52 citations · 92 across the 5 of their papers we have counts for
5 papers
SoftPatch+: Fully Unsupervised Anomaly Classification and Segmentation
Chengjie Wang, Xi Jiang, Bin-Bin Gao +4
Although mainstream unsupervised anomaly detection (AD) (including image-level classification and pixel-level segmentation)algorithms perform well in academic datasets, their perfo…
PVG: Progressive Vision Graph for Vision Recognition
Jiafu Wu, Jian Li, Jiangning Zhang +4
Convolution-based and Transformer-based vision backbone networks process images into the grid or sequence structures, respectively, which are inflexible for capturing irregular obj…
Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation
Yue Han, Jiangning Zhang, Yabiao Wang +5
Few-Shot Instance Segmentation (FSIS) requires detecting and segmenting novel classes with limited support examples. Existing methods based on Region Proposal Networks (RPNs) face…
PatchMix Augmentation to Identify Causal Features in Few-shot Learning
Chengming Xu, Chen Liu, Xinwei Sun +4
The task of Few-shot learning (FSL) aims to transfer the knowledge learned from base categories with sufficient labelled data to novel categories with scarce known information. It…
APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation
Jiacheng Chen, Bin-Bin Gao, Zongqing Lu +3
Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only a few labeled support images. Most advanced solutions exploit a metric learning…