A review of generalized zero-shot learning methods
Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples
under the condition that some output classes are unknown during supervised learning. To …
under the condition that some output classes are unknown during supervised learning. To …
Fine-grained zero-shot learning: Advances, challenges, and prospects
Recent zero-shot learning (ZSL) approaches have integrated fine-grained analysis, ie, fine-
grained ZSL, to mitigate the commonly known seen/unseen domain bias and misaligned …
grained ZSL, to mitigate the commonly known seen/unseen domain bias and misaligned …
Msdn: Mutually semantic distillation network for zero-shot learning
The key challenge of zero-shot learning (ZSL) is how to infer the latent semantic knowledge
between visual and attribute features on seen classes, and thus achieving a desirable …
between visual and attribute features on seen classes, and thus achieving a desirable …
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 …
semantic knowledge from seen classes to unseen ones. Typically, to guarantee desirable …
Transzero: Attribute-guided transformer for zero-shot learning
Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic
knowledge from seen classes to unseen ones. Semantic knowledge is learned from attribute …
knowledge from seen classes to unseen ones. Semantic knowledge is learned from attribute …
Open-vocabulary instance segmentation via robust cross-modal pseudo-labeling
Open-vocabulary instance segmentation aims at segmenting novel classes without mask
annotations. It is an important step toward reducing laborious human supervision. Most …
annotations. It is an important step toward reducing laborious human supervision. Most …
Progressive semantic-visual mutual adaption for generalized zero-shot learning
Abstract Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge
transferred from the seen domain, relying on the intrinsic interactions between visual and …
transferred from the seen domain, relying on the intrinsic interactions between visual and …
Tsca: On the semantic consistency alignment via conditional transport for compositional zero-shot learning
Compositional Zero-Shot Learning (CZSL) aims to recognize novel\textit {state-object}
compositions by leveraging the shared knowledge of their primitive components. Despite …
compositions by leveraging the shared knowledge of their primitive components. Despite …
Duet: Cross-modal semantic grounding for contrastive zero-shot learning
Zero-shot learning (ZSL) aims to predict unseen classes whose samples have never
appeared during training. One of the most effective and widely used semantic information for …
appeared during training. One of the most effective and widely used semantic information for …
Non-generative generalized zero-shot learning via task-correlated disentanglement and controllable samples synthesis
Y Feng, X Huang, P Yang, J Yu… - Proceedings of the …, 2022 - openaccess.thecvf.com
Synthesizing pseudo samples is currently the most effective way to solve the Generalized
Zero Shot Learning (GZSL) problem. Most models achieve competitive performance but still …
Zero Shot Learning (GZSL) problem. Most models achieve competitive performance but still …