DevoSeg: Budget-Aware Self-Evolving Semantic Segmentation under Visual Degradation

Semantic segmentation under visual degradation is challenging because class cues and object boundaries deteriorate differently across images. Effective inference therefore calls for decisions that adapt to the current image and improve through experience from earlier cases. We introduce DevoSeg, a self-evolving framework that learns how to deploy a frozen segmentor from completed segmentations. It (1) estimates the complementary, Direct-relative opportunity of policy sets and assigns an image-dependent execution budget; (2) compares masks from selected policies with an independently produced Direct mask using post-execution image–mask evidence; and (3) organizes observed effects and costs as contextual knowledge, with a disjoint-group validation rule for proposed decision-state updates. Separating policy acquisition from mask selection lets past outcomes guide which alternatives to explore while the final decision uses the predictions actually obtained. On 200 ACDC images, DevoSeg’s mask selector improves pooled Cityscapes-19 mIoU by 2.084 percentage points over Direct. Image-adaptive acquisition executes 30.1% fewer candidate policies and reduces their collection time by 23.3% relative to fixed eight-policy acquisition.