2 papers
cs.CV2025
SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation
Jun Yin, Fei Wu, Yupeng Ren +5
Public remote sensing datasets often face limitations in universality due to resolution variability and inconsistent land cover category definitions. To harness the vast pool of un…
cs.CV2024
Lite-SAM Is Actually What You Need for Segment Everything
Jianhai Fu, Yuanjie Yu, Ningchuan Li +5
This paper introduces Lite-SAM, an efficient end-to-end solution for the SegEvery task designed to reduce computational costs and redundancy. Lite-SAM is composed of four main comp…