Intermediary-Focused Multi-Task Learning: Managing Heterogeneous Sources for Semantic Change Detection

Although Multi-Task Learning (MTL) is widely adopted in computer vision for learning shared representations, it struggles with inconsistencies from heterogeneous sources, especially in dense prediction tasks like Semantic Change Detection (SCD). To address this, we propose a novel intermediary-focused MTL framework, exemplified by the Prototypes-Focused SCD Network (ProF-SCD). The ProF-SCD features three key components: a Prototypes-Based Domain Alignment (PDA) module that aligns features across domains using label-supervised prototypes, effectively reducing temporal gaps and preserving true semantic changes; a Double Predictions Semantic Segmentation head (DP-SS); and a Dual Guidance Binary Change Detection head (DG-BCD). The latter two heads utilize auxiliary guidance from the PDA module to enhance their respective tasks. Extensive experiments on real-world datasets show that ProF-SCD achieves significant performance gains, improving mIoU by up to 0.33% and SeK precision by 0.42% over state-of-the-art methods, while using less than onethird of the parameters and half of the FLOPs. These results validate ProF-SCD’s effectiveness, demonstrating the robustness of our intermediary-focused approach and advancing the state of SCD. Our source code is available at https://github.com/sdustmmlab/ProF-SCD .