Fetching the paper…
Reading the bibliography…
We investigate transductive zero-shot point cloud semantic segmentation, where the network is trained on seen objects and able to segment unseen objects.
Context-aware feature generation for zero-shot semantic segmentation. In Proceedings of the 28th ACM International Conference on Multimedia . 1921–1929
Zhangxuan Gu, Siyuan Zhou, Li Niu, Zihan Zhao, and Liqing Zhang. 2020 · 1929
Earlier work this paper cites.
Topic modeling: beyond bag-of-words. In Proceedings of the 23rd international conference on Machine learning . 977–984
Hanna M Wallach. 2006 · 2006
Earlier work this paper cites.
Hubs in space: Popular nearest neighbors in high-dimensional data
Milos Radovanovic, Alexandros Nanopoulos, and Mirjana Ivanovic. 2010 · 2010
Earlier work this paper cites.
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 · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013b · 2013
Earlier work this paper cites.
Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) . 1532–1543
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Earlier work this paper cites.
Ridge regression, hubness, and zero-shot learning. In Joint European conference on machine learning and knowledge discovery in databases . Springer, 135–151
Yutaro Shigeto, Ikumi Suzuki, Kazuo Hara, Masashi Shimbo, and Yuji Matsumoto. 2015 · 2015
Earlier work this paper cites.
Joint 2d-3d-semantic data for indoor scene understanding
Iro Armeni, Sasha Sax, Amir R Zamir, and Silvio Savarese. 2017 · 2017
Earlier work this paper cites.
ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. In Proc. Computer Vision and Pattern Recognition (CVPR), IEEE
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. 2017 · 2017
Earlier work this paper cites.
Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition . 652–660
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. 2017 · 2017
Earlier work this paper cites.
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 · 2018
Earlier work this paper cites.
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences. In Proc. of the IEEE/CVF International Conf. on Computer Vision (ICCV)
J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall. 2019 · 2019
Earlier work this paper cites.
Zero-shot semantic segmentation
Maxime Bucher, Tuan-Hung Vu, Matthieu Cord, and Patrick Pérez. 2019 · 2019
Earlier work this paper cites.
nuScenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. 2019 · 2019
Earlier work this paper cites.
Mitigating the hubness problem for zero-shot learning of 3d objects
Ali Cheraghian, Shafin Rahman, Dylan Campbell, and Lars Petersson. 2019b · 2019
Cited alongside, same era.
Zero-shot learning of 3d point cloud objects. In 2019 16th International Conference on Machine Vision Applications (MVA) . IEEE, 1–6
Ali Cheraghian, Shafin Rahman, and Lars Petersson. 2019a · 2019
Cited alongside, same era.
4d spatio-temporal convnets: Minkowski convolutional neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 3075–3084
Christopher Choy, JunYoung Gwak, and Silvio Savarese. 2019 · 2019
Cited alongside, same era.
Paraphrase generation with latent bag of words
Yao Fu, Yansong Feng, and John P Cunningham. 2019 · 2019
Cited alongside, same era.
Kpconv: Flexible and deformable convolution for point clouds. In Proceedings of the IEEE/CVF international conference on computer vision . 6411–6420
Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds. In 2021 International Conference on 3D Vision (3DV) . IEEE, 992–1002
Björn Michele, Alexandre Boulch, Gilles Puy, Maxime Bucher, and Renaud Marlet. 2021 · 2021
Later among the works it cites.
Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 16024–16033
Jianyun Xu, Ruixiang Zhang, Jian Dou, Yushi Zhu, Jie Sun, and Shiliang Pu. 2021 · 2021
Later among the works it cites.
Prototypical matching and open set rejection for zero-shot semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 6974–6983
Hui Zhang and Henghui Ding. 2021 · 2021
Later among the works it cites.
Cylindrical and asymmetrical 3d convolution networks for lidar segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 9939–9948
Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Yuexin Ma, Wei Li, Hongsheng Li, and Dahua Lin. 2021 · 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…
Hugues Thomas, Charles R Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, and Leonidas J Guibas. 2019 · 2019
Cited alongside, same era.
