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While there has been a number of studies on Zero-Shot Learning (ZSL) for 2D images, its application to 3D data is still recent and scarce, with just a few methods limited to classification.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Retrieving articulated 3-d models using medial surfaces
Kaleem Siddiqi, Juan Zhang, Diego Macrini, Ali Shokoufandeh, Sylvain Bouix, and Sven Dickinson · 2008
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Describing objects by their attributes
Ali Farhadi, Ian Endres, Derek Hoiem, and David Forsyth · 2009
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Learning to detect unseen object classes by between-class attribute transfer
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2009
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Hubs in space: Popular nearest neighbors in high-dimensional data
Milos Radovanovic, Alexandros Nanopoulos, and Mirjana Ivanovic · 2010
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Recognizing human actions by attributes
Jingen Liu, Benjamin Kuipers, and Silvio Savarese · 2011
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Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
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Generalized denoising auto-encoders as generative models
Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent · 2013
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Devise: A deep visual-semantic embedding model
Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc’Aurelio Ranzato, and Tomas Mikolov · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Zero-shot learning by convex combination of semantic embeddings
Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg S Corrado, and Jeffrey Dean · 2013
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Zero-shot learning through cross-modal transfer
Richard Socher, Milind Ganjoo, Christopher D Manning, and Andrew Ng · 2013
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Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2014
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Learning rich features from RGB-D images for object detection and segmentation
Saurabh Gupta, Ross Girshick, Pablo Arbeláez, and Jitendra Malik · 2014
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Zero-shot recognition with unreliable attributes
Dinesh Jayaraman and Kristen Grauman · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
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Accelerating t-sne using tree-based algorithms
Laurens Van Der Maaten · 2014
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Generative moment matching networks
Yujia Li, Kevin Swersky, and Rich Zemel · 2015
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Non-rigid 3D Shape Retrieval
Z. Lian, J. Zhang, S. Choi, H. ElNaghy, J. El-Sana, T. Furuya, A. Giachetti, R. A. Guler, L. Lai, C. Li, H. Li, F. A. Limberger, R. Martin, R. U. Nakanishi, A. P. Neto, L. G. Nonato, R. Ohbuchi, K. Pevzner, D. Pickup, P. Rosin, A. Sharf, L. Sun, X. Sun, S. Tari, G. Unal, and R. C. Wilson · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
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VoxNet: A 3D convolutional neural network for real-time object recognition
Daniel Maturana and Sebastian Scherer · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Ridge regression, hubness, and zero-shot learning
Yutaro Shigeto, Ikumi Suzuki, Kazuo Hara, Masashi Shimbo, and Yuji Matsumoto · 2015
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Multi-view convolutional neural networks for 3D shape recognition
Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller · 2015
Earlier work this paper cites.
3D ShapeNets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
Earlier work this paper cites.
Recovering the missing link: Predicting class-attribute associations for unsupervised zero-shot learning
Ziad Al-Halah, Makarand Tapaswi, and Rainer Stiefelhagen · 2016
Earlier work this paper cites.
3D semantic parsing of large-scale indoor spaces
Iro Armeni, Ozan Sener, Amir R Zamir, Helen Jiang, Ioannis Brilakis, Martin Fischer, and Silvio Savarese · 2016
Earlier work this paper cites.
An empirical study and analysis of generalized zero-shot learning for object recognition in the wild
Wei-Lun Chao, Soravit Changpinyo, Boqing Gong, and Fei Sha · 2016
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2016
Cited alongside, same era.
Volumetric and multi-view CNNs for object classification on 3D data
Charles R Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J Guibas · 2016
Cited alongside, same era.
Latent embeddings for zero-shot classification
Yongqin Xian, Zeynep Akata, Gaurav Sharma, Quynh Nguyen, Matthias Hein, and Bernt Schiele · 2016
Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly
Yongqin Xian, Christoph H Lampert, Bernt Schiele, and Zeynep Akata · 2018
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Feature generating networks for zero-shot learning
Yongqin Xian, Tobias Lorenz, Bernt Schiele, and Zeynep Akata · 2018
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SpiderCNN: Deep learning on point sets with parameterized convolutional filters
Yifan Xu, Tianqi Fan, Mingye Xu, Long Zeng, and Yu Qiao · 2018
Later among the works it cites.
