Zero-Shot Learning/3D Classification4 [논문리뷰]Contrastive Generative Network with Recursive-Loop for 3D point cloud generalized zero-shot classification Paper OverviewPattern Recognition' 23https://www.sciencedirect.com/science/article/pii/S0031320323005411Abstract저자들은 discriminative point cloud feature 합성을 다룬다.Contrastive Generative Network with Recursive-Loop (CGRL)을 제안하여,feature의 inter-class 거리는 늘리고 intra-class 차이는 줄인다.KeywordsGenerlized Zero-Shot Learning, Zero-Shot LearningProposed Method1. Problem definitionsInductive GZSL이다.training set: .. 2025. 7. 4. [논문리뷰] Zero-Shot Learning on 3D Point Cloud Objects and Beyond Paper OverviewIJCV'22https://arxiv.org/abs/2104.04980 Zero-Shot Learning on 3D Point Cloud Objects and BeyondZero-shot learning, the task of learning to recognize new classes not seen during training, has received considerable attention in the case of 2D image classification. However, despite the increasing ubiquity of 3D sensors, the corresponding 3D point cloudarxiv.org Abstract저자들은 3D ZSL에 대한.. 2024. 4. 16. [논문리뷰] Transductive Zero-Shot Learning for 3D Point Cloud Classification Paper OverviewWACV'20https://arxiv.org/abs/1912.07161 Transductive Zero-Shot Learning for 3D Point Cloud ClassificationZero-shot learning, the task of learning to recognize new classes not seen during training, has received considerable attention in the case of 2D image classification. However despite the increasing ubiquity of 3D sensors, the corresponding 3D point cloudarxiv.org Abstract이 논문은 .. 2024. 3. 12. [논문리뷰] Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects Paper OverviewBMVC'19https://arxiv.org/abs/1907.06371 Mitigating the Hubness Problem for Zero-Shot Learning of 3D ObjectsThe development of advanced 3D sensors has enabled many objects to be captured in the wild at a large scale, and a 3D object recognition system may therefore encounter many objects for which the system has received no training. Zero-Shot Learning (ZSL) apparxiv.org Abstract3D .. 2024. 3. 5. 이전 1 다음
