75 citations · 109 across the 9 of their papers we have counts for
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cs.CV2023★ 1 cited
Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model
Zelin Peng, Zhengqin Xu, Zhilin Zeng +3
Parameter-efficient fine-tuning (PEFT) is an effective methodology to unleash the potential of large foundation models in novel scenarios with limited training data. In the compute…
cs.CV2023★ 3 cited
SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstruction
Zelin Peng, Zhengqin Xu, Zhilin Zeng +2
Segment Anything Model (SAM) has received remarkable attention as it offers a powerful and versatile solution for object segmentation in images. However, fine-tuning SAM for downst…
cs.CV2023★ 2 cited
USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentation
Zelin Peng, Guanchun Wang, Lingxi Xie +3
Seed area generation is usually the starting point of weakly supervised semantic segmentation (WSSS). Computing the Class Activation Map (CAM) from a multi-label classification net…