33 citations · 41 across the 7 of their papers we have counts for
6 papers · 1 filter
Masked Cross-image Encoding for Few-shot Segmentation
Wenbo Xu, Huaxi Huang, Ming Cheng +3
Few-shot segmentation (FSS) is a dense prediction task that aims to infer the pixel-wise labels of unseen classes using only a limited number of annotated images. The key challenge…
Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration
Guofeng Mei, Hao Tang, Xiaoshui Huang +5
Deep point cloud registration methods face challenges to partial overlaps and rely on labeled data. To address these issues, we propose UDPReg, an unsupervised deep probabilistic r…
Position Embedding Needs an Independent Layer Normalization
Runyi Yu, Zhennan Wang, Yinhuai Wang +5
The Position Embedding (PE) is critical for Vision Transformers (VTs) due to the permutation-invariance of self-attention operation. By analyzing the input and output of each encod…
Structured Discriminative Tensor Dictionary Learning for Unsupervised Domain Adaptation
Songsong Wu, Yan Yan, Hao Tang +3
Unsupervised Domain Adaptation (UDA) addresses the problem of performance degradation due to domain shift between training and testing sets, which is common in computer vision appl…
Compare More Nuanced:Pairwise Alignment Bilinear Network For Few-shot Fine-grained Learning
Huaxi Huang, Junjie Zhang, Jian Zhang +2
The recognition ability of human beings is developed in a progressive way. Usually, children learn to discriminate various objects from coarse to fine-grained with limited supervis…
Fast Registration for cross-source point clouds by using weak regional affinity and pixel-wise refinement
Xiaoshui Huang, Lixin Fan, Qiang Wu +2
Many types of 3D acquisition sensors have emerged in recent years and point cloud has been widely used in many areas. Accurate and fast registration of cross-source 3D point clouds…