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Found 4 papers, 1 papers with code
GIPCOL: Graph-Injected Soft Prompting for Compositional Zero-Shot Learning
In this work, we propose GIP-COL (Graph-Injected Soft Prompting for COmpositional Learning) to better explore the compositional zero-shot learning (CZSL) ability of VLMs within the prompt-based learning framework.
MetaReVision: Meta-Learning with Retrieval for Visually Grounded Compositional Concept Acquisition
Inspired by this observation, in this paper, we propose MetaReVision, a retrieval-enhanced meta-learning model to address the visually grounded compositional concept learning problem.
Prompting Large Pre-trained Vision-Language Models For Compositional Concept Learning
This work explores the zero-shot compositional learning ability of large pre-trained vision-language models(VLMs) within the prompt-based learning framework and propose a model (\textit{PromptCompVL}) to solve the compositonal zero-shot learning (CZSL) problem.
Zero-Shot Compositional Concept Learning
In this paper, we study the problem of recognizing compositional attribute-object concepts within the zero-shot learning (ZSL) framework.
