让去雾模型根据下游任务需求实时调整，无需重训练。
Adaptive Dynamic Dehazing via Instruction-Driven and Task-Feedback Closed-Loop Optimization for Diverse Downstream Task Adaptation
- 通过任务反馈和文本指令双机制动态调节去雾效果。
- 在多个视觉任务上验证，性能显著优于传统方法。
- 适合需要灵活适配不同下游任务的实时应用。
真实视觉系统中，去雾不仅需提升图像清晰度，还需满足多样下游任务的需求。为此，我们提出一种新型自适应动态去雾框架，采用闭环优化机制，在推理阶段通过下游任务表现反馈与用户指令引导，实现无需重训练即可满足多任务需求的动态调整。技术上，框架集成两种互补机制：(1) 基于多任务性能的动态调制反馈环；(2) 支持用户指定高层任务偏好的文本指令接口。该双重引导策略使模型在训练后可实时适应多任务演变需求。大量实验在多种视觉任务上验证了方法的有效性、鲁棒性和泛化能力，确立了一种与下游应用主动协同的交互式、任务自适应去雾新范式。
原文摘要 · Abstract (English)
In real-world vision systems,haze removal is required not only to enhance image visibility but also to meet the specific needs of diverse downstream tasks.To address this challenge,we propose a novel adaptive dynamic dehazing framework that incorporates a closed-loop optimization mechanism.It enables feedback-driven refinement based on downstream task performance and user instruction-guided adjustment during inference,allowing the model to satisfy the specific requirements of multiple downstream tasks without retraining.Technically,our framework integrates two complementary and innovative mechanisms: (1)a task feedback loop that dynamically modulates dehazing outputs based on performance across multiple downstream tasks,and (2) a text instruction interface that allows users to specify high-level task preferences.This dual-guidance strategy enables the model to adapt its dehazing behavior after training,tailoring outputs in real time to the evolving needs of multiple tasks.Extensive experiments across various vision tasks demonstrate the strong effectiveness,robustness,and generalizability of our approach.These results establish a new paradigm for interactive,task-adaptive dehazing that actively collaborates with downstream applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。