Hierarchical Prompt Learning for Multi-Task Learning

Yajing Liu, Yuning Lu, Hao Liu, Yaozu An, Zhuoran Xu, Zhuokun Yao, Baofeng Zhang, Zhiwei Xiong, Chenguang Gui; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 10888-10898

Vision-language models (VLMs) can effectively transfer to various vision tasks via prompt learning. Real-world scenarios often require adapting a model to multiple similar yet distinct tasks. Existing methods focus on learning a specific prompt for each task, limiting the ability to exploit potentially shared information from other tasks. Naively training a task-shared prompt using a combination of all tasks ignores fine-grained task correlations. Significant discrepancies across tasks could cause negative transferring. Considering this, we present Hierarchical Prompt (HiPro) learning, a simple and effective method for jointly adapting a pre-trained VLM to multiple downstream tasks. Our method quantifies inter-task affinity and subsequently constructs a hierarchical task tree. Task-shared prompts learned by internal nodes explore the information within the corresponding task group, while task-individual prompts learned by leaf nodes obtain fine-grained information targeted at each task. The combination of hierarchical prompts provides high-quality content of different granularity. We evaluate HiPro on four multi-task learning datasets. The results demonstrate the effectiveness of our method.