Prompt Guided Transformer for Multi-Task Dense Prediction

• URL: http://arxiv.org/abs/2307.15362v1
• Date: Fri, 28 Jul 2023 07:25:57 GMT
• Title: Prompt Guided Transformer for Multi-Task Dense Prediction
• Authors: Yuxiang Lu, Shalayiding Sirejiding, Yue Ding, Chunlin Wang and Hongtao Lu
• Abstract summary: We introduce a lightweight task-conditional model called Prompt Guided Transformer to optimize performance and model parameters. Our approach achieves state-of-the-art results among task-conditional methods while using fewer parameters, and maintains a significant balance between performance and parameter size.
• Score: 14.815576352301322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Task-conditional architecture offers advantage in parameter efficiency but falls short in performance compared to state-of-the-art multi-decoder methods. How to trade off performance and model parameters is an important and difficult problem. In this paper, we introduce a simple and lightweight task-conditional model called Prompt Guided Transformer (PGT) to optimize this challenge. Our approach designs a Prompt-conditioned Transformer block, which incorporates task-specific prompts in the self-attention mechanism to achieve global dependency modeling and parameter-efficient feature adaptation across multiple tasks. This block is integrated into both the shared encoder and decoder, enhancing the capture of intra- and inter-task features. Moreover, we design a lightweight decoder to further reduce parameter usage, which accounts for only 2.7% of the total model parameters. Extensive experiments on two multi-task dense prediction benchmarks, PASCAL-Context and NYUD-v2, demonstrate that our approach achieves state-of-the-art results among task-conditional methods while using fewer parameters, and maintains a significant balance between performance and parameter size.

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