Incremental Few-Shot Semantic Segmentation Via Multi-Level Switchable Visual Prompts
Maoxian Wan, Kaige Li, Qichuan Geng, Weimin Shi, Zhong Zhou
2025年份
1被引次数
摘要
trophic forgetting and reducing increasing computation.
Our method achieves new state-of-the-art performance, outperforming previous SoTA methods by 30.28% mIoU-N on VOC and 13.90% mIoU-N on COCO under 1-shot. Code is available at https://github.com/WanMotion/MSVP.
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它引用的顶会 Paper17
- An Image is Worth 16x16 Words: Transformers for Image Recognition at ScaleAlexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn 等ICLR 2021 · 被引用 21,477 次
- BEiT: BERT Pre-Training of Image TransformersHangbo Bao, Li Dong, Songhao Piao, Furu WeiICLR 2022 · 被引用 3,632 次
- Learning to Prompt for Continual LearningZifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang 等CVPR 2022 · 被引用 635 次
- GroupViT: Semantic Segmentation Emerges from Text SupervisionJiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon 等CVPR 2022 · 被引用 398 次
- Forward Compatible Few-Shot Class-Incremental LearningDa-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma 等CVPR 2022 · 被引用 259 次
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