Supporting Vision-Language Model Inference with Confounder-pruning Knowledge Prompt
This work addresses the challenge of enhancing transferability in vision-language models for tasks like domain generalization, representing an incremental improvement over existing prompting techniques.
The paper tackles the problem of improving vision-language model inference by addressing the lack of semantic information in existing prompt methods, proposing CPKP, a semantic-aware prompt learning method that retrieves and refines task-relevant semantic information from an ontological knowledge graph, resulting in an average performance gain of 4.64% over manual prompts and 1.09% over learnable prompts with two shots.
Vision-language models are pre-trained by aligning image-text pairs in a common space to deal with open-set visual concepts. To boost the transferability of the pre-trained models, recent works adopt fixed or learnable prompts, i.e., classification weights are synthesized from natural language describing task-relevant categories, to reduce the gap between tasks in the training and test phases. However, how and what prompts can improve inference performance remains unclear. In this paper, we explicitly clarify the importance of including semantic information in prompts, while existing prompting methods generate prompts without exploring the semantic information of textual labels. Manually constructing prompts with rich semantics requires domain expertise and is extremely time-consuming. To cope with this issue, we propose a semantic-aware prompt learning method, namely CPKP, which retrieves an ontological knowledge graph by treating the textual label as a query to extract task-relevant semantic information. CPKP further introduces a double-tier confounder-pruning procedure to refine the derived semantic information. The graph-tier confounders are gradually identified and phased out, inspired by the principle of Granger causality. The feature-tier confounders are demolished by following the maximum entropy principle in information theory. Empirically, the evaluations demonstrate the effectiveness of CPKP, e.g., with two shots, CPKP outperforms the manual-prompt method by 4.64% and the learnable-prompt method by 1.09% on average, and the superiority of CPKP in domain generalization compared to benchmark approaches. Our implementation is available at https://github.com/Mowenyii/CPKP.