AINet+: Advancing Superpixel Segmentation via Cascaded Association Implantation

TL;DR

This paper introduces AINet+ with the Association Implantation module, explicitly modeling pixel-grid relationships and progressively refining superpixel segmentation, leading to improved boundary accuracy and competitive results across multiple benchmarks.

Contribution

We propose the Association Implantation module for explicit pixel-grid interaction and a boundary-aware loss to enhance superpixel boundary accuracy.

Findings

Achieves competitive performance on BSDS500, NYUv2, ACDC, and ISIC2017.

Enables explicit context extraction at pixel-grid level.

Improves boundary delineation in superpixel segmentation.

Abstract

Superpixel segmentation has seen significant progress benefiting from the deep convolutional networks. The typical approach entails initial division of the image into grids, followed by a learning process that assigns each pixel to adjacent grid segments. However, reliance on convolutions with confined receptive fields results in an implicit, rather than explicit, understanding of pixel-grid interactions. This limitation often leads to a deficit of contextual information during the mapping of associations. To counteract this, we introduce the Association Implantation (AI) module, designed to allow networks to explicitly engage with pixel-grid relationships. This module embeds grid features directly into the vicinity of the central pixel and employs convolutional operations on an enlarged window, facilitating an adaptive transfer of knowledge. This approach enables the network to explicitly extract context at the pixel-grid level, which is more aligned with the objectives of superpixel segmentation than mere pixel-wise interactions. By integrating the AI module across various layers, we enable a progressive refinement of pixel-superpixel relationships from coarse to fine. To further enhance the assignment of boundary pixels, we've engineered a boundary-aware loss function. This function aids in the discrimination of boundary-adjacent pixels at the feature level, thereby empowering subsequent modules to precisely identify boundary pixels and enhance overall boundary accuracy. Our method has been rigorously tested on four benchmarks, including BSDS500, NYUv2, ACDC, and ISIC2017, and our model can achieve competitive performance with comparison methods.