1 citations · 1 across the 3 of their papers we have counts for
7 papers
ViRefSAM: Visual Reference-Guided Segment Anything Model for Remote Sensing Segmentation
Hanbo Bi, Yulong Xu, Ya Li +8
The Segment Anything Model (SAM), with its prompt-driven paradigm, exhibits strong generalization in generic segmentation tasks. However, applying SAM to remote sensing (RS) images…
A Complex-valued SAR Foundation Model Based on Physically Inspired Representation Learning
Mengyu Wang, Hanbo Bi, Yingchao Feng +7
Vision foundation models in remote sensing have been extensively studied due to their superior generalization on various downstream tasks. Synthetic Aperture Radar (SAR) offers all…
RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation
Hanbo Bi, Yingchao Feng, Boyuan Tong +11
The rapid advancement of foundation models has revolutionized visual representation learning in a self-supervised manner. However, their application in remote sensing (RS) remains…
RS-vHeat: Heat Conduction Guided Efficient Remote Sensing Foundation Model
Huiyang Hu, Peijin Wang, Hanbo Bi +10
Remote sensing foundation models largely break away from the traditional paradigm of designing task-specific models, offering greater scalability across multiple tasks. However, th…
AgMTR: Agent Mining Transformer for Few-shot Segmentation in Remote Sensing
Hanbo Bi, Yingchao Feng, Yongqiang Mao +4
Few-shot Segmentation (FSS) aims to segment the interested objects in the query image with just a handful of labeled samples (i.e., support images). Previous schemes would leverage…
Prompt-and-Transfer: Dynamic Class-aware Enhancement for Few-shot Segmentation
Hanbo Bi, Yingchao Feng, Wenhui Diao +5
For more efficient generalization to unseen domains (classes), most Few-shot Segmentation (FSS) would directly exploit pre-trained encoders and only fine-tune the decoder, especial…