Zero-Shot Learning15 [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]Bridging Language and Geometric Primitives for Zero-shot Point Cloud Segme Paper OverviewACM MM'23https://arxiv.org/abs/2210.09923 Zero-shot point cloud segmentation by transferring geometric primitivesWe investigate transductive zero-shot point cloud semantic segmentation, where the network is trained on seen objects and able to segment unseen objects. The 3D geometric elements are essential cues to imply a novel 3D object type. However, previous methodarxiv.orgAbstra.. 2025. 3. 20. [논문리뷰] MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning MathJax = { tex: {inlineMath: [['$', '$'], ['\\(', '\\)']]} }; Paper OverviewCVPR'22https://openaccess.thecvf.com/content/CVPR2022/html/Chen_MSDN_Mutually_Semantic_Distillation_Network_for_Zero-Shot_Learning_CVPR_2022_paper.html CVPR 2022 Open Access RepositoryMSDN: Mutually Semantic Distillation Network for Zero-Shot Learning Shiming Chen, Ziming Hong, Guo-Sen Xie, Wenhan Yang, Qinmu .. 2024. 4. 22. [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] FREE: Feature Refinement for Generalized Zero-Shot Learning Paper Overview ICCV'21 https://arxiv.org/abs/2107.13807 FREE: Feature Refinement for Generalized Zero-Shot Learning Generalized zero-shot learning (GZSL) has achieved significant progress, with many efforts dedicated to overcoming the problems of visual-semantic domain gap and seen-unseen bias. However, most existing methods directly use feature extraction models traine arxiv.org Abstract 대부분 존재.. 2024. 4. 11. [논문리뷰] Adaptive Confidence Smoothing for Generalized Zero-Shot Learning Paper Overview CVPR'19 https://arxiv.org/abs/1812.09903 Adaptive Confidence Smoothing for Generalized Zero-Shot Learning Generalized zero-shot learning (GZSL) is the problem of learning a classifier where some classes have samples and others are learned from side information, like semantic attributes or text description, in a zero-shot learning fashion (ZSL). Training a sing arxiv.org Abstract 저.. 2024. 3. 25. 이전 1 2 3 다음
