딥러닝 공부방

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 FREE, Feature Refinement for Generaliz..

Task-Indenpendent Knowledge Makes for Transferable Represenations for Generalized Zero-Shot Learning https://arxiv.org/abs/2104.01832 Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning Generalized Zero-Shot Learning (GZSL) targets recognizing new categories by learning transferable image representations. Existing methods find that, by aligning im..

Zero-Shot Learning via Contrastive Learning on Dual Knowledge Graphs https://ieeexplore.ieee.org/document/9607851 Zero-Shot Learning via Contrastive Learning on Dual Knowledge Graphs Graph Convolutional Networks (GCNs), which can integrate both explicit knowledge and implicit knowledge together, have shown effectively for zero-shot learning problems. Previous GCN-based methods generally leverage..

Isometric Propagation Network for Generalized Zero-Shot Learning https://arxiv.org/abs/2102.02038 Isometric Propagation Network for Generalized Zero-shot Learning Zero-shot learning (ZSL) aims to classify images of an unseen class only based on a few attributes describing that class but no access to any training sample. A popular strategy is to learn a mapping between the semantic space of class..

Convolutional Prototype Learning for Zero-Shot Recognition(2019) https://arxiv.org/abs/1910.09728 Convolutional Prototype Learning for Zero-Shot Recognition Zero-shot learning (ZSL) has received increasing attention in recent years especially in areas of fine-grained object recognition, retrieval, and image captioning. The key to ZSL is to transfer knowledge from the seen to the unseen classes v..

Class-Prototype Discriminative Network for Generalized Zero-Shot Learning https://ieeexplore.ieee.org/abstract/document/8966463 Class-Prototype Discriminative Network for Generalized Zero-Shot Learning We present a novel end-to-end deep metric learning model named Class-Prototype Discriminative Network (CPDN) for Generalized Zero-Shot Learning (GZSL). It consists of a generative network for prod..

Prototypical Matching and Open Seg Rejection for Zero-Shot Semantic Segmentation https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_Prototypical_Matching_and_Open_Set_Rejection_for_Zero-Shot_Semantic_Segmentation_ICCV_2021_paper Seen과 unknown 을 구분하는 segmentation model을 학습한 후에 unknown에 대하여 unseen class를 예측한다. unseen class를 쉽게 확장하기 위하여 일반적인 classifier가 아닌 prototype matching 방법을 제안한다. sema..

https://arxiv.org/abs/1908.05832 Transferable Contrastive Network for Generalized Zero-Shot Learning Zero-shot learning (ZSL) is a challenging problem that aims to recognize the target categories without seen data, where semantic information is leveraged to transfer knowledge from some source classes. Although ZSL has made great progress in recent years, arxiv.org Transferable Contrastive Networ..

https://arxiv.org/abs/2103.16173 Contrastive Embedding for Generalized Zero-Shot Learning Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and unseen classes, when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing vis arxiv.org Zero shot Learning은 보통 embedding based me..

Scaling Up Vision-Language Representation Learning with Noisy Text Supervision https://arxiv.org/abs/2102.05918 Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision Pre-trained representations are becoming crucial for many NLP and perception tasks. While representation learning in NLP has transitioned to training on raw text without human annotations, visual ..