16 citations · 17 across the 3 of their papers we have counts for
3 papers
cs.CV2024★ 1 cited
Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning
Huali Xu, Li Liu, Shuaifeng Zhi +4
Existing Cross-Domain Few-Shot Learning (CDFSL) methods require access to source domain data to train a model in the pre-training phase. However, due to increasing concerns about d…
cs.CV2023
Enhancing Representations through Heterogeneous Self-Supervised Learning
Zhong-Yu Li, Bo-Wen Yin, Yongxiang Liu +2
Incorporating heterogeneous representations from different architectures has facilitated various vision tasks, e.g., some hybrid networks combine transformers and convolutions. How…
cs.CV2023★ 16 cited
DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation
Bowen Yin, Xuying Zhang, Zhongyu Li +3
We present DFormer, a novel RGB-D pretraining framework to learn transferable representations for RGB-D segmentation tasks. DFormer has two new key innovations: 1) Unlike previous…