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Found 4 papers, 2 papers with code
Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignment
However, with only a few training images, there exist two crucial problems: (1) the visual feature distributions are easily distracted by class-irrelevant information in images, and (2) the alignment between the visual and language feature distributions is difficult.
Hierarchical Prompt Learning for Multi-Task Learning
Considering this, we present Hierarchical Prompt (HiPro) learning, a simple and effective method for jointly adapting a pre-trained VLM to multiple downstream tasks.
Self-Supervision Can Be a Good Few-Shot Learner
Specifically, we maximize the mutual information (MI) of instances and their representations with a low-bias MI estimator to perform self-supervised pre-training.
cross-domain few-shot learning
Unsupervised Few-Shot Image Classification
+1
Prompt Distribution Learning
We present prompt distribution learning for effectively adapting a pre-trained vision-language model to address downstream recognition tasks.
