{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T14:20:42Z","timestamp":1756995642396,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T00:00:00Z","timestamp":1717027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,30]]},"DOI":"10.1145\/3652583.3658073","type":"proceedings-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T06:30:40Z","timestamp":1717741840000},"page":"257-265","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["NeurNCD: Novel Class Discovery via Implicit Neural Representation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2271-8270","authenticated-orcid":false,"given":"Junming","family":"Wang","sequence":"first","affiliation":[{"name":"The University of Hong Kong, Hong Kong SAR, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7948-1906","authenticated-orcid":false,"given":"Yi","family":"Shi","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,6,7]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00539"},{"key":"e_1_3_2_1_2_1","volume-title":"SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene Understanding. In Robotics Research","author":"Blum Hermann","year":"2023","unstructured":"Hermann Blum, Marcus G M\u00fcller, Abel Gawel, Roland Siegwart, and Cesar Cadena. 2023. SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene Understanding. In Robotics Research. Springer, 119--135."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"e_1_3_2_1_4_1","volume-title":"UK","author":"Chen Xiaokang","year":"2020","unstructured":"Xiaokang Chen, Kwan-Yee Lin, Jingbo Wang, Wayne Wu, Chen Qian, Hongsheng Li, and Gang Zeng. 2020. Bi-directional cross-modality feature propagation with separation-and-aggregation gate for RGB-D semantic segmentation. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XI. Springer, 561--577."},{"key":"e_1_3_2_1_5_1","volume-title":"Struct-NeRF: Neural Radiance Fields for Indoor Scenes with Structural Hints. arXiv preprint arXiv:2209.05277","author":"Chen Zheng","year":"2022","unstructured":"Zheng Chen, Chen Wang, Yuan-Chen Guo, and Song-Hai Zhang. 2022. Struct-NeRF: Neural Radiance Fields for Indoor Scenes with Structural Hints. arXiv preprint arXiv:2209.05277 (2022)."},{"key":"e_1_3_2_1_6_1","unstructured":"Camille Couprie Cl\u00e9ment Farabet Laurent Najman and Yann LeCun. 2013. Indoor semantic segmentation using depth information. (2013)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3203812"},{"key":"e_1_3_2_1_8_1","volume-title":"Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation. arXiv preprint arXiv:2203.15224","author":"Fu Xiao","year":"2022","unstructured":"Xiao Fu, Shangzhan Zhang, Tianrun Chen, Yichong Lu, Lanyun Zhu, Xiaowei Zhou, Andreas Geiger, and Yiyi Liao. 2022. Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation. arXiv preprint arXiv:2203.15224 (2022)."},{"key":"e_1_3_2_1_9_1","volume-title":"2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 6835--6842","author":"Furrer Fadri","year":"2018","unstructured":"Fadri Furrer, Tonci Novkovic, Marius Fehr, Abel Gawel, Margarita Grinvald, Torsten Sattler, Roland Siegwart, and Juan Nieto. 2018. Incremental object data-base: Building 3d models from multiple partial observations. In 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 6835--6842."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00543"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0777-6"},{"key":"e_1_3_2_1_12_1","volume-title":"Learning the k in k-means. Advances in neural information processing systems 16","author":"Hamerly Greg","year":"2003","unstructured":"Greg Hamerly and Charles Elkan. 2003. Learning the k in k-means. Advances in neural information processing systems 16 (2003)."},{"key":"e_1_3_2_1_13_1","volume-title":"Adam: A method for stochastic optimization.","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. (2014)."},{"key":"e_1_3_2_1_14_1","volume-title":"CTNet: Context-based tandem network for semantic segmentation","author":"Li Zechao","year":"2021","unstructured":"Zechao Li, Yanpeng Sun, Liyan Zhang, and Jinhui Tang. 2021. CTNet: Context-based tandem network for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)."},{"key":"e_1_3_2_1_15_1","volume-title":"CMX: Cross-modal fusion for RGB-X semantic segmentation with transformers. arXiv preprint arXiv:2203.04838","author":"Liu Huayao","year":"2022","unstructured":"Huayao Liu, Jiaming Zhang, Kailun Yang, Xinxin Hu, and Rainer Stiefelhagen. 2022. CMX: Cross-modal fusion for RGB-X semantic segmentation with transformers. arXiv preprint arXiv:2203.04838 (2022)."},{"key":"e_1_3_2_1_16_1","first-page":"870","article-title":"Del: Deep embedding learning for efficient image segmentation","volume":"864","author":"Liu Yun","year":"2018","unstructured":"Yun Liu, Peng-Tao Jiang, Vahan Petrosyan, Shi-Jie Li, Jiawang Bian, Le Zhang 0001, and Ming-Ming Cheng. 2018. Del: Deep embedding learning for efficient image segmentation.. In IJCAI, Vol. 864. 870.","journal-title":"IJCAI"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503250"},{"key":"e_1_3_2_1_18_1","volume-title":"Instant neural graphics primitives with a multiresolution hash encoding. arXiv preprint arXiv:2201.05989","author":"M\u00fcller Thomas","year":"2022","unstructured":"Thomas M\u00fcller, Alex Evans, Christoph Schied, and Alexander Keller. 