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Found 5 papers, 2 papers with code
DPGN: Distribution Propagation Graph Network for Few-shot Learning
To combine the distribution-level relations and instance-level relations for all examples, we construct a dual complete graph network which consists of a point graph and a distribution graph with each node standing for an example.
Ranked #2 on
Few-Shot Learning
on Mini-ImageNet - 1-Shot Learning
Will Multi-modal Data Improves Few-shot Learning?
Most few-shot learning models utilize only one modality of data.
Injecting Image Details into CLIP's Feature Space
Although CLIP-like Visual Language Models provide a functional joint feature space for image and text, due to the limitation of the CILP-like model's image input size (e. g., 224), subtle details are lost in the feature representation if we input high-resolution images (e. g., 2240).
Introducing Vision Transformer for Alzheimer's Disease classification task with 3D input
Many high-performance classification models utilize complex CNN-based architectures for Alzheimer's Disease classification.
RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing
Moreover, we present an image-text paired dataset in the field of remote sensing (RS), RS5M, which has 5 million RS images with English descriptions.
Ranked #1 on
Cross-Modal Retrieval
on RSITMD
(using extra training data)
