Dual-Path Consistency Learning via Hierarchical Neighborhood Perturbation for Scribble-Supervised Semantic Segmentation

Scribble-supervised semantic segmentation is a weakly supervised paradigm based on sparse annotations, which has attracted increasing attention due to its low labeling cost, high annotation flexibility, and promising performance. Existing methods primarily employ perturbation techniques such as noise injection, feature mixing, or channel dropout to foster diverse feature representations through consistency learning. However, they often overlook perturbation of hierarchical semantic-aware, which are vital for ensuring robust consistency learning. To address these issues, we propose a dual-path mutual guidance consistency learning framework with hierarchical neighborhood perturbation, termed DCNP. First, the hierarchical neighborhood random perturbation (HNRP) module implements pixel-level semantic-aware feature perturbation through locally-constrained neighborhood sampling around each target position. This hierarchical design aligns with the multi-scale structure of the encoder, enhancing feature stability under semantic-varying perturbation. In conjunction with HNRP, the dual-path mutual guidance consistency learning (DPMG) module incorporates a consistency loss that enforces bidirectional prediction consistency between the perturbed features branch and the original features branch through cross-branch pseudo-label supervision, effectively alleviating the problem of insufficient supervision caused by sparse annotations. Experimental results show that the proposed method achieves outstanding performance on five scribble-annotated datasets, offering meaningful insights into the design of efficient and architecturally simple frameworks for sparse annotation. The source code is available at https://github.com/chenller/ScribbleDCNP