5 papers
A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class
Qian Qiao, Yu Xie, Shaoyao Huang +1
Few-shot image classification aims to classify novel classes with few labeled samples. Recent research indicates that deep local descriptors have better representational capabiliti…
TALDS-Net: Task-Aware Adaptive Local Descriptors Selection for Few-shot Image Classification
Qian Qiao, Yu Xie, Ziyin Zeng +1
Few-shot image classification aims to classify images from unseen novel classes with few samples. Recent works demonstrate that deep local descriptors exhibit enhanced representati…
PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification
Meijuan Su, Feihong He, Fanzhang Li
Few-shot image classification has received considerable attention for overcoming the challenge of limited classification performance with limited samples in novel classes. Most exi…
Cartoondiff: Training-free Cartoon Image Generation with Diffusion Transformer Models
Feihong He, Gang Li, Lingyu Si +4
Image cartoonization has attracted significant interest in the field of image generation. However, most of the existing image cartoonization techniques require re-training models u…
Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier
Zhao Zhang, Weiming Jiang, Jie Qin +4
In this paper, we propose an analysis mechanism based structured Analysis Discriminative Dictionary Learning (ADDL) framework. ADDL seamlessly integrates the analysis discriminativ…