Zero-Shot Learning14 [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] Generalized Zero-Shot Learning Via Over-Complete Distribution Paper Overview CVPR'20 https://arxiv.org/abs/2004.00666 Generalized Zero-Shot Learning Via Over-Complete Distribution A well trained and generalized deep neural network (DNN) should be robust to both seen and unseen classes. However, the performance of most of the existing supervised DNN algorithms degrade for classes which are unseen in the training set. To learn a discr arxiv.org Abstract Zero.. 2024. 3. 11. [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. [논문리뷰] HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning Paper Overview NeurIPS'21 https://arxiv.org/abs/2109.15163 HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. Typically, to guarantee desirable knowledge transfer, a common (latent) space is adopted for associating the visual and arxiv.org Abs.. 2024. 2. 27. [논문리뷰] Autoencoder based novelty detection for generalized zero shot learning 이 논문은 autoencoder 기반 novelty detection을 위한 논문이다. ICIP'19 https://ieeexplore.ieee.org/document/8803562 Autoencoder based novelty detection for generalized zero shot learning The problem of generalized zero-shot learning deals with the classification of test examples for which training data may or may not be available. Existing baseline algorithms connect the seen and unseen set of categories by l.. 2024. 2. 5. [논문리뷰] Zero-Shot Point Cloud Segmentation by Semantic-Visual Aware Synthesis 이 논문은Point clouds Inductive Generalized Zero shot semantic segmentatation분야의 2번째 논문이다.unseen feature 생성에 초점을 두었다.3DGenZ 논문이 나온 후 2년만에 후속 연구가 발표된 것이라아주 귀한 논문이라 할수 있다. ICCV'23https://openaccess.thecvf.com/content/ICCV2023/html/Yang_Zero-Shot_Point_Cloud_Segmentation_by_Semantic-Visual_Aware_Synthesis_ICCV_2023_paper.html ICCV 2023 Open Access RepositoryZero-Shot Point Cloud Segmentation by Semanti.. 2024. 1. 23. 이전 1 2 3 다음
