MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning

• URL: http://arxiv.org/abs/2312.08636v1
• Date: Thu, 14 Dec 2023 03:33:02 GMT
• Title: MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning
• Authors: Yi Xin, Junlong Du, Qiang Wang, Ke Yan, Shouhong Ding
• Abstract summary: Multi-task learning is designed to train multiple correlated tasks simultaneously. To tackle this challenge, we integrate the decoder-free vision-language model CLIP. We propose Multi-modal Alignment Prompt (MmAP) for CLIP, which aligns text and visual modalities during fine-tuning process.
• Score: 29.88567810099265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-Task Learning (MTL) is designed to train multiple correlated tasks simultaneously, thereby enhancing the performance of individual tasks. Typically, a multi-task network structure consists of a shared backbone and task-specific decoders. However, the complexity of the decoders increases with the number of tasks. To tackle this challenge, we integrate the decoder-free vision-language model CLIP, which exhibits robust zero-shot generalization capability. Recently, parameter-efficient transfer learning methods have been extensively explored with CLIP for adapting to downstream tasks, where prompt tuning showcases strong potential. Nevertheless, these methods solely fine-tune a single modality (text or visual), disrupting the modality structure of CLIP. In this paper, we first propose Multi-modal Alignment Prompt (MmAP) for CLIP, which aligns text and visual modalities during fine-tuning process. Building upon MmAP, we develop an innovative multi-task prompt learning framework. On the one hand, to maximize the complementarity of tasks with high similarity, we utilize a gradient-driven task grouping method that partitions tasks into several disjoint groups and assign a group-shared MmAP to each group. On the other hand, to preserve the unique characteristics of each task, we assign an task-specific MmAP to each task. Comprehensive experiments on two large multi-task learning datasets demonstrate that our method achieves significant performance improvements compared to full fine-tuning while only utilizing approximately 0.09% of trainable parameters.

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