CAT-SAM: Conditional Tuning for Few-Shot Adaptation of Segment Anything Model
- 10 November 2024
- book chapter
- Published by Springer Nature
- p. 189-206
Abstract
No abstract availableKeywords
All Related Versions
This publication has 39 references indexed in Scilit:
- CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning2024
- Correction to: Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world ApplicationsMachine Intelligence Research, 2024
- Self-prompting Large Vision Models for Few-Shot Medical Image Segmentation2023
- CLIP-Adapter: Better Vision-Language Models with Feature AdaptersInternational Journal of Computer Vision, 2023
- Highly Accurate Dichotomous Image Segmentation2022
- Visual Prompt Tuning2022
- Learning to Prompt for Vision-Language ModelsInternational Journal of Computer Vision, 2022
- A coarse-to-fine boundary refinement network for building footprint extraction from remote sensing imageryISPRS Journal of Photogrammetry and Remote Sensing, 2022
- Bidirectional Feature Pyramid Network with Recurrent Attention Residual Modules for Shadow Detection2018
- Large-Scale Training of Shadow Detectors with Noisily-Annotated Shadow Examples2016