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall · 2019
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Zero-shot semantic segmentation
Maxime Bucher, Tuan-Hung Vu, Matthieu Cord, and Patrick Pérez · 2019
Later among the works it cites.
Mitigating the hubness problem for zero-shot learning of 3d objects
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Cited alongside, same era.
Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
Cited alongside, same era.
Generating visual representations for zero-shot classification
Maxime Bucher, Stephane Herbin, and Frederic Jurie · 2017
Cited alongside, same era.
Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
Cited alongside, same era.
Ali Cheraghian, Shafin Rahman, Dylan Campbell, and Lars Petersson · 2019
Later among the works it cites.
Zero-shot learning of 3d point cloud objects
Ali Cheraghian, Shafin Rahman, and Lars Petersson · 2019
Later among the works it cites.
Zero-shot semantic segmentation via variational mapping
Naoki Kato, Toshihiko Yamasaki, and Kiyoharu Aizawa · 2019
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Relation-shape convolutional neural network for point cloud analysis
Yongcheng Liu, Bin Fan, Shiming Xiang, and Chunhong Pan · 2019
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Generalized zero-and few-shot learning via aligned variational autoencoders
Edgar Schonfeld, Sayna Ebrahimi, Samarth Sinha, Trevor Darrell, and Zeynep Akata · 2019
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KPConv: Flexible and deformable convolution for point clouds
Hugues Thomas, Charles R Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, and Leonidas J Guibas · 2019
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A survey of zero-shot learning: Settings, methods, and applications
Wei Wang, Vincent W Zheng, Han Yu, and Chunyan Miao · 2019
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PointConv: Deep convolutional networks on 3D point clouds
Wenxuan Wu, Zhongang Qi, and Li Fuxin · 2019
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Semantic projection network for zero- and few-label semantic segmentation
Yongqin Xian, Subhabrata Choudhury, Yang He, Bernt Schiele, and Zeynep Akata · 2019
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Semantic projection network for zero-and few-label semantic segmentation
Yongqin Xian, Subhabrata Choudhury, Yang He, Bernt Schiele, and Zeynep Akata · 2019
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f-vaegan-d2: A feature generating framework for any-shot learning
Yongqin Xian, Saurabh Sharma, Bernt Schiele, and Zeynep Akata · 2019
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ConvPoint: Continuous convolutions for point cloud processing
Alexandre Boulch · 2020
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FKAConv: Feature-kernel alignment for point cloud convolution
Alexandre Boulch, Gilles Puy, and Renaud Marlet · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Transductive zero-shot learning for 3D point cloud classification
Ali Cheraghian, Shafin Rahman, Dylan Campbell, and Lars Petersson · 2020
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Context-aware feature generation for zero-shot semantic segmentation
Zhangxuan Gu, Siyuan Zhou, Li Niu, Zihan Zhao, and Liqing Zhang · 2020
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Momentum Contrast for Unsupervised Visual Representation Learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick · 2020
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Uncertainty-aware learning for zero-shot semantic segmentation
Ping Hu, Stan Sclaroff, and Kate Saenko · 2020
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Consistent structural relation learning for zero-shot segmentation
Peike Li, Yunchao Wei, and Yi Yang · 2020
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Zero-shot learning and its applications from autonomous vehicles to covid-19 diagnosis: A review
Mahdi Rezaei and Mahsa Shahidi · 2020
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Sign: Spatial-information incorporated generative network for generalized zero-shot semantic segmentation
Jiaxin Cheng, Soumyaroop Nandi, Prem Natarajan, and Wael Abd-Almageed · 2021
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Zero-shot learning on 3d point cloud objects and beyond
Ali Cheraghian, Shafinn Rahman, Townim F Chowdhury, Dylan Campbell, and Lars Petersson · 2021
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Segmenting 3d hybrid scenes via zero-shot learning
Bo Liu, Qiulei Dong, and Zhanyi Hu · 2021
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