Knowledge-guided semantic transfer network for few-shot image recognition
Deep learning-based models have been shown to outperform human beings in many
computer vision tasks with massive available labeled training data in learning. However …
computer vision tasks with massive available labeled training data in learning. However …
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 …
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 …
CLIP-guided prototype modulating for few-shot action recognition
Learning from large-scale contrastive language-image pre-training like CLIP has shown
remarkable success in a wide range of downstream tasks recently, but it is still under …
remarkable success in a wide range of downstream tasks recently, but it is still under …
Crest: Cross-modal resonance through evidential deep learning for enhanced zero-shot learning
Zero-shot learning (ZSL) enables the recognition of novel classes by leveraging semantic
knowledge transfer from known to unknown categories. This knowledge, typically …
knowledge transfer from known to unknown categories. This knowledge, typically …
Context disentangling and prototype inheriting for robust visual grounding
Visual grounding (VG) aims to locate a specific target in an image based on a given
language query. The discriminative information from context is important for distinguishing …
language query. The discriminative information from context is important for distinguishing …
Deep semantic-visual alignment for zero-shot remote sensing image scene classification
Deep neural networks have achieved promising progress in remote sensing (RS) image
classification, for which the training process requires abundant samples for each class …
classification, for which the training process requires abundant samples for each class …
Explanatory object part aggregation for zero-shot learning
Zero-shot learning (ZSL) aims to recognize objects from unseen classes only based on
labeled images from seen classes. Most existing ZSL methods focus on optimizing feature …
labeled images from seen classes. Most existing ZSL methods focus on optimizing feature …
A closer look at few-shot 3d point cloud classification
In recent years, research on few-shot learning (FSL) has been fast-growing in the 2D image
domain due to the less requirement for labeled training data and greater generalization for …
domain due to the less requirement for labeled training data and greater generalization for …
TransZero++: Cross attribute-guided transformer for zero-shot learning
Zero-shot learning (ZSL) tackles the novel class recognition problem by transferring
semantic knowledge from seen classes to unseen ones. Semantic knowledge is typically …
semantic knowledge from seen classes to unseen ones. Semantic knowledge is typically …