# A Self-Training Framework Based on Multi-Scale Attention Fusion for Weakly Supervised Semantic Segmentation Source: [A Self-Training Framework Based on Multi-Scale Attention Fusion for Weakly Supervised Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:16:36.929Z Authors: Guoqing Yang, Chuang Zhu, Yu Zhang Published: 2023-05-10T02:16:12.000Z ## Abstract Weakly supervised semantic segmentation (WSSS) based on image-level labels is challenging since it is hard to obtain complete semantic regions. To address this issue, we propose a self-training method that utilizes fused multi-scale class-aware attention maps. Our observation is that attention maps of different scales contain rich complementary information, especially for large and small objects. Therefore, we collect information from attention maps of different scales and obtain multi-scale attention maps. We then apply denoising and reactivation strategies to enhance the potential regions and reduce noisy areas. Finally, we use the refined attention maps to retrain the network. Experiments showthat our method enables the model to extract rich semantic information from multi-scale images and achieves 72.4% mIou scores on both the PASCAL VOC 2012 validation and test sets. The code is available at https://bupt-ai-cz.github.io/SMAF. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()