Zero-Shot Learning14 [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. [논문리뷰] A Semantic Encoding Out-of-Distribution Classifier for Generalized Zero-Shot Learning Paper Overview IEEE Signal Processing Letters'21 https://ieeexplore.ieee.org/document/9464653 Request Rejected ieeexplore.ieee.org Abstract 저자들은 새로운 semantic encoding out-of-distribution classifier (SE-OOD)를 제안한다. 본 방법은 먼저 semantically consistent mapping을 하여 모든 visual sample을 대응되는 semantic attribute에 project한다. 그 다음 projected visual sample과 원래 semantic attribute 둘다 distribution alignment를 위해 lat.. 2024. 3. 18. 이전 1 2 3 다음
