While deep learning architectures have driven substantial improvements in salient object detection (SOD), effectively handling objects of unpredictable scales and ambiguous categories remains a complex challenge. These issues are fundamentally tied to how networks process multi-level and multi-scale feature representations. To address this, a novel framework is presented that utilizes aggregate interaction modules to fuse spatial features from neighboring network tiers. By employing minimal up-sampling and down-sampling rates, this mechanism significantly minimizes the introduction of noise. Furthermore, self-interaction modules are embedded within each decoder unit to generate highly refine... More >