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EnglishPoint cloud processing and 3D shape understanding are challenging tasks for which deep learning techniques have demonstrated great potentials. Still further progresses are essential to allow artificial intelligent agents to interact with the real world. In many practical conditions the amount of annotated data may be limited and integrating new sources of knowledge becomes crucial to support autonomous learning. Here we consider several scenarios involving synthetic and real world point clouds where supervised learning fails due to data scarcity and large domain gaps. We propose to enrich standard feature representations by leveraging self-supervision through a multi-task model that can solve a 3D puzzle while learning the main task of shape classification or part segmentation. An extensive analysis investigating few-shot, transfer learning and cross-domain settings shows the effectiveness of our approach with state-of-the-art results.
Joint supervised and self-supervised learning for 3D real world challenges / Alliegro, A.; Boscaini, D.; Tommasi, T.. - (2020), pp. 6718-6725. ((Intervento presentato al convegno 25th International Conference on Pattern Recognition, ICPR 2020 tenutosi a Milan (Ita) nel 10-15 Jan. 2021 [10.1109/ICPR48806.2021.9412483].
| Titolo: | Joint supervised and self-supervised learning for 3D real world challenges | |
| Autori: | ||
| Data di pubblicazione: | 2020 | |
| Serie: | ||
| Abstract: | Point cloud processing and 3D shape understanding are challenging tasks for which deep learning t...echniques have demonstrated great potentials. Still further progresses are essential to allow artificial intelligent agents to interact with the real world. In many practical conditions the amount of annotated data may be limited and integrating new sources of knowledge becomes crucial to support autonomous learning. Here we consider several scenarios involving synthetic and real world point clouds where supervised learning fails due to data scarcity and large domain gaps. We propose to enrich standard feature representations by leveraging self-supervision through a multi-task model that can solve a 3D puzzle while learning the main task of shape classification or part segmentation. An extensive analysis investigating few-shot, transfer learning and cross-domain settings shows the effectiveness of our approach with state-of-the-art results. | |
| ISBN: | 978-1-7281-8808-9 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| preprint_Joint_Supervised_and_Self-Supervised_Learning_for_3D_Real_World_Challenges.pdf | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri | |
| Joint_Supervised_and_Self-Supervised_Learning_for_3D_Real_World_Challenges.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2923372
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