Unlocking the Potential of Pre-Trained Vision Transformers for Few-Shot Semantic Segmentation through Relationship Descriptors
No official code found. Best implementation: ZiqinZhou66/FewSegwithRD (5 stars, MIT, last commit 2024-03-26).
No official code found. Best implementation: ZiqinZhou66/FewSegwithRD (5 stars, MIT, last commit 2024-03-26).
Training Large Language Models for Retrieval-Augmented Question Answering through Backtracking Correction
Unleashing the Full Potential of Product Quantization for Large-Scale Image Retrieval
Improved Expressivity of Hypergraph Neural Networks through High-Dimensional Generalized Weisfeiler-Leman Algorithms
CFPT: Empowering Time Series Forecasting through Cross-Frequency Interaction and Periodic-Aware Timestamp Modeling
JoAPR: Cleaning the Lens of Prompt Learning for Vision-Language Models
Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning
@article{zhou2026unlocking,
title = {Unlocking the Potential of Pre-Trained Vision Transformers for Few-Shot Semantic Segmentation through Relationship Descriptors},
author = {Ziqin Zhou and Hai-Ming Xu and Yangyang Shu and Lingqiao Liu},
year = {2026},
doi = {10.1109/CVPR52733.2024.00366},
url = {https://doi.org/10.1109/CVPR52733.2024.00366},
journal = {CVPR 2024 2024}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/3ad2ba8eca619fe2a45f41271f3624b2f5a3e8f5)