HazeMT-DCR: Demand-Aware Counterfactual Expert Routing for Incremental Multi-Task Prediction in Hazy Scenes

Existing multi-task perception methods for hazy scenes usually adopt a two-stage “dehazing-then-prediction" paradigm. However, dehazing objectives for visual restoration or pixel reconstruction may not match downstream task requirements. A unified shared representation may also fail to capture the different semantic, structural, and geometric cues required by different tasks. To address these issues, we propose HazeMT-DCR, a demand-aware counterfactual expert routing framework for multi-task prediction in hazy scenes. We reformulate hazy multi-task perception as shared functional reuse and dynamic expert composition conditioned on task demands. Without explicit clear-image restoration, HazeMT-DCR calibrates multi-scale visual features using latent degradation states and conditions shared functional experts on task demands, degradation states, and image content. Each task learns an independent reinforcement learning routing policy to dynamically combine the shared experts. To improve routing reliability, we introduce a task-demand intervention counterfactual regret mechanism. Expert removal identifies redundant or negatively contributing experts, while task-demand replacement checks whether expert responses depend on the correct task demand. The resulting counterfactual signals are used to improve expert routing. HazeMT-DCR also decouples shared modules from task-specific parameters, allowing new tasks to be adapted by training only task-specific routing and output modules without retraining the full model. We evaluate HazeMT-DCR on four dense prediction tasks on hazy PASCAL-Context. Experiments show that HazeMT-DCR outperforms existing parameter-efficient multi-task adaptation methods while requiring only a small number of trainable parameters, and supports efficient task expansion.