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PartImageNet and Pascal Part datasets
PartImageNet and Pascal Part datasets for few-shot part segmentation. -
PartSeg: Few-shot Part Segmentation via Part-aware Prompt Learning
Few-shot part segmentation using few-shot support images and pre-trained image-language model CLIP. -
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
Gaussian processes (GPs) are non-parametric, flexible models that work well in many tasks. Combining GPs with deep learning methods via deep kernel learning (DKL) is especially... -
ScanObjectNN
Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen... -
Few-Shot Class-Incremental Learning
Few-Shot Class-Incremental Learning (FSCIL) is a special case of Class-Incremental Learning (CIL), where only a few training examples are available at every learning session. -
Multimodal Parameter-Efficient Few-Shot Class Incremental Learning
Few-Shot Class Incremental Learning (FSCIL) is a challenging continual learning task, where limited training examples are available during several learning sessions. -
Human Connectome Project (HCP) dataset
The Human Connectome Project (HCP) dataset contains volumetric task fMRI activation maps from the Human Connectome Project 1200 dataset (HCP1200) distribution, for the 965...
