Visual and Semantic Prompt Collaboration for Generalized Zero-Shot Learning

• URL: http://arxiv.org/abs/2503.23030v1
• Date: Sat, 29 Mar 2025 10:17:57 GMT
• Title: Visual and Semantic Prompt Collaboration for Generalized Zero-Shot Learning
• Authors: Huajie Jiang, Zhengxian Li, Xiaohan Yu, Yongli Hu, Baocai Yin, Jian Yang, Yuankai Qi,
• Abstract summary: Generalized zero-shot learning aims to recognize both seen and unseen classes with the help of semantic information that is shared among different classes.<n>Existing approaches fine-tune the visual backbone by seen-class data to obtain semantic-related visual features.<n>This paper proposes a novel visual and semantic prompt collaboration framework, which utilizes prompt tuning techniques for efficient feature adaptation.
• Score: 58.73625654718187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generalized zero-shot learning aims to recognize both seen and unseen classes with the help of semantic information that is shared among different classes. It inevitably requires consistent visual-semantic alignment. Existing approaches fine-tune the visual backbone by seen-class data to obtain semantic-related visual features, which may cause overfitting on seen classes with a limited number of training images. This paper proposes a novel visual and semantic prompt collaboration framework, which utilizes prompt tuning techniques for efficient feature adaptation. Specifically, we design a visual prompt to integrate the visual information for discriminative feature learning and a semantic prompt to integrate the semantic formation for visualsemantic alignment. To achieve effective prompt information integration, we further design a weak prompt fusion mechanism for the shallow layers and a strong prompt fusion mechanism for the deep layers in the network. Through the collaboration of visual and semantic prompts, we can obtain discriminative semantic-related features for generalized zero-shot image recognition. Extensive experiments demonstrate that our framework consistently achieves favorable performance in both conventional zero-shot learning and generalized zero-shot learning benchmarks compared to other state-of-the-art methods.

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