<p>Smoke semantic segmentation (SSS) is particularly challenging task due to the various patterns of the target itself, which are caused by the characteristics of smoke, like, non-rigid, translucent, fuzzy, environment-sensitive, and so forth. This paper tailor-makes the Scale-Cross Non-Local Network (SCNN) for Smoke Segmentation, aiming to accurately locate the position of smoke in complex scenes. While non-local enjoys the bonus of the excellent competence in modeling long-range contextual dependencies acquired by self-attention, the constraint on single-scale input and the suitability for low-resolution feature erode its capability in information representation. To address these issues, we bespoke a Scale-Cross Non-Local (SCNL) module to better integrate local features with global dependencies. In practical scenes, diverse non-smoke objects sharing similarity with smoke pose great obstacles to accurate location of smoke. As a solution, we design a Pyramid Irregular Convolution (PIC) module containing rich high-level semantic to further refine the feature representation of segmentation task. By supervising classification task, the high-level semantics obtained can guide the segmentation feature to correct semantic errors at the image level and alleviate the issue of between-class similarity. To assess its generalization ability, we empirically evaluate our SCNN on extensive synthetic and real data. Experimental results demonstrate that SCNN achieves state-of-the-art performance, exhibiting enhanced smoke localization, accuracy in boundary detection, and a significant reduction in the false segmentation rate for smoke-like objects.</p>