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Parameter-efficient Fine-tuning in Hyperspherical Space for Open-vocabulary Semantic Segmentation
Since the PEFT strategy is conducted symmetrically to the two CLIP modalities, the misalignment between them is mitigated.
Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model
Parameter-efficient fine-tuning (PEFT) is an effective methodology to unleash the potential of large foundation models in novel scenarios with limited training data.
SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstruction
Most existing fine-tuning methods attempt to bridge the gaps among different scenarios by introducing a set of new parameters to modify SAM's original parameter space.
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