Structural Pruning via Spatial-aware Information Redundancy for Semantic Segmentation

Dongyue Wu, Zilin Guo, Li Yu, Nong Sang, Changxin Gao

Abstract: In recent years, semantic segmentation has flourished in various applications. However, the high computational cost remains a significant challenge that hinders its further adoption. The filter pruning method for structured network slimming offers a direct and effective solution for the reduction of segmentation networks. Nevertheless, we argue that the majority of existing pruning methods overlook the fact that segmentation is a location-sensitive task, which consequently leads to the sub-optimal performance of existing pruning methods originally designed for image classification when applied to segmentation networks. To address this issue, this paper proposes a novel approach, denoted as Spatial-aware Information Redundancy Filter Pruning (SIRFP), which aims to reduce feature redundancy between channels. First, we formulate the pruning problem as a maximum edge weight clique problem (MEWCP) in graph theory, thereby minimizing the feature redundancy among the remaining features after pruning. Within this framework, we introduce a spatial-aware redundancy metric based on feature maps into the consideration of segmentation network pruning, thus endowing the pruning process with location sensitivity to better adapt to segmentation tasks. Additionally, based on the MEWCP, we propose a low computational complexity greedy strategy to solve this NP-hard problem, making it feasible and efficient for structured pruning. To validate the effectiveness of our method, we conducted extensive comparative experiments on various challenging datasets. The results demonstrate the superior performance of SIRFP for semantic segmentation tasks.