Enhanced Foreground–Background Discrimination for Weakly Supervised Semantic Segmentation

Abstract

Weakly supervised semantic segmentation (WSSS) methods are extensively studied due to the availability of image-level annotations. Relying on class activation maps (CAMs) derived from original classification networks often suffers from issues such as inaccurate object localization, incomplete object regions, and the inclusion of confusing background pixels. To address these issues, we propose a two-stage method that enhances the foreground–background discriminative ability in a global context (FB-DGC). Specifically, a cross-domain feature calibration module (CFCM) is first proposed to calibrate foreground and background salient features using global spatial location information, thereby expanding foreground features while mitigating the impact of inaccurate localization in class activation regions. A class-specific distance module (CSDM) is further adopted to facilitate the separation of foreground–background features, thereby enhancing the activation of target regions, which alleviates the over-smoothing of features produced by the network and mitigates issues associated with confused features. In addition, an adaptive edge feature extraction (AEFE) strategy is proposed to identify target features in candidate boundary regions and capture missed features, compensating for drawbacks in recognising the co-occurrence of multiple targets. The proposed method is extensively evaluated on the challenging PASCAL VOC 2012 and MS COCO 2014 datasets, demonstrating its feasibility and superiority.