TAP: Parameter-efficient Task-Aware Prompting for Adverse Weather Removal
Hanting Wang, Shengpeng Ji, Shulei Wang, Hai Huang, Xiao Jin, Qifei Zhang, Tao Jin

TL;DR
This paper introduces a parameter-efficient, task-aware prompt tuning framework for multi-task image restoration under adverse weather, achieving high performance with significantly fewer parameters.
Contribution
It proposes a novel two-stage training paradigm with enhanced, low-rank decomposed prompts that model inter-task relatedness, improving efficiency and accuracy in weather-related image restoration.
Findings
Achieves superior restoration performance with only 2.75M parameters.
Effectively models inter-task relatedness through contrastive constraints.
Enhances task-specific prompts with low-rank decomposition for better generalization.
Abstract
Image restoration under adverse weather conditions has been extensively explored, leading to numerous high-performance methods. In particular, recent advances in All-in-One approaches have shown impressive results by training on multi-task image restoration datasets. However, most of these methods rely on dedicated network modules or parameters for each specific degradation type, resulting in a significant parameter overhead. Moreover, the relatedness across different restoration tasks is often overlooked. In light of these issues, we propose a parameter-efficient All-in-One image restoration framework that leverages task-aware enhanced prompts to tackle various adverse weather degradations.Specifically, we adopt a two-stage training paradigm consisting of a pretraining phase and a prompt-tuning phase to mitigate parameter conflicts across tasks. We first employ supervised learning to…
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Taxonomy
TopicsImage Enhancement Techniques · Advanced Image Processing Techniques · Image and Signal Denoising Methods
