CRLNet: Cascaded Resolution Learning Network for Natural Scenes Segmentation

The natural environment presents a multitude of scenes with diverse content, posing challenges for satisfactory segmentation results using existing segmentation networks. In response, we propose a Cascaded Resolution Learning Network (CRLNet) to enhance segmentation performance through global textual embedding and multi-resolution feature learning. The CRLNet constructs a multi-path segmentation system that integrates multi-resolution feature data from different paths, thereby progressively enhancing local feature learning. Two key modules, the Partition-Fusion Channel Attention Module (PFCAM) and the Features Learning Module (FLM), are pivotal components of CRLNet. The PFCAM serves as a computationally efficient channel attention module to mitigate segmentation confusion stemming from similar objects. Meanwhile, the FLM is tailored to learn resolution feature maps from different paths, facilitating the refinement of object representation and enhancing segmentation performance. Extensive experiments conducted on real natural scene datasets demonstrate the superiority of the proposed CRLNet over existing efficient segmentation methods in terms of accuracy.