3D Compositional Zero-shot Learning with DeCompositional Consensus
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[1] Tatsuya Harada,et al. Unsupervised Pose-Aware Part Decomposition for 3D Articulated Objects , 2021, ArXiv.
[2] Alexandre Boulch,et al. Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds , 2021, 2021 International Conference on 3D Vision (3DV).
[3] Yongqin Xian,et al. Learning Graph Embeddings for Open World Compositional Zero-Shot Learning , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] Kui Jia,et al. 3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Kai Xu,et al. Learning Fine-Grained Segmentation of 3D Shapes without Part Labels , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Federico Tombari,et al. Learning Graph Embeddings for Compositional Zero-shot Learning , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Muhammad Ferjad Naeem,et al. Open World Compositional Zero-Shot Learning , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Xiaojuan Qi,et al. Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud , 2020, AAAI.
[9] Zi Huang,et al. Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches , 2020, ACM Multimedia.
[10] Gal Chechik,et al. A causal view of compositional zero-shot recognition , 2020, NeurIPS.
[11] Dacheng Tao,et al. Learning Unseen Concepts via Hierarchical Decomposition and Composition , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Andreas Geiger,et al. Learning Unsupervised Hierarchical Part Decomposition of 3D Objects From a Single RGB Image , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Cewu Lu,et al. Symmetry and Group in Attribute-Object Compositions , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Kaichun Mo,et al. Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories , 2020, ICLR.
[15] Ravi Teja Mullapudi,et al. Learning to Move with Affordance Maps , 2020, ICLR.
[16] Lars Petersson,et al. Transductive Zero-Shot Learning for 3D Point Cloud Classification , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).
[17] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[18] Hao Zhang,et al. BSP-Net: Generating Compact Meshes via Binary Space Partitioning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Geoffrey E. Hinton,et al. CvxNet: Learnable Convex Decomposition , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Matthias Nießner,et al. RIO: 3D Object Instance Re-Localization in Changing Indoor Environments , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[21] Lars Petersson,et al. Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects , 2019, BMVC.
[22] Bernt Schiele,et al. Semantic Projection Network for Zero- and Few-Label Semantic Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Marc'Aurelio Ranzato,et al. Task-Driven Modular Networks for Zero-Shot Compositional Learning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[24] Andreas Geiger,et al. Superquadrics Revisited: Learning 3D Shape Parsing Beyond Cuboids , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Alexandre Boulch. ConvPoint: Continuous convolutions for point cloud processing , 2019, Comput. Graph..
[26] Siddhartha Chaudhuri,et al. BAE-NET: Branched Autoencoder for Shape Co-Segmentation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[27] Jan Kautz,et al. Putting Humans in a Scene: Learning Affordance in 3D Indoor Environments , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Lars Petersson,et al. Zero-shot Learning of 3D Point Cloud Objects , 2019, 2019 16th International Conference on Machine Vision Applications (MVA).
[29] Federico Tombari,et al. 3D Point Capsule Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Leonidas J. Guibas,et al. PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Abhinav Gupta,et al. Videos as Space-Time Region Graphs , 2018, ECCV.
[32] Kristen Grauman,et al. Attributes as Operators , 2018, ECCV.
[33] Yue Wang,et al. Dynamic Graph CNN for Learning on Point Clouds , 2018, ACM Trans. Graph..
[34] Geoffrey E. Hinton,et al. Dynamic Routing Between Capsules , 2017, NIPS.
[35] Christoph H. Lampert,et al. Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[36] Martial Hebert,et al. From Red Wine to Red Tomato: Composition with Context , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Leonidas J. Guibas,et al. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.
[38] Davide Modolo,et al. Objects as Context for Detecting Their Semantic Parts , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[39] Leonidas J. Guibas,et al. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Leonidas J. Guibas,et al. Learning Shape Abstractions by Assembling Volumetric Primitives , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[41] Leonidas J. Guibas,et al. A scalable active framework for region annotation in 3D shape collections , 2016, ACM Trans. Graph..
[42] Davide Modolo,et al. Do Semantic Parts Emerge in Convolutional Neural Networks? , 2016, International Journal of Computer Vision.
[43] Silvio Savarese,et al. 3D Semantic Parsing of Large-Scale Indoor Spaces , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[44] Bernt Schiele,et al. Learning Deep Representations of Fine-Grained Visual Descriptions , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Michael S. Bernstein,et al. Image retrieval using scene graphs , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Edward H. Adelson,et al. Discovering states and transformations in image collections , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Kristen Grauman,et al. Fine-Grained Visual Comparisons with Local Learning , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[48] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[49] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[50] Cordelia Schmid,et al. Label-Embedding for Attribute-Based Classification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[51] Andrew Y. Ng,et al. Zero-Shot Learning Through Cross-Modal Transfer , 2013, NIPS.
[52] Andrew W. Fitzgibbon,et al. KinectFusion: Real-time dense surface mapping and tracking , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.
[53] Thomas A. Funkhouser,et al. A benchmark for 3D mesh segmentation , 2009, ACM Trans. Graph..
[54] Christoph H. Lampert,et al. Learning to detect unseen object classes by between-class attribute transfer , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[55] Yoshua Bengio,et al. Zero-data Learning of New Tasks , 2008, AAAI.
[56] David A. McAllester,et al. A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[57] A Treves,et al. Neural networks in the brain involved in memory and recall. , 1993, Progress in brain research.
[58] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[59] I. Biederman. Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.
[60] Donald D. Hoffman,et al. Parts of recognition , 1984, Cognition.
[61] Geoffrey E. Hinton. Some Demonstrations of the Effects of Structural Descriptions in Mental Imagery , 1979, Cogn. Sci..
[62] Franc Solina,et al. Segmentation and Recovery of Superquadrics , 2000, Computational Imaging and Vision.
[63] J. van Pelt,et al. The self-organizing brain : from growth cones to functional networks ; proceedings of the 18th International Summer School of Brain Research, held at the University of Amsterdam and the Academic Medical Center (The Netherlands) from 23 to 27 August 1993 , 1994 .
[64] E. Rolls,et al. Neural networks in the brain involved in memory and recall , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).