2 citations · 5 across the 4 of their papers we have counts for
4 papers
Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning
Yixiong Zou, Yicong Liu, Yiman Hu +2
Cross-domain few-shot learning (CDFSL) aims to acquire knowledge from limited training data in the target domain by leveraging prior knowledge transferred from source domains with…
Masked Graph Autoencoder with Non-discrete Bandwidths
Ziwen Zhao, Yuhua Li, Yixiong Zou +2
Masked graph autoencoders have emerged as a powerful graph self-supervised learning method that has yet to be fully explored. In this paper, we unveil that the existing discrete ed…
ECEA: Extensible Co-Existing Attention for Few-Shot Object Detection
Zhimeng Xin, Tianxu Wu, Shiming Chen +3
Few-shot object detection (FSOD) identifies objects from extremely few annotated samples. Most existing FSOD methods, recently, apply the two-stage learning paradigm, which transfe…
CSGCL: Community-Strength-Enhanced Graph Contrastive Learning
Han Chen, Ziwen Zhao, Yuhua Li +3
Graph Contrastive Learning (GCL) is an effective way to learn generalized graph representations in a self-supervised manner, and has grown rapidly in recent years. However, the und…