BLO-SAM: Bi-Level Optimization Based Finetuning for Semantic Segmentation
Core Concepts
BLO-SAM introduces a bi-level optimization approach to finetune the Segment Anything Model (SAM) for semantic segmentation tasks, addressing overfitting and improving generalization.
Abstract
BLO-SAM aims to enhance SAM's performance by optimizing model parameters and prompt embedding separately on different subsets of training data. It outperforms other SAM-based methods in diverse tasks with limited labeled data, showcasing its effectiveness in combating overfitting and eliminating the need for manual prompts.
BLO-SAM
Stats
SAM struggles with segmenting specific objects autonomously.
Current solutions often lead to overfitting in scenarios with limited data.
BLO-SAM significantly reduces the risk of overfitting by training model parameters and prompt embedding on separate subsets of the training dataset.
BLO-SAM demonstrates superior performance over state-of-the-art image segmentation methods.
BLO-SAM has minimal trainable parameters among all trainable methods, highlighting its parameter efficiency.
Quotes
"Our method enables fully automated segmentation without the need for manual prompts during inference and training."
"BLO-SAM significantly reduces the risk of overfitting by training the model’s weight parameters and the prompt embedding on two separate subsets of the training dataset."