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 …
Zero-shot referring image segmentation with global-local context features
Referring image segmentation (RIS) aims to find a segmentation mask given a referring
expression grounded to a region of the input image. Collecting labelled datasets for this …
expression grounded to a region of the input image. Collecting labelled datasets for this …
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 …
A zero-shot fault semantics learning model for compound fault diagnosis
J Xu, S Liang, X Ding, R Yan - Expert Systems with Applications, 2023 - Elsevier
Compound fault diagnosis of bearings has always been a challenge, due to the occurrence
of various faults with randomness and complexity. Existing deep learning-based methods …
of various faults with randomness and complexity. Existing deep learning-based methods …
Hybrid routing transformer for zero-shot learning
Zero-shot learning (ZSL) aims to learn models that can recognize unseen image semantics
based on the training of data with seen semantics. Recent studies either leverage the global …
based on the training of data with seen semantics. Recent studies either leverage the global …
Intra-modal proxy learning for zero-shot visual categorization with clip
Vision-language pre-training methods, eg, CLIP, demonstrate an impressive zero-shot
performance on visual categorizations with the class proxy from the text embedding of the …
performance on visual categorizations with the class proxy from the text embedding of the …
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 …
Improving zero-shot generalization for clip with variational adapter
The excellent generalization capability of pre-trained Vision-Language Models (VLMs)
makes fine-tuning VLMs for downstream zero-shot tasks a popular choice. Despite …
makes fine-tuning VLMs for downstream zero-shot tasks a popular choice. Despite …
Learning adversarial semantic embeddings for zero-shot recognition in open worlds
Abstract Zero-Shot Learning (ZSL) focuses on classifying samples of unseen classes with
only their side semantic information presented during training. It cannot handle real-life …
only their side semantic information presented during training. It cannot handle real-life …