · 2021
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Systematic evaluation of causal discovery in visual model based reinforcement learning
Ke, N. R., Didolkar, A., Mittal, S., Goyal, A., Lajoie, G., Bauer, S., Rezende, D., Bengio, Y., Mozer, M., and Pal, C · 2021
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Watters, N., Matthey, L., Burgess, C. P., and Lerchner, A · 2019
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Messytable: Instance association in multiple camera views
Cai, Z., Zhang, J., Ren, D., Yu, C., Zhao, H., Yi, S., Yeo, C. K., and Change Loy, C · 2020
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End-to-end object detection with transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
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Reconstruction bottlenecks in object-centric generative models
Engelcke, M., Jones, O. P., and Posner, I · 2020
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Shortcut learning in deep neural networks
Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., and Wichmann, F. A · 2020
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Object files and schemata: Factorizing declarative and procedural knowledge in dynamical systems
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Sita: Single image test-time adaptation
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Coconets: Continuous contrastive 3d scene representations, 2021
Lal, S., Prabhudesai, M., Mediratta, I., Harley, A. W., and Fragkiadaki, K · 2021
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pixelnerf: Neural radiance fields from one or few images
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A survey of unsupervised domain adaptation for visual recognition
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Point transformer
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In-place scene labelling and understanding with implicit scene representation
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Parts: Unsupervised segmentation with slots, attention and independence maximization
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Mt3: Meta test-time training for self-supervised test-time adaption
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Test-time adaptation with shape moments for image segmentation
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Google scanned objects: A high-quality dataset of 3d scanned household items
Downs, L., Francis, A., Koenig, N., Kinman, B., Hickman, R., Reymann, K., McHugh, T. B., and Vanhoucke, V · 2022
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Test-time training with masked autoencoders
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Kubric: A scalable dataset generator
Greff, K., Belletti, F., Beyer, L., Doersch, C., Du, Y., Duckworth, D., Fleet, D. J., Gnanapragasam, D., Golemo, F., Herrmann, C., et al · 2022
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Masked autoencoders are scalable vision learners
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Geonerf: Generalizing nerf with geometry priors
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Mask3d for 3d semantic instance segmentation
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Mm-tta: Multi-modal test-time adaptation for 3d semantic segmentation
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