Domain Prompt Learning with Quaternion Networks

• URL: http://arxiv.org/abs/2312.08878v1
• Date: Tue, 12 Dec 2023 08:49:39 GMT
• Title: Domain Prompt Learning with Quaternion Networks
• Authors: Qinglong Cao, Zhengqin Xu, Yuntian Chen, Chao Ma, Xiaokang Yang
• Abstract summary: We propose to leverage domain-specific knowledge from domain-specific foundation models to transfer the robust recognition ability of Vision-Language Models to specialized domains. We present a hierarchical approach that generates vision prompt features by analyzing intermodal relationships between hierarchical language prompt features and domain-specific vision features. Our proposed method achieves new state-of-the-art results in prompt learning.
• Score: 49.45309818782329
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompt learning has emerged as an effective and data-efficient technique in large Vision-Language Models (VLMs). However, when adapting VLMs to specialized domains such as remote sensing and medical imaging, domain prompt learning remains underexplored. While large-scale domain-specific foundation models can help tackle this challenge, their concentration on a single vision level makes it challenging to prompt both vision and language modalities. To overcome this, we propose to leverage domain-specific knowledge from domain-specific foundation models to transfer the robust recognition ability of VLMs from generalized to specialized domains, using quaternion networks. Specifically, the proposed method involves using domain-specific vision features from domain-specific foundation models to guide the transformation of generalized contextual embeddings from the language branch into a specialized space within the quaternion networks. Moreover, we present a hierarchical approach that generates vision prompt features by analyzing intermodal relationships between hierarchical language prompt features and domain-specific vision features. In this way, quaternion networks can effectively mine the intermodal relationships in the specific domain, facilitating domain-specific vision-language contrastive learning. Extensive experiments on domain-specific datasets show that our proposed method achieves new state-of-the-art results in prompt learning.

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