2022. Instant neural graphics primitives with a multiresolution hash encoding. arXiv preprint arXiv:2201.05989 (2022)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00106"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2018.8593993"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00943"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48506.2021.9561675"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298655"},{"key":"e_1_3_2_1_25_1","unstructured":"Julian Straub Thomas Whelan Lingni Ma Yufan Chen Erik Wijmans Simon Green Jakob J Engel Raul Mur-Artal Carl Ren Shobhit Verma et al. 2019. The Replica dataset: A digital replica of indoor spaces. arXiv preprint arXiv:1906.05797 (2019)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2913555"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2015.7354011"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01258"},{"key":"e_1_3_2_1_29_1","volume-title":"GPU Technology Conference","volume":"1","author":"Vanholder Han","year":"2016","unstructured":"Han Vanholder. 2016. Efficient inference with tensorrt. In GPU Technology Conference, Vol. 1. 2."},{"key":"e_1_3_2_1_30_1","volume-title":"Etienne Pot, Andrea Tagliasacchi, and Daniel Duckworth.","author":"Vora Suhani","year":"2021","unstructured":"Suhani Vora, Noha Radwan, Klaus Greff, Henning Meyer, Kyle Genova, Mehdi SM Sajjadi, Etienne Pot, Andrea Tagliasacchi, and Daniel Duckworth. 2021. Nesf: Neural semantic fields for generalizable semantic segmentation of 3d scenes. arXiv preprint arXiv:2111.13260 (2021)."},{"key":"e_1_3_2_1_31_1","volume-title":"R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis. arXiv preprint arXiv:2203.17261","author":"Wang Huan","year":"2022","unstructured":"Huan Wang, Jian Ren, Zeng Huang, Kyle Olszewski, Menglei Chai, Yun Fu, and Sergey Tulyakov. 2022. R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis. arXiv preprint arXiv:2203.17261 (2022)."},{"key":"e_1_3_2_1_32_1","volume-title":"AGRNav: Efficient and Energy-Saving Autonomous Navigation for Air-Ground Robots in Occlusion-Prone Environments. arXiv preprint arXiv:2403.11607","author":"Wang Junming","year":"2024","unstructured":"Junming Wang, Zekai Sun, Xiuxian Guan, Tianxiang Shen, Zongyuan Zhang, Tianyang Duan, Dong Huang, Shixiong Zhao, and Heming Cui. 2024. AGRNav: Efficient and Energy-Saving Autonomous Navigation for Air-Ground Robots in Occlusion-Prone Environments. arXiv preprint arXiv:2403.11607 (2024)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/202"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01187"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548088"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00536"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2005.845141"},{"key":"e_1_3_2_1_38_1","first-page":"2358","article-title":"Convolutional prototype network for open set recognition","volume":"44","author":"Yang Hong-Ming","year":"2020","unstructured":"Hong-Ming Yang, Xu-Yao Zhang, Fei Yin, Qing Yang, and Cheng-Lin Liu. 2020. Convolutional prototype network for open set recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5 (2020), 2358--2370.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_1_39_1","volume-title":"Deep Markov Clustering for Panoptic Segmentation. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2380--2384","author":"Ye Minxiang","year":"2022","unstructured":"Minxiang Ye, Yifei Zhang, Shiqiang Zhu, Anhuan Xie, and Dan Zhang. 2022. Deep Markov Clustering for Panoptic Segmentation. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2380--2384."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00430"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01554"},{"key":"e_1_3_2_1_42_1","volume-title":"iLabel: Revealing Objects in Neural Fields","author":"Zhi Shuaifeng","year":"2022","unstructured":"Shuaifeng Zhi, Edgar Sucar, Andre Mouton, Iain Haughton, Tristan Laidlow, and Andrew J Davison. 2022. iLabel: Revealing Objects in Neural Fields. IEEE Robotics and Automation Letters (2022)."},{"key":"e_1_3_2_1_43_1","volume-title":"Open3D: A modern library for 3D data processing. arXiv preprint arXiv:1801.09847","author":"Zhou Qian-Yi","year":"2018","unstructured":"Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun. 2018. Open3D: A modern library for 3D data processing. arXiv preprint arXiv:1801.09847 (2018)."}],"event":{"name":"ICMR '24: International Conference on Multimedia Retrieval","sponsor":["SIGMM ACM Special Interest Group on Multimedia","SIGSOFT ACM Special Interest Group on Software Engineering"],"location":"Phuket Thailand","acronym":"ICMR '24"},"container-title":["Proceedings of the 2024 International Conference on Multimedia Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652583.3658073","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3652583.3658073","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T08:52:59Z","timestamp":1755766379000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652583.3658073"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,30]]},"references-count":43,"alternative-id":["10.1145\/3652583.3658073","10.1145\/3652583"],"URL":"https:\/\/doi.org\/10.1145\/3652583.3658073","relation":{},"subject":[],"published":{"date-parts":[[2024,5,30]]},"assertion":[{"value":"2024-06-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}