Task-Aware Low-Rank Adaptation for Multi-Task Learning in Computer Vision

แนวคิดหลัก

Proposing TA-LoRA method for multi-task learning in computer vision using SAM.

บทคัดย่อ

The Segment Anything Model (SAM) is enhanced with the Task-Aware Low-Rank Adaptation (TA-LoRA) method to facilitate multi-task learning. The study explores the transfer of semantic information to various downstream tasks, introducing modified SAM (mSAM) for multi-task learning. TA-LoRA injects update parameter tensors into each layer of the encoder in SAM, incorporating task-shared and task-specific information efficiently. Extensive experiments validate the efficacy of TA-LoRA in enhancing mSAM performance across multiple tasks.

สถิติ

SAM trained on a dataset of 11 million samples.
TA-LoRA method exhibits sublinear growth in learnable parameters with an increase in tasks.

คำพูด

"The proposed TA-LoRA method enhances model performance by effectively learning both task-shared and task-specific information simultaneously."
"Extensive experiments substantiate the efficacy of TA-LoRA in enhancing the performance of mSAM across multiple downstream tasks."

ข้อมูลเชิงลึกที่สำคัญจาก

Task-Aware Low-Rank Adaptation of Segment Anything Model

by Xuehao Wang,... ที่ arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10971.pdf

สอบถามเพิ่มเติม

How does the low-rank assumption on update parameter tensors benefit multi-task learning

The low-rank assumption on update parameter tensors benefits multi-task learning by allowing the model to efficiently capture both task-specific and task-shared information. By decomposing the update parameter tensor into low-rank factors, such as core tensors and factor matrices, the model can effectively learn representations that are relevant to each individual task while also leveraging shared knowledge across tasks. This approach helps in reducing overfitting, improving generalization capabilities, and enhancing overall performance on multiple tasks simultaneously.

What are the implications of freezing heavyweight encoders and fine-tuning update parameter tensors

Freezing heavyweight encoders while fine-tuning update parameter tensors has several implications for multi-task learning. Firstly, it allows the model to retain valuable pre-trained information from the heavyweight encoder, which is crucial for maintaining high-quality representations across tasks. By focusing on updating only specific parameters related to each task through the update parameter tensors, the model can adapt more efficiently without losing important features learned during pre-training. Additionally, this approach helps in controlling computational costs and preventing catastrophic forgetting by selectively adjusting parameters based on task requirements.

How can the findings from this study be applied to other large foundation models beyond SAM

The findings from this study can be applied to other large foundation models beyond SAM by serving as a blueprint for adapting these models for multi-task learning scenarios. Models like BERT in natural language processing or CLIP in vision-language understanding could benefit from similar low-rank adaptation techniques when faced with multiple downstream tasks. By incorporating Task-Aware Low-Rank Adaptation (TA-LoRA) methods or similar strategies into these models, researchers can enhance their ability to handle diverse tasks efficiently while leveraging shared information effectively. This approach could lead to improved performance and generalization across various domains where large foundation models are utilized for complex tasks requiring multi-modal understanding.