Pre-trained vision-language models (VLMs) have achieved promising success in
many fields, especially with prompt learning paradigm. 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. The soft prompt in GIPCOL is structured and
consists of the prefix learnable vectors, attribute label and object label. In
addition, the attribute and object labels in the soft prompt are designated as
nodes in a compositional graph. The compositional graph is constructed based on
the compositional structure of the objects and attributes extracted from the
training data and consequently feeds the updated concept representation into
the soft prompt to capture this compositional structure for a better prompting
for CZSL. With the new prompting strategy, GIPCOL achieves state-of-the-art AUC
results on all three CZSL benchmarks, including MIT-States, UT-Zappos, and
C-GQA datasets in both closed and open settings compared to previous non-CLIP
as well as CLIP-based methods. We analyze when and why GIPCOL operates well
given the CLIP backbone and its training data limitations, and our findings
shed light on designing more effective prompts for CZSL

我们提出了 GIP-COL（注入图结构的软提示用于组合学习），在提示为基础的学习框架内更好地探索预训练视觉 - 语言模型（VLMs）的组合式零样本学习（CZSL）能力。使用新的提示策略，GIPCOL 在三个 CZSL 基准测试中取得了最新的 AUC 结果，包括 MIT-States、UT-Zappos 和 C-GQA 数据集，相比之前的非 CLIP 和基于 CLIP 方法，在封闭和开放设置下都取得了最新的结果。我们的研究结果具有启发意义，可为 CZSL 的有效提示设计提供指导。