Work

U-SAM: Upgrade Segment Anything Model With Semantic-Aware and Memory-Efficient

Year: 2025

Type: conference-paper

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp49660.2025.10889270

Authors: Xiaofeng Jin, Jie Hu, Jianghang Lin, Shengchuan Zhang, Liujuan Cao

Institutions: Xiamen University, National University of Singapore

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: closed

Funders: National Natural Science Foundation of China, Natural Science Foundation of Fujian Province, National Science and Technology Major Project, National Science Fund for Distinguished Young Scholars

OpenAlex ID: W4408352759

FWCI:

Cites: 33

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Related to: 10