AINet+: Advancing Superpixel Segmentation via Cascaded Association Implantation

2021-01-26

AI Summary

To address the implicit pixel-grid relationship modeling, insufficient context awareness, and low boundary accuracy in superpixel segmentation, this paper proposes a Cascaded Association Implantation (AI) module. It explicitly models structured pixel-to-grid associations for the first time, employing a multi-level cascaded architecture to progressively refine relationships from coarse to fine granularity. Adaptive large-kernel convolutions are introduced to enhance local-global contextual fusion, while a boundary-aware loss function strengthens discriminative learning for boundary pixels. Evaluated on four major benchmarks—BSDS500, NYUv2, ACDC, and ISIC2017—the method achieves state-of-the-art or highly competitive segmentation performance, notably improving boundary accuracy (e.g., +2.3% F-measure on BSDS500). These results validate both the effectiveness and generalizability of explicit association modeling 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.

Problem

• Superpixel Segmentation
• Information Exchange
• Boundary Identification

Innovation

• Association Implantation Module
• Boundary-Aware Loss Function
• Hyperpixel Segmentation