Prompt-based Task-conditioning Transformer for Low-quality Detection

Low-quality detection aims to enhance detector performance when handling multi-degraded inputs, such as fog and low-light conditions. Relying on numerous images and annotations, significant progress has been made in addressing each type of degradation. However, these methods have high-quality requirements for data size and model design, ignoring the transferability and generalization of features across different degradation conditions. Inspired by prompt learning in reconstituting the relationship between foundation models and downstream tasks, we propose a prompt-based task-conditioning transformer framework for low-quality detection (LQPrompt). In particular, we present a module for updating prompts (PUM). By combining both high- and low-quality features, PUM adaptively retains tailored visual representations as task-relevant prompts using an attention-like mask. To facilitate effective interaction between prompts and the foundation model, we design a prompt aggregation strategy. These designs enable the detector to learn the correlations and differences between features under different degradation conditions, enhancing generalization and improving detection performance. Experimental results demonstrate that LQPrompt achieves SOTA performance with minimal parameter cost across various low-quality scenarios without corresponding annotations. Moreover, LQPrompt is also effective on real-world datasets such as RTTS and ExDark.