The Segment Anything Model (SAM) stands as a foundational framework for image segmentation. While it exhibits remarkable zero-shot generalization in typical scenarios, its advantage diminishes when applied to specialized domains like medical imagery and remote sensing. To address this limitation, this paper introduces Conv-LoRA, a simple yet effective parameter-efficient fine-tuning approach. By integrating ultra-lightweight convolutional parameters into Low-Rank Adaptation (LoRA), Conv-LoRA can inject image-related inductive biases into the plain ViT encoder, further reinforcing SAM's local prior assumption. Notably, Conv-LoRA not only preserves SAM's extensive segmentation knowledge but also revives its capacity of learning high-level image semantics, which is constrained by SAM's foreground-background segmentation pretraining. Comprehensive experimentation across diverse benchmarks spanning multiple domains underscores Conv-LoRA's superiority in adapting SAM to real-world semantic segmentation tasks.

本文介绍了Conv-LoRA，这是一种简单而有效的参数高效微调方法，它将超轻量级的卷积参数集成到Low-Rank Adaptation (LoRA) 中，进一步加强了Segment Anything Model (SAM) 的局部先验假设，并在实验中展现了在真实世界语义分割任务中适应SAM的优势。

卷积与LoRA相遇：一种参数高效微调的分割模型