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Superpixel segmentation has seen significant progress benefiting from thedeep convolutional networks. The typical approach entails initial division ofthe image into grids, followed by a learning process that assigns each pixel toadjacent grid segments. However, reliance on convolutions with confinedreceptive fields results in an implicit, rather than explicit, understanding ofpixel-grid interactions. This limitation often leads to a deficit of contextualinformation during the mapping of associations. To counteract this, weintroduce the Association Implantation (AI) module, designed to allow networksto explicitly engage with pixel-grid relationships. This module embeds gridfeatures directly into the vicinity of the central pixel and employsconvolutional operations on an enlarged window, facilitating an adaptivetransfer of knowledge. This approach enables the network to explicitly extractcontext at the pixel-grid level, which is more aligned with the objectives ofsuperpixel segmentation than mere pixel-wise interactions. By integrating theAI module across various layers, we enable a progressive refinement ofpixel-superpixel relationships from coarse to fine. To further enhance theassignment of boundary pixels, we've engineered a boundary-aware loss function.This function aids in the discrimination of boundary-adjacent pixels at thefeature level, thereby empowering subsequent modules to precisely identifyboundary pixels and enhance overall boundary accuracy. Our method has beenrigorously tested on four benchmarks, including BSDS500, NYUv2, ACDC, andISIC2017, and our model can achieve competitive performance with comparisonmethods.

AINet+: Advancing Superpixel Segmentation via Cascaded Association Implantation