In this episode we discuss Foundation Model Drives Weakly Incremental Learning for Semantic Segmentation
by Chaohui Yu, Qiang Zhou, Jingliang Li, Jianlong Yuan, Zhibin Wang, Fan Wang. The paper proposes a novel and data-efficient framework for weakly incremental learning for semantic segmentation (WILSS) called FMWISS. WILSS aims to learn to segment new classes from cheap and readily available image-level labels. The proposed framework uses pre-training based co-segmentation to generate dense pseudo labels and a teacher-student architecture to optimize noisy pseudo masks with a dense contrastive loss. Additionally, memory-based copy-paste augmentation is introduced to address the catastrophic forgetting problem of old classes. The framework achieves superior performance on Pascal VOC and COCO datasets compared to state-of-the-art methods.
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