Unsupervised learning of intrinsic structural representation points. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 9121–9130
Nenglun Chen, Lingjie Liu, Zhiming Cui, Runnan Chen, Duygu Ceylan, Changhe Tu, and Wenping Wang. 2020 · 2020
Cited alongside, same era.
Transductive zero-shot learning for 3d point cloud classification. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision . 923–933
Ali Cheraghian, Shafin Rahman, Dylan Campbell, and Lars Petersson. 2020 · 2020
Cited alongside, same era.
MMDetection3D: OpenMMLab next-generation platform for general 3D object detection
MMDetection3D Contributors. 2020 · 2020
Cited alongside, same era.
Uncertainty-aware learning for zero-shot semantic segmentation
Ping Hu, Stan Sclaroff, and Kate Saenko. 2020a · 2020
Cited alongside, same era.
Consistent structural relation learning for zero-shot segmentation
Peike Li, Yunchao Wei, and Yi Yang. 2020 · 2020
Cited alongside, same era.
Learning unbiased zero-shot semantic segmentation networks via transductive transfer
Fengmao Lv, Haiyang Liu, Yichen Wang, Jiayi Zhao, and Guowu Yang. 2020 · 2020
Cited alongside, same era.
Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li. 2020 · 2020
Cited alongside, same era.
Towards 3d scene understanding by referring synthetic models
Runnan Chen, Xinge Zhu, Nenglun Chen, Dawei Wang, Wei Li, Yuexin Ma, Ruigang Yang, and Wenping Wang. 2022 · 2022
Closest in time.
Studies on attention modeling for visual understanding
Runnan Chen. 2023 · 2023
Closest in time.
Towards Label-free Scene Understanding by Vision Foundation Models
Runnan Chen, Youquan Liu, Lingdong Kong, Nenglun Chen, Xinge Zhu, Yuexin Ma, Tongliang Liu, and Wenping Wang. 2023a · 2023
Closest in time.
Rethinking range view representation for lidar segmentation
Lingdong Kong, Youquan Liu, Runnan Chen, Yuexin Ma, Xinge Zhu, Yikang Li, Yuenan Hou, Yu Qiao, and Ziwei Liu. 2023a · 2023
Closest in time.
Benchmarking 3D Perception Robustness to Common Corruptions and Sensor Failure. In International Conference on Learning Representations 2023 Workshop on Scene Representations for Autonomous Driving
Lingdong Kong, Youquan Liu, Xin Li, Runnan Chen, Wenwei Zhang, Jiawei Ren, Liang Pan, Kai Chen, and Ziwei Liu. 2023b · 2023
Closest in time.
Robo3d: Towards robust and reliable 3d perception against corruptions
Lingdong Kong, Youquan Liu, Xin Li, Runnan Chen, Wenwei Zhang, Jiawei Ren, Liang Pan, Kai Chen, and Ziwei Liu. 2023c · 2023
Closest in time.
Segment Any Point Cloud Sequences by Distilling Vision Foundation Models
Youquan Liu, Lingdong Kong, Jun Cen, Runnan Chen, Wenwei Zhang, Liang Pan, Kai Chen, and Ziwei Liu. 2023 · 2023
Closest in time.
See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data
Yuhang Lu, Qi Jiang, Runnan Chen, Yuenan Hou, Xinge Zhu, and Yuexin Ma. 2023 · 2023
Closest in time.
Human-centric Scene Understanding for 3D Large-scale Scenarios
Yiteng Xu, Peishan Cong, Yichen Yao, Runnan Chen, Yuenan Hou, Xinge Zhu, Xuming He, Jingyi Yu, and Yuexin Ma. 2023 · 2023
Closest in time.
Learning a deep embedding model for zero-shot learning. In Proceedings of the IEEE conference on computer vision and pattern recognition . 2021–2030
Li Zhang, Tao Xiang, and Shaogang Gong. 2017 · 2030
Closest